Google Professional Data Engineer practice questions

1. Question 1 of 25

   You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?

   • AExport the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
   • BExport the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
   • CExport the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.
   • DExport the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

   Show answer

   Correct answer: B

   How others answered

   • B50%
   • A46%

   Only 50% agreed on this answer — treat it as contested rather than settled.

   Explanation

   You are transferring sensitive patient information, so C & D are ruled out. Choice comes down to A & B. Here it gets tricky. How to choose Transfer Appliance: (https://cloud.google.com/transfer-appliance/docs/2.0/overview) Without knowing the bandwidth, it is not possible to determine whether the upload can be completed within 7 days, as recommended by Google. So the safest and most performant way is to use Transfer Appliance. Therefore my choice is B.

   Adapted from community discussion by Ganshank · 2020-04-20 · 63 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • Google Cloud documentation
   • Google Cloud documentation
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   Candidate context

   Question 1 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

   1. G

      Ganshank

      2020-04-20

      63 upvotes

      You are transferring sensitive patient information, so C & D are ruled out. Choice comes down to A & B. Here it gets tricky. How to choose Transfer Appliance: (https://cloud.google.com/transfer-appliance/docs/2.0/overview) Without knowing the bandwidth, it is not possible to determine whether the upload can be completed within 7 days, as recommended by Google. So the safest and most performant way is to use Transfer Appliance. Therefore my choice is B.

   2. T

      TNT87

      2020-09-29

      19 upvotes

      Answer is B,gsutil has a limit of 1TBaccording to Google documentation,if data is morethan 1TBthen we have to use Transfer Appliance.

   3. Y

      Yiouk

      2021-08-04

      9 upvotes

      The answer is clearly seen here: https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets#transfer-options

   4. S

      SSV

      2020-07-07

      8 upvotes

      Answer should be B: A is also correct but it has its own limit. It allows only 5TB data upload at a time to cloud storage. https://cloud.google.com/storage/quotas I will go with B

2. Question 2 of 25

   You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

   • ALeverage BigQuery UPDATE statements to update the inventory balances as they are changing.
   • BPartition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
   • CUse the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
   • DUse the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

   Show answer

   Correct answer: C

   How others answered

   • C52%
   • A48%

   Only 52% agreed on this answer — treat it as contested rather than settled.

   Explanation

   I'm afraid that even the DML statements per day are unlimited nowadays (2022), the answer [C] remains the best option for the near real-time constraint, since updating row-by-row in BigQuery is considered by Google an anti-pattern. Quoting from https://cloud.google.com/bigquery/docs/best-practices-performance-patterns#dml_statements_that_update_or_insert_single_rows : Best practice: Avoid point-specific DML statements (updating or inserting 1 row at a time). Batch your updates and inserts. ... BigQuery DML statements are intended for bulk updates. UPDATE and DELETE DML statements in BigQuery are oriented towards periodic rewrites of your data, not single row mutations. ... If your use case involves frequent single row inserts, consider streaming your data instead. Through this prism: [C] (streaming) > [D] (batch) > A [row-by-row]

   Adapted from community discussion by NicolasN · 2022-11-05 · 9 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • Google Cloud documentation
   • Google Cloud documentation
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   Question 2 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

   1. H

      haroldbenites

      2020-08-21

      25 upvotes

      C is correct. It says “update Every hour” And need “ accuracy”

   2. N

      NicolasN

      2022-11-05

      Selected C9 upvotes

      I'm afraid that even the DML statements per day are unlimited nowadays (2022), the answer [C] remains the best option for the near real-time constraint, since updating row-by-row in BigQuery is considered by Google an anti-pattern. Quoting from https://cloud.google.com/bigquery/docs/best-practices-performance-patterns#dml_statements_that_update_or_insert_single_rows : Best practice: Avoid point-specific DML statements (updating or inserting 1 row at a time). Batch your updates and inserts. ... BigQuery DML statements are intended for bulk updates. UPDATE and DELETE DML statements in BigQuery are oriented towards periodic rewrites of your data, not single row mutations. ... If your use case involves frequent single row inserts, consider streaming your data instead. Through this prism: [C] (streaming) > [D] (batch) > A [row-by-row]

   3. J

      jalk

      2020-06-25

      12 upvotes

      Since BQ doesn't allow more than 1500 updates pr table pr day, A and B are out. Bulk loading (D) is also out since batching the inserts goes against the real time requirement. But C with the more complex querying is at odds with the maximize dashboard performance

   4. A

      alecuba16

      2022-08-18

      Selected C5 upvotes

      2022 Cannot be A there are limits: Table operations per day 1500 operations Your project can make up to 1,500 table operations per table per day, whether the operation appends data to the table or truncates the table. This limit includes the combined total of all load jobs, copy jobs, and query jobs that append to or overwrite a destination table or that use a DML DELETE, INSERT, MERGE, TRUNCATE TABLE, or UPDATE statement to write data to a table. DML statements count toward partition limits, but aren't limited by them. For more information about DML limits, see Data manipulation language statements. 1.000 updates per hour * 24 = 24000 updates > 1500 daily limit. https://cloud.google.com/bigquery/quotas

3. Question 3 of 25

   You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table that have a recovery point objective (RPO) of 30 days?

   • ASet the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
   • BSet the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
   • CSet the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
   • DSet the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

   Show answer

   Correct answer: C

   How others answered

   • C41%
   • D22%
   • A22%

   Only 41% agreed on this answer — treat it as contested rather than settled.

   Explanation

   Answer is C: https://cloud.google.com/bigquery/docs/table-snapshots-intro "Benefits of using table snapshots include the following: Keep a record for longer than seven days. With BigQuery time travel, you can only access a table's data from seven days ago or more recently. With table snapshots, you can preserve a table's data from a specified point in time for as long as you want. Minimize storage cost. BigQuery only stores bytes that are different between a snapshot and its base table, so a table snapshot typically uses less storage than a full copy of the table." But the wording is foolish... It's table snapshot, NOT point in time snapshot! https://cloud.google.com/bigquery/docs/time-travel#restore-a-table this is point in time using time travel window - max is 7 days...

   Adapted from community discussion by desertlotus1211 · 2023-01-19 · 5 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • Google Cloud documentation
   • Google Cloud documentation
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   Question 3 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

   1. D

      DeepakVenkatachalam

      2023-09-15

      10 upvotes

      Answer is B. Timetravel only covers for 7 days and a scheduled query is needed for creating Table snapshots for 30 days. Also table snapshot must remain in the same region as base table (please refer to limitation of table snapshot from below link) https://cloud.google.com/bigquery/docs/table-snapshots-intro

   2. D

      desertlotus1211

      2023-01-19

      5 upvotes

      Answer is C: https://cloud.google.com/bigquery/docs/table-snapshots-intro "Benefits of using table snapshots include the following: Keep a record for longer than seven days. With BigQuery time travel, you can only access a table's data from seven days ago or more recently. With table snapshots, you can preserve a table's data from a specified point in time for as long as you want. Minimize storage cost. BigQuery only stores bytes that are different between a snapshot and its base table, so a table snapshot typically uses less storage than a full copy of the table." But the wording is foolish... It's table snapshot, NOT point in time snapshot! https://cloud.google.com/bigquery/docs/time-travel#restore-a-table this is point in time using time travel window - max is 7 days...

   3. Z

      zellck

      2022-12-05

      Selected C4 upvotes

      C is the answer. https://cloud.google.com/bigquery/docs/table-snapshots-intro#table_snapshots A BigQuery table snapshot preserves the contents of a table (called the base table) at a particular time. You can save a snapshot of a current table, or create a snapshot of a table as it was at any time in the past seven days. A table snapshot can have an expiration; when the configured amount of time has passed since the table snapshot was created, BigQuery deletes the table snapshot. You can query a table snapshot as you would a standard table. Table snapshots are read-only, but you can create (restore) a standard table from a table snapshot, and then you can modify the restored table.

   4. M

      max_c

      2022-09-19

      Selected C4 upvotes

      C for high availability and use of built-in feature Point-in-time snapshots only have data from the past 7 days at their creation. They can be stored for as long as desired. https://cloud.google.com/bigquery/docs/table-snapshots-intro#table_snapshots

4. Question 4 of 25

   You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors. You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

   • ADeploy small Kafka clusters in your data centers to buffer events.
   • BHave the data acquisition devices publish data to Cloud Pub/Sub.
   • CEstablish a Cloud Interconnect between all remote data centers and Google.
   • DWrite a Cloud Dataflow pipeline that aggregates all data in session windows.

   Show answer

   Correct answer: B

   How others answered

   • B70%

   Only 70% agreed on this answer — treat it as contested rather than settled.

   Explanation

   C. This is a tricky one. The issue here is the unreliable connection between data collection and data processing infrastructure, and to resolve it in a cost-effective manner. However, it also mentions that the company is using leased lines. I think replacing the leased lines with Cloud InterConnect would solve the problem, and hopefully not be an added expense. https://cloud.google.com/interconnect/docs/concepts/overview

   Adapted from community discussion by Ganshank · 2020-04-13 · 22 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • Google Cloud documentation
   • Google Cloud documentation
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   Question 4 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

   1. G

      Ganshank

      2020-04-13

      22 upvotes

      C. This is a tricky one. The issue here is the unreliable connection between data collection and data processing infrastructure, and to resolve it in a cost-effective manner. However, it also mentions that the company is using leased lines. I think replacing the leased lines with Cloud InterConnect would solve the problem, and hopefully not be an added expense. https://cloud.google.com/interconnect/docs/concepts/overview

   2. F

      fire558787

      2021-08-17

      17 upvotes

      Disagree. OK, the problem is between the data centers and Google's data centers. However, Cloud Interconnect costs money. If the devices write to PubSub instead, they would bypass their own data centers and write directly to Google. It seems that connectivity is not a problem for the collecting devices, since it says that data is collected in local data centers. So my guess goes towards PubSub rather than Interconnect.

   3. A

      ayush_1995

      2023-01-29

      Selected B10 upvotes

      B. Have the data acquisition devices publish data to Cloud Pub/Sub. This would provide a reliable messaging service for your event data, allowing you to ingest and process your data in a timely manner, regardless of the reliability of the leased lines. Cloud Pub/Sub also offers automatic retries and fault-tolerance, which would further improve the reliability of your event delivery. Additionally, using Cloud Pub/Sub would allow you to easily scale up or down your event processing infrastructure as needed, which would help to minimize costs.

   4. M

      MrCastro

      2021-07-30

      6 upvotes

      "leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable." The leased lines are used only when i'm sending the event to the datacenters. If i use cloud pub/sub, it will not be used or will be more reliable as Google datacenters are highly available. I also think having a cloud pub sub topic will be WAAAY cheaper than interconnecting some datacenters around the world so it's very cost effective. IMO the answer is B

5. Question 5 of 25

   You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

   • ASpeech-to-Text API
   • BCloud Natural Language API
   • CDialogflow Enterprise Edition
   • DAutoML Natural Language

   Show answer

   Correct answer: C

   How others answered

   • C81%
   • A19%

   Explanation

   Option A - Cloud Speech-to-Text API. The question is just asking to " interpret customer voice commands" .. it does not mention anything related to sentiment analysis so NLP is not required. DialogFlow is more of a chat bot services typically suited for a "Service Desk" kind of setup - where clients will call a centralized helpdesk and automation is achieved through Chat bot services like - google Dialog flow

   Adapted from community discussion by Alasmindas · 2020-11-10 · 19 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • Google Cloud documentation
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   Question 5 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

   1. R

      rickywck

      2020-03-17

      26 upvotes

      should be C, since we need to recognize both voice and intent

   2. A

      Alasmindas

      2020-11-10

      19 upvotes

      Option A - Cloud Speech-to-Text API. The question is just asking to " interpret customer voice commands" .. it does not mention anything related to sentiment analysis so NLP is not required. DialogFlow is more of a chat bot services typically suited for a "Service Desk" kind of setup - where clients will call a centralized helpdesk and automation is achieved through Chat bot services like - google Dialog flow

   3. M

      MaxNRG

      2022-01-04

      Selected C6 upvotes

      C: Dialogflow Enterprise Edition is an end-to-end development suite for building conversational interfaces for websites, mobile applications, popular messaging platforms, and IoT devices. You can use it to build interfaces (e.g., chatbots) that are capable of natural and rich interactions between your users and your business. It is powered by machine learning to recognize the intent and context of what a user says, allowing your conversational interface to provide highly efficient and accurate responses. https://cloud.google.com/dialogflow/ Dialogflow API V2 is the new iteration of our developer API. The new API integrates Google Cloud Speech-to-Text, enabling developers to send audio directly to Dialogflow for combined speech recognition and natural language understanding. https://dialogflow.com/v2-faq https://cloud.google.com/blog/products/gcp/introducing-dialogflow-enterprise-edition-a-new-way-to-build-voice-and-text-conversational-apps

   4. H

      hdmi_switch

      2021-07-27

      8 upvotes

      Cloud Speech-to-Text API just converts speech to text. You will have text files as an output and then the requirement is to "interpret customer voice commands and issue an order to the backend systems". This is not achieved by having text files. I would go with option C, since Dialogflow can interpret the commands (intents) and integrates other applications e.g. backend systems.

6. Question 6 of 25

   You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

   • AUse Cloud Vision AutoML with the existing dataset.
   • BUse Cloud Vision AutoML, but reduce your dataset twice.
   • CUse Cloud Vision API by providing custom labels as recognition hints.
   • DTrain your own image recognition model leveraging transfer learning techniques.

   Show answer

   Correct answer: A

   How others answered

   • A57%
   • B31%

   Only 57% agreed on this answer — treat it as contested rather than settled.

   Explanation

   First I think in Vision API, but that is a pre-trained AI, will not recognize my labels, so because you have 1000 samples per item, AUTO ML is perfect. B cannot be because have not sensed to reduce your dataset if you have the recommended number of info. https://cloud.google.com/vision/automl/docs/beginners-guide#include_enough_labeled_examples_in_each_category The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label.

   Adapted from community discussion by odacir · 2023-06-08 · 8 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   • Google Cloud documentation
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   Question 6 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

   1. O

      odacir

      2023-06-08

      Selected A8 upvotes

      First I think in Vision API, but that is a pre-trained AI, will not recognize my labels, so because you have 1000 samples per item, AUTO ML is perfect. B cannot be because have not sensed to reduce your dataset if you have the recommended number of info. https://cloud.google.com/vision/automl/docs/beginners-guide#include_enough_labeled_examples_in_each_category The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label.

   2. C

      Cloud_Enthusiast

      2021-05-09

      8 upvotes

      A: https://cloud.google.com/vision/automl/docs/beginners-guide "The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label."

   3. T

      techtitan

      2023-08-08

      8 upvotes

      A - https://cloud.google.com/vertex-ai/docs/beginner/beginners-guide Target at least 1000 examples per target

   4. D

      dragon123

      2021-02-24

      7 upvotes

      The bare minimum required by AutoML Vision training is 100 image examples per category/label. So it should be B

7. Question 7 of 25

   You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

   • AOrganize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
   • BOrganize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
   • COrganize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
   • DOrganize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

   Show answer

   Correct answer: D

   How others answered

   • D51%
   • B49%

   Only 51% agreed on this answer — treat it as contested rather than settled.

   Explanation

   From BigQuery documentation - Benefits of using table snapshots include the following: - Keep a record for longer than seven days. With BigQuery time travel, you can only access a table's data from seven days ago or more recently. With table snapshots, you can preserve a table's data from a specified point in time for as long as you want. - Minimize storage cost. BigQuery only stores bytes that are different between a snapshot and its base table, so a table snapshot typically uses less storage than a full copy of the table. So storing data in GCS will make copies of data for each table. Table snapshots are more optimal in this scenario.

   Adapted from community discussion by Lanro · 2023-07-31 · 8 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • Google Cloud documentation
   • Google Cloud documentation
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   Question 7 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

   1. G

      Ganshank

      2020-04-13

      12 upvotes

      B The questions is specifically about organizing the data in BigQuery and storing backups.

   2. L

      Lanro

      2023-07-31

      Selected D8 upvotes

      From BigQuery documentation - Benefits of using table snapshots include the following: - Keep a record for longer than seven days. With BigQuery time travel, you can only access a table's data from seven days ago or more recently. With table snapshots, you can preserve a table's data from a specified point in time for as long as you want. - Minimize storage cost. BigQuery only stores bytes that are different between a snapshot and its base table, so a table snapshot typically uses less storage than a full copy of the table. So storing data in GCS will make copies of data for each table. Table snapshots are more optimal in this scenario.

   3. P

      Poojaji

      2020-06-06

      10 upvotes

      Decorators can only correct the data unto 7 days. Hence Option B

   4. A

      ABM9

      2020-12-16

      8 upvotes

      Should be B Using snapshot decorators , recovery is valid only for a period of 7 days. Here it says 2 weeks so "D" is ruled out. You can undelete a table within seven days of deletion, including explicit deletions and implicit deletions due to table expiration. After seven days, it is not possible to undelete a table using any method, including opening a support ticket. https://cloud.google.com/bigquery/docs/managing-tables

8. Question 8 of 25

   You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling. Which Google database service should you use?

   • ACloud SQL
   • BBigQuery
   • CCloud Bigtable
   • DCloud Datastore

   Show answer

   Correct answer: D

   How others answered

   • D57%
   • A33%

   Only 57% agreed on this answer — treat it as contested rather than settled.

   Explanation

   Initially, thinking D is the best answer but when question is re-re-read, A seems to be correct answer for following reasons 1. Is payment TRANSACTION -- DB should able to perform full blown transaction (updating inventory, sales info etc, though not specified) , not just ATOMIC which DataStore provides 2. Its point-of-sale application, not ONLINE STORE where HIGH number of concurrent users ordering stuff. 3. User Base could grow exponentially - again more users does mot mean concurrent users and more processing power. Its only about storage. 4. Do not want to Manage infrastructure scaling. - Cloud SQL can scale in terms of storage. 5. CloudStore is poor selection for OLTP application - Each property is index - so higher latency Not sure, during exam 2 min is enough to think on various point.. I may be wrong or wrong path ... lets brainstrom..

   Adapted from community discussion by DeepakKhattar · 2020-06-03 · 78 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 8 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

   1. D

      DeepakKhattar

      2020-06-03

      78 upvotes

      Initially, thinking D is the best answer but when question is re-re-read, A seems to be correct answer for following reasons 1. Is payment TRANSACTION -- DB should able to perform full blown transaction (updating inventory, sales info etc, though not specified) , not just ATOMIC which DataStore provides 2. Its point-of-sale application, not ONLINE STORE where HIGH number of concurrent users ordering stuff. 3. User Base could grow exponentially - again more users does mot mean concurrent users and more processing power. Its only about storage. 4. Do not want to Manage infrastructure scaling. - Cloud SQL can scale in terms of storage. 5. CloudStore is poor selection for OLTP application - Each property is index - so higher latency Not sure, during exam 2 min is enough to think on various point.. I may be wrong or wrong path ... lets brainstrom..

   2. J

      jvg637

      2020-03-11

      38 upvotes

      D seems to be the right one. Cloud SQL doesn't automatically scale

   3. R

      Radhika7983

      2024-09-24

      13 upvotes

      D seems to be the answer. This is what I think based on my analysis below. POS is OLTP system but now a days NOSQL with ACID properties also are used for OLTP, Cloud sql is good for relational database and it would have been an option here but it clearly says that "you do not want to manage infrastructure scaling". In cloud SQL, which is managed service and not server less, you need to manually do vertical scaling(scale up and scale down). Hence I believe CLOUD SQL is not the option here. I also tried creating a datastore using google cloud console and it gives 2 options now that is cloud firestore in native mode and cloud firestore in data store mode. automatic scaling is available in both where there is no manual scaling up or down is required. Also, both firestore in native and datastore provides ACID properties. Also, firestore is now optimized for OLTP. Please see below https://cloud.google.com/solutions/building-scalable-apps-with-cloud-firestore Though the question only talks about datastore, I am just providing additional information. Considering all what I read through, D is the answer.

   4. H

      hendrixlives

      2024-09-24

      Selected D4 upvotes

      D is correct: Datastore (currently Firestore in native or datastore mode). It is a fully managed and serverless solution that allows for transactions and will autoscale (storage and compute) without the need to manage any infrastructure. A is wrong: Cloud SQL is fully a managed transactional DB, but only the storage grows automatically. As your user base increases, you will need to increase the CPU/memory of the instance, and to do that you must edit the instance manually (and the questions specifically says "you do not want to manage infrastructure scaling") B is wrong: Bigquery is OLAP (for analytics). NoOps, fully managed, autoscales and allows transactions, but it is not designed for this use case. C is wrong: Bigtable is a NoSQL database for massive writes, and to scale (storage and CPU) you must add nodes, so it is completely out of this use case.

9. Question 9 of 25

   The marketing team at your organization provides regular updates of a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

   • AReduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
   • BIncrease the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
   • CSplit the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
   • DImport the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.

   Show answer

   Correct answer: D

   How others answered

   • D100%

   Explanation

   D: BigQuery is primarly designed and suit to append-only technology with some limited DML statements. It's not a relational database where you constantly update your user records if they edit their profile. Instead you need to arhitect your code so each edit is a new row in Bigquery, and you always query the latest row. The DML statement limitation is low, because it targets different scenarios and not yours, aka live update on rows. You could ingest your data into a separate table, and issue 1 update statement per day. https://stackoverflow.com/questions/45183082/can-we-increase-update-quota-in-bigquery https://cloud.google.com/blog/products/gcp/performing-large-scale-mutations-in-bigquery

   Adapted from community discussion by MaxNRG · 2023-01-09 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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   Candidate context

   Question 9 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

   1. R

      rickywck

      2021-03-17

      30 upvotes

      Should be D. https://cloud.google.com/blog/products/gcp/performing-large-scale-mutations-in-bigquery

   2. [

      [Removed]

      2021-03-22

      16 upvotes

      Should be D https://cloud.google.com/bigquery/docs/reference/standard-sql/dml-syntax#merge_statement https://cloud.google.com/blog/products/gcp/performing-large-scale-mutations-in-bigquery

   3. D

      dambilwa

      2021-06-25

      7 upvotes

      Option [D] is the most appropriate answer. However DMLs now longer have any limits in BigQuery - https://cloud.google.com/blog/products/data-analytics/dml-without-limits-now-in-bigquery

   4. [

      [Removed]

      2021-03-28

      7 upvotes

      Answer: D Description: Google says to merge data in this cases by creating two tables

10. Question 10 of 25

    Your United States-based company has created an application for assessing and responding to user actions. The primary table's data volume grows by 250,000 records per second. Many third parties use your application's APIs to build the functionality into their own frontend applications. Your application's APIs should comply with the following requirements: ✑ Single global endpoint ✑ ANSI SQL support ✑ Consistent access to the most up-to-date data What should you do?

    • AImplement BigQuery with no region selected for storage or processing.
    • BImplement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.
    • CImplement Cloud SQL for PostgreSQL with the master in North America and read replicas in Asia and Europe.
    • DImplement Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

    Show answer

    Correct answer: B

    How others answered

    • B89%

    Explanation

    A - BigQuery with NO Region ? (Looks wrong) B - Spanner (SQL support and Scalable and have replicas ) - Looks correct C - SQL (can't store so many records) (wrong) D - Bigtable - NO SQL (wrong) Vote for B

    Adapted from community discussion by sumanshu · 2023-01-03 · 24 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 10 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. S

       sumanshu

       2023-01-03

       24 upvotes

       A - BigQuery with NO Region ? (Looks wrong) B - Spanner (SQL support and Scalable and have replicas ) - Looks correct C - SQL (can't store so many records) (wrong) D - Bigtable - NO SQL (wrong) Vote for B

    2. M

       MaxNRG

       2023-07-09

       Selected B6 upvotes

       B: Cloud Spanner is the first scalable, enterprise-grade, globally-distributed, and strongly consistent database service built for the cloud specifically to combine the benefits of relational database structure with non-relational horizontal scale. https://cloud.google.com/spanner/ Cloud Spanner is a fully managed, mission-critical, relational database service that offers transactional consistency at global scale, schemas, SQL (ANSI 2011 with extensions), and automatic, synchronous replication for high availability. https://cloud.google.com/spanner/docs/ https://cloud.google.com/spanner/docs/instances#available-configurations-multi-region

    3. T

       Tanmoyk

       2022-03-17

       8 upvotes

       B is correct, Bigquery cannot support 250K data ingestion/second , as ANSI SQL support is required , no other options left except Spanner.

    4. A

       Archy

       2022-02-01

       6 upvotes

       B, as Cloud Spanner has three types of replicas: read-write replicas, read-only replicas, and witness replicas.

11. Question 11 of 25

    Select 2

    You are running a pipeline in Dataflow that receives messages from a Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

    • AIncrease the number of max workers
    • BUse a larger instance type for your Dataflow workers
    • CChange the zone of your Dataflow pipeline to run in us-central1
    • DCreate a temporary table in Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Bigtable to BigQuery
    • ECreate a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

    Show answer

    Correct answer: A, B

    How others answered

    • AB100%

    Explanation

    A & B instance n1-standard-1 is low configuration and hence need to be larger configuration, definitely B should be one of the option. Increase max workers will increase parallelism and hence will be able to process faster given larger CPU size and multi core processor instance type is chosen. Option A can be a better step.

    Adapted from community discussion by jvg637 · 2020-09-18 · 50 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 11 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. J

       jvg637

       2020-09-18

       50 upvotes

       A & B instance n1-standard-1 is low configuration and hence need to be larger configuration, definitely B should be one of the option. Increase max workers will increase parallelism and hence will be able to process faster given larger CPU size and multi core processor instance type is chosen. Option A can be a better step.

    2. S

       sumanshu

       2022-01-04

       14 upvotes

       A & B. With autoscaling enabled, the Dataflow service does not allow user control of the exact number of worker instances allocated to your job. You might still cap the number of workers by specifying the --max_num_workers option when you run your pipeline. Here as per question CAP is 3, So we can change that CAP. For batch jobs, the default machine type is n1-standard-1. For streaming jobs, the default machine type for Streaming Engine-enabled jobs is n1-standard-2 and the default machine type for non-Streaming Engine jobs is n1-standard-4. When using the default machine types, the Dataflow service can therefore allocate up to 4000 cores per job. If you need more cores for your job, you can select a larger machine type.

    3. B

       barnac1es

       2024-03-24

       Selected A, B3 upvotes

       A. Increase the number of max workers: By increasing the number of maximum workers, you allow Dataflow to allocate more computing resources to handle the peak load of incoming data. This can help improve processing speed and reduce CPU utilization per worker. B. Use a larger instance type for your Dataflow workers: Using a larger instance type with more CPU and memory resources can help your Dataflow workers handle a higher volume of data and processing tasks more efficiently. It can address CPU bottlenecks during peak periods.

    4. A

       AJKumar

       2020-12-22

       4 upvotes

       D and E can be eliminated right away as the question does not talk abt spanner or bigtable, C is also eliminated as changing time zones is irrelevant. Answer A&B.

12. Question 12 of 25

    Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

    • AEnable data access logs in each Data Analyst's project. Restrict access to Stackdriver Logging via Cloud IAM roles.
    • BExport the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts' projects. Restrict access to the Cloud Storage bucket.
    • CExport the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.
    • DExport the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.

    Show answer

    Correct answer: D

    How others answered

    • D100%

    Explanation

    Answer D is correct. Aggregated log sink will create a single sink for all projects, the destination can be a google cloud storage, pub/sub topic, bigquery table or a cloud logging bucket. without aggregated sink this will be required to be done for each project individually which will be cumbersome. https://cloud.google.com/logging/docs/export/aggregated_sinks

    Adapted from community discussion by SteelWarrior · 2021-03-24 · 30 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 12 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. S

       SteelWarrior

       2021-03-24

       30 upvotes

       Answer D is correct. Aggregated log sink will create a single sink for all projects, the destination can be a google cloud storage, pub/sub topic, bigquery table or a cloud logging bucket. without aggregated sink this will be required to be done for each project individually which will be cumbersome. https://cloud.google.com/logging/docs/export/aggregated_sinks

    2. [

       [Removed]

       2020-09-25

       12 upvotes

       Correct: D https://cloud.google.com/iam/docs/roles-audit-logging#scenario_external_auditors

    3. S

       sumanshu

       2022-01-04

       6 upvotes

       A - eliminated , because logs needs to be retained for 6 months (So, some storage require) B - eliminated, because if we store in same project then, Data Analyst can also access (But in question it's mention, ONLY audit personnel needs access) C - Wrong (No need to restrict project as well as logs separately) - wording does not look okay. D - Correct (If we restrict the project, then all resources get restricted) Vote for D

    4. C

       Chelseajcole

       2022-03-31

       4 upvotes

       The auditor needs to audit data analyst's behaviors (how they access multiple projects in BQ ). So, the key is, multiple projects. According to Google doc project-level sinks: https://cloud.google.com/logging/docs/export/configure_export_v2 However, the Cloud Console can only create or view sinks in Cloud projects. To create sinks in organizations, folders, or billing accounts using the gcloud command-line tool or Cloud Logging API, see Aggregated sinks. Obviously, the auditor needs to check all projects accessed by data analyst which is not project-level, a higher level like folder or organization level, this can only be done via the aggregate sink. So D is the answer.

13. Question 13 of 25

    You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?

    • AAssign the users/groups data viewer access at the table level for each table
    • BCreate SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views
    • CCreate authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views
    • DCreate authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside

    Show answer

    Correct answer: A

    How others answered

    • A66%
    • D28%

    Only 66% agreed on this answer — treat it as contested rather than settled.

    Explanation

    Correct A: A . Now it is feasible to provide table level access to user by allowing user to query single table and no other table will be visible to user in same dataset.

    Adapted from community discussion by someshsehgal · 2021-02-09 · 41 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 13 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. S

       someshsehgal

       2021-02-09

       41 upvotes

       Correct A: A . Now it is feasible to provide table level access to user by allowing user to query single table and no other table will be visible to user in same dataset.

    2. H

       hdmi_switch

       2021-07-28

       14 upvotes

       Back then D, now A. Question is, if the exam is already up to date.

    3. J

       jits1984

       2021-10-28

       11 upvotes

       Should still be D. Question states - "They should only see certain tables based on their team membership" Option A states - Assign the users/groups data viewer access at the table level for each table With A, everyone will see every table. Hence D.

    4. R

       rickywck

       2020-03-20

       9 upvotes

       Yes, should be D: https://cloud.google.com/bigquery/docs/share-access-views

14. Question 14 of 25

    You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

    • ARe-write the application to load accumulated data every 2 minutes.
    • BConvert the streaming insert code to batch load for individual messages.
    • CLoad the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
    • DEstimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

    Show answer

    Correct answer: D

    How others answered

    • D68%
    • B23%

    Only 68% agreed on this answer — treat it as contested rather than settled.

    Explanation

    B. Streams data into BigQuery one record at a time without needing to run a load job: https://cloud.google.com/bigquery/docs/reference/rest/v2/tabledata/insertAll Instead of using a job to load data into BigQuery, you can choose to stream your data into BigQuery one record at a time by using the tabledata.insertAll method. This approach enables querying data without the delay of running a load job: https://cloud.google.com/bigquery/streaming-data-into-bigquery The BigQuery Storage Write API is a unified data-ingestion API for BigQuery. It combines the functionality of streaming ingestion and batch loading into a single high-performance API. You can use the Storage Write API to stream records into BigQuery that become available for query as they are written, or to batch process an arbitrarily large number of records and commit them in a single atomic operation. Committed mode. Records are available for reading immediately as you write them to the stream. Use this mode for streaming workloads that need minimal read latency. https://cloud.google.com/bigquery/docs/write-api

    Adapted from community discussion by MaxNRG · 2021-11-08 · 7 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 14 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. M

       MaxNRG

       2021-11-08

       7 upvotes

       B. Streams data into BigQuery one record at a time without needing to run a load job: https://cloud.google.com/bigquery/docs/reference/rest/v2/tabledata/insertAll Instead of using a job to load data into BigQuery, you can choose to stream your data into BigQuery one record at a time by using the tabledata.insertAll method. This approach enables querying data without the delay of running a load job: https://cloud.google.com/bigquery/streaming-data-into-bigquery The BigQuery Storage Write API is a unified data-ingestion API for BigQuery. It combines the functionality of streaming ingestion and batch loading into a single high-performance API. You can use the Storage Write API to stream records into BigQuery that become available for query as they are written, or to batch process an arbitrarily large number of records and commit them in a single atomic operation. Committed mode. Records are available for reading immediately as you write them to the stream. Use this mode for streaming workloads that need minimal read latency. https://cloud.google.com/bigquery/docs/write-api

    2. N

       noob_master

       2024-09-24

       Selected D8 upvotes

       Answer: D. The only that describe a way to resolve the problem, with buffering the data. (the question is possible old, the best approach would be Pub/Sub + Dataflow Streaming + Bigquery for streaming data instead near-real time)

    3. M

       musumusu

       2024-09-24

       5 upvotes

       Answer: D What to learn or look for 1. In-Flight data = (Real Time data, i.e still in streaming pipeline and not landed in BigQuery) 2. Dataflow (assume in best case) streaming pipeline is running to send data to Bigquery. Why not option B: change streaming to batch upload is not business requirement, we have to stuck to streaming and real time analysis. Option D: make bigquery run after waiting for sometime (twice here), How will you do it? - there is not setting in BQ to do it, right!. So, adjust it in your pipeline (dataflow) - For example, add Fixed window, and you want to execute aggregation query after 2 min. Code ```pipeline.apply(...) .apply(Window. into(FixedWindows.of(Duration.standardMinutes(2)))) .apply(BigQueryIO.writeTableRows() .to("my_dataset.my_table") ```

    4. [

       [Removed]

       2022-08-11

       Selected D6 upvotes

       if you convert the individual messages to batch load at 10k per min. you'll get in 10 min. to the limit of load jobs per day (100k). Source: https://cloud.google.com/bigquery/quotas#load_jobs

15. Question 15 of 25

    You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and-shift migration is necessary. However, the data you've been using will be migrated to migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

    • AUse Vertex AI for training existing Spark ML models
    • BRewrite your models on TensorFlow, and start using Vertex AI
    • CUse Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
    • DSpin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

    Show answer

    Correct answer: C

    How others answered

    • C65%
    • A35%

    Only 65% agreed on this answer — treat it as contested rather than settled.

    Explanation

    C is the answer. https://cloud.google.com/dataproc/docs/concepts/overview Dataproc is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data. Integrated — Dataproc has built-in integration with other Google Cloud Platform services, such as BigQuery, Cloud Storage, Cloud Bigtable, Cloud Logging, and Cloud Monitoring, so you have more than just a Spark or Hadoop cluster—you have a complete data platform. For example, you can use Dataproc to effortlessly ETL terabytes of raw log data directly into BigQuery for business reporting.

    Adapted from community discussion by zellck · 2022-12-01 · 4 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 15 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. Z

       zellck

       2022-12-01

       Selected C4 upvotes

       C is the answer. https://cloud.google.com/dataproc/docs/concepts/overview Dataproc is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data. Integrated — Dataproc has built-in integration with other Google Cloud Platform services, such as BigQuery, Cloud Storage, Cloud Bigtable, Cloud Logging, and Cloud Monitoring, so you have more than just a Spark or Hadoop cluster—you have a complete data platform. For example, you can use Dataproc to effortlessly ETL terabytes of raw log data directly into BigQuery for business reporting.

    2. V

       vamgcp

       2023-07-26

       Selected C6 upvotes

       Option C : It is the most rapid way to migrate your existing training pipelines to Google Cloud. It allows you to continue using your existing Spark ML models. It allows you to take advantage of the scalability and performance of Dataproc. It allows you to read data directly from BigQuery, which is a more efficient way to process large datasets

    3. B

       barnac1es

       2023-09-24

       Selected C2 upvotes

       Dataproc for Spark: Google Cloud Dataproc is a managed Spark and Hadoop service that allows you to run Spark jobs seamlessly on Google Cloud. It provides the flexibility to run Spark jobs using Spark MLlib and other Spark libraries. BigQuery Integration: You mentioned that your data is being migrated to BigQuery. Dataproc has native integration with BigQuery, allowing you to read data directly from BigQuery tables. This eliminates the need to export data from BigQuery to another storage system before processing it with Spark. Rapid Migration: This approach allows you to quickly migrate your existing Spark ML models and training pipelines without the need for a complete rewrite or extensive changes to your existing workflows. You can continue using your Spark ML models while adapting them to read data from BigQuery.

    4. M

       MaxNRG

       2023-12-19

       Selected C4 upvotes

       Use Cloud Dataproc, BigQuery, and Apache Spark ML for Machine Learning https://cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml Using Apache Spark with TensorFlow on Google Cloud Platform https://cloud.google.com/blog/products/gcp/using-apache-spark-with-tensorflow-on-google-cloud-platform

16. Question 16 of 25

    You need to choose a database for a new project that has the following requirements: ✑ Fully managed ✑ Able to automatically scale up ✑ Transactionally consistent ✑ Able to scale up to 6 TB ✑ Able to be queried using SQL Which database do you choose?

    • ACloud SQL
    • BCloud Bigtable
    • CCloud Spanner
    • DCloud Datastore

    Show answer

    Correct answer: A

    How others answered

    • A51%
    • C49%

    Only 51% agreed on this answer — treat it as contested rather than settled.

    Explanation

    Correct: A It asks for scaling up which can be done in cloud sql, horizontal scaling is not possible in cloud sql Automatic storage increase If you enable this setting, Cloud SQL checks your available storage every 30 seconds. If the available storage falls below a threshold size, Cloud SQL automatically adds additional storage capacity. If the available storage repeatedly falls below the threshold size, Cloud SQL continues to add storage until it reaches the maximum of 30 TB.

    Adapted from community discussion by [Removed] · 2020-03-25 · 35 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 16 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. [

       [Removed]

       2020-03-25

       35 upvotes

       Correct: A It asks for scaling up which can be done in cloud sql, horizontal scaling is not possible in cloud sql Automatic storage increase If you enable this setting, Cloud SQL checks your available storage every 30 seconds. If the available storage falls below a threshold size, Cloud SQL automatically adds additional storage capacity. If the available storage repeatedly falls below the threshold size, Cloud SQL continues to add storage until it reaches the maximum of 30 TB.

    2. G

       gcp_k

       2021-10-21

       9 upvotes

       Answer could be : A Both Cloud Spanner and Cloud SQL does not have auto scaling feature natively. You need to build automation around it based on metrics. Both Cloud SQL and Cloud Spanner supports SQL. With Cloud SQL, you can go up to 10 TB of storage which also satisfies the other requirement. Consistency - Of course, with Cloud SQL, you have single master and read replicas. So technically the data will be consistent across all instances so to speak. The reason why I didn't choose Spanner is - There is no requirement for HA DR, multi region, secondary indexes, etc.. So I choose "A"

    3. G

       Gcpyspark

       2020-12-20

       8 upvotes

       Google documentation says "Cloud SQL is a fully-managed database service that helps you set up, maintain, manage, and administer your relational databases on Google Cloud Platform."

    4. S

       StelSen

       2021-01-14

       7 upvotes

       Answer: C Look at key requirement: Automatic Scale Up (An ability to increase compute/memory capacity, Not storage. For storage, there is another requirement which is increase upto 6TB). So, Option-C is more appropriate, although the Cost is high. This is just an exam. In real world, I will convince customer to use Cloud SQL and will be able to use some Cloud Function to achieve near real time scaling even if I use Cloud SQL)

17. Question 17 of 25

    Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?

    • AUse Google Stackdriver Audit Logs to review data access.
    • BGet the identity and access management IIAM) policy of each table
    • CUse Stackdriver Monitoring to see the usage of BigQuery query slots.
    • DUse the Google Cloud Billing API to see what account the warehouse is being billed to.

    Show answer

    Correct answer: A

    How others answered

    • A91%

    Explanation

    To begin securing your data warehouse in Google BigQuery and gain insights into what everyone is doing with the datasets, the first step you should take is: A. Use Google Stackdriver Audit Logs to review data access. Reviewing the audit logs provides visibility into who is accessing your data, when they are doing so, and what actions they are taking within BigQuery. This is crucial for understanding current data usage and potential security risks. Option B (getting the IAM policy of each table) is important but more focused on controlling access rather than discovering what everyone is currently doing. Option C (using Stackdriver Monitoring to see query slots usage) can help with monitoring and optimizing your BigQuery usage but doesn't provide a comprehensive view of what users are doing with the data. Option D (using the Google Cloud Billing API) is more related to tracking billing information rather than understanding what users are doing with the data.

    Adapted from community discussion by rtcpost · 2024-09-24 · 4 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    • Google Cloud documentation
    • Google Cloud documentation
    • Google Cloud documentation
    • Google Cloud documentation
    • Google Cloud documentation

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    Question 17 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. R

       rtcpost

       2024-09-24

       Selected A4 upvotes

       To begin securing your data warehouse in Google BigQuery and gain insights into what everyone is doing with the datasets, the first step you should take is: A. Use Google Stackdriver Audit Logs to review data access. Reviewing the audit logs provides visibility into who is accessing your data, when they are doing so, and what actions they are taking within BigQuery. This is crucial for understanding current data usage and potential security risks. Option B (getting the IAM policy of each table) is important but more focused on controlling access rather than discovering what everyone is currently doing. Option C (using Stackdriver Monitoring to see query slots usage) can help with monitoring and optimizing your BigQuery usage but doesn't provide a comprehensive view of what users are doing with the data. Option D (using the Google Cloud Billing API) is more related to tracking billing information rather than understanding what users are doing with the data.

    2. R

       Radhika7983

       2020-11-06

       7 upvotes

       Table access control is now possible in big query. However, before even checking table access control permission which is not set by the company as a formal security policy yet, we need to first understand by looking at the big query immutable audit logs as who is accessing what DAT sets and tables. Based on the information, access control policy at dataset and table level can be set. So the correct answer is A

    3. R

       rocky48

       2024-09-24

       Selected A2 upvotes

       A. Use Google Stackdriver Audit Logs to review data access. Reviewing the audit logs provides visibility into who is accessing your data, when they are doing so, and what actions they are taking within BigQuery. This is crucial for understanding current data usage and potential security risks. Option B (getting the IAM policy of each table) is important but more focused on controlling access rather than discovering what everyone is currently doing. Option C (using Stackdriver Monitoring to see query slots usage) can help with monitoring and optimizing your BigQuery usage but doesn't provide a comprehensive view of what users are doing with the data. Option D (using the Google Cloud Billing API) is more related to tracking billing information rather than understanding what users are doing with the data.

    4. M

       MaxNRG

       2024-09-24

       2 upvotes

       A is correct because this is the best way to get granular access to data showing which users are accessing which data. B is not correct because we already know that all users already have access to all data, so this information is unlikely to be useful. It will also not show what users have done, just what they can do. C is not correct because slot usage will not inform security policy. D is not correct because a billing account is typically shared among many people and will only show the amount of data queried and stored https://cloud.google.com/bigquery/docs/reference/auditlogs/#mapping-audit-entries-to-log-streams https://cloud.google.com/bigquery/docs/monitoring#slots-available

18. Question 18 of 25

    Your company currently runs a large on-premises cluster using Spark, Hive, and HDFS in a colocation facility. The cluster is designed to accommodate peak usage on the system; however, many jobs are batch in nature, and usage of the cluster fluctuates quite dramatically. Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more serverless offerings in order to take advantage of the cloud. Because of the timing of their contract renewal with the colocation facility, they have only 2 months for their initial migration. How would you recommend they approach their upcoming migration strategy so they can maximize their cost savings in the cloud while still executing the migration in time?

    • AMigrate the workloads to Dataproc plus HDFS; modernize later.
    • BMigrate the workloads to Dataproc plus Cloud Storage; modernize later.
    • CMigrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery.
    • DModernize the Spark workload for Dataflow and the Hive workload for BigQuery.

    Show answer

    Correct answer: B

    How others answered

    • B89%

    Explanation

    B is the answer. https://cloud.google.com/architecture/hadoop/migrating-apache-spark-jobs-to-cloud-dataproc#overview When you want to move your Apache Spark workloads from an on-premises environment to Google Cloud, we recommend using Dataproc to run Apache Spark/Apache Hadoop clusters. Dataproc is a fully managed, fully supported service offered by Google Cloud. It allows you to separate storage and compute, which helps you to manage your costs and be more flexible in scaling your workloads. https://cloud.google.com/bigquery/docs/migration/hive#data_migration Migrating Hive data from your on-premises or other cloud-based source cluster to BigQuery has two steps: 1. Copying data from a source cluster to Cloud Storage 2. Loading data from Cloud Storage into BigQuery

    Adapted from community discussion by zellck · 2023-05-30 · 8 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 18 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. Z

       zellck

       2023-05-30

       Selected B8 upvotes

       B is the answer. https://cloud.google.com/architecture/hadoop/migrating-apache-spark-jobs-to-cloud-dataproc#overview When you want to move your Apache Spark workloads from an on-premises environment to Google Cloud, we recommend using Dataproc to run Apache Spark/Apache Hadoop clusters. Dataproc is a fully managed, fully supported service offered by Google Cloud. It allows you to separate storage and compute, which helps you to manage your costs and be more flexible in scaling your workloads. https://cloud.google.com/bigquery/docs/migration/hive#data_migration Migrating Hive data from your on-premises or other cloud-based source cluster to BigQuery has two steps: 1. Copying data from a source cluster to Cloud Storage 2. Loading data from Cloud Storage into BigQuery

    2. J

       John_Pongthorn

       2023-04-03

       Selected B3 upvotes

       B is most likely 1. migrate job and infrastructure to dataproc on clound 2. any data, move from hdfs on-premise to google cloud storage ( one of them is Hive) If you want to modernize Hive to Bigquery , you are need to move it into GCS(preceding step) first and load it into bigquery that is all. https://cloud.google.com/blog/products/data-analytics/apache-hive-to-bigquery https://cloud.google.com/architecture/hadoop/migrating-apache-spark-jobs-to-cloud-dataproc https://cloud.google.com/architecture/hadoop/hadoop-gcp-migration-data

    3. M

       MaxNRG

       2024-06-19

       Selected B2 upvotes

       Based on the time constraint of 2 months and the goal to maximize cost savings, I would recommend option B - Migrate the workloads to Dataproc plus Cloud Storage; modernize later. The key reasons are: • Dataproc provides a fast, native migration path from on-prem Spark and Hive to the cloud. This allows meeting the 2 month timeline. • Using Cloud Storage instead of HDFS avoids managing clusters for variable workloads and provides cost savings. • Further optimizations and modernization to serverless (Dataflow, BigQuery) can happen incrementally later without time pressure.

    4. M

       MaxNRG

       2024-06-19

       3 upvotes

       Option A still requires managing HDFS. Option C and D require full modernization of workloads in 2 months which is likely infeasible. Therefore, migrating to Dataproc with Cloud Storage fast tracks the migration within 2 months while realizing immediate cost savings, enabling the flexibility to iteratively modernize and optimize the workloads over time.

19. Question 19 of 25

    Your company is implementing a data warehouse using BigQuery, and you have been tasked with designing the data model. You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days. Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

    • ADenormalize the data.
    • BShard the data by customer ID.
    • CMaterialize the dimensional data in views.
    • DPartition the data by transaction date.

    Show answer

    Correct answer: D

    How others answered

    • D88%

    Explanation

    D is the right answer because it does not increase storage costs. A is not correct because denormalization typically increases the amount of storage needed.

    Adapted from community discussion by waiebdi · 2023-08-12 · 14 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 19 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. W

       waiebdi

       2023-08-12

       Selected D14 upvotes

       D is the right answer because it does not increase storage costs. A is not correct because denormalization typically increases the amount of storage needed.

    2. N

       NicolasN

       2023-05-06

       Selected D4 upvotes

       A sneaky question. [D] Yes - Since data is queried with date criteria, partitioning by transaction date will surely speed it up without further cost. [A] Yes? - Star schema is a denormalized model but as user Reall01 pointed out, the option to use nested and repeated fields can be considered a further denormalization. And if the model hasn't frequently changing dimensions, this kind of denormalization will result in increased performance, according to https://cloud.google.com/bigquery/docs/loading-data#loading_denormalized_nested_and_repeated_data : "In some circumstances, denormalizing your data and using nested and repeated fields doesn't result in increased performance. Avoid denormalization in these use cases: - You have a star schema with frequently changing dimensions" I guess that the person who added this question, had in mind [D] as a correct answer. If the questioner had all the aforementioned under consideration, would state clearly if there are frequently changing dimensions in the schema.

    3. Z

       zellck

       2023-05-29

       Selected D3 upvotes

       D is the answer. https://cloud.google.com/bigquery/docs/partitioned-tables A partitioned table is a special table that is divided into segments, called partitions, that make it easier to manage and query your data. By dividing a large table into smaller partitions, you can improve query performance, and you can control costs by reducing the number of bytes read by a query.

    4. [

       [Removed]

       2023-03-05

       Selected D2 upvotes

       https://cloud.google.com/bigquery/docs/migration/schema-data-overview#migrating_data_and_schema_from_on-premises_to_bigquery Star schema. This is a denormalized model, where a fact table collects metrics such as order amount, discount, and quantity, along with a group of keys. These keys belong to dimension tables such as customer, supplier, region, and so on. Graphically, the model resembles a star, with the fact table in the center surrounded by dimension tables. Star schema is already denormalized so partition makes more sense going with D

20. Question 20 of 25

    You are building a report-only data warehouse where the data is streamed into BigQuery via the streaming API. Following Google's best practices, you have both a staging and a production table for the data. How should you design your data loading to ensure that there is only one master dataset without affecting performance on either the ingestion or reporting pieces?

    • AHave a staging table that is an append-only model, and then update the production table every three hours with the changes written to staging.
    • BHave a staging table that is an append-only model, and then update the production table every ninety minutes with the changes written to staging.
    • CHave a staging table that moves the staged data over to the production table and deletes the contents of the staging table every three hours.
    • DHave a staging table that moves the staged data over to the production table and deletes the contents of the staging table every thirty minutes.

    Show answer

    Correct answer: C

    How others answered

    • C62%
    • A26%

    Only 62% agreed on this answer — treat it as contested rather than settled.

    Explanation

    [C] I found the correct answer based on a real case, where Google's Solutions Architect team decided to move an internal process to use BigQuery. The related doc is here: https://cloud.google.com/blog/products/data-analytics/moving-a-publishing-workflow-to-bigquery-for-new-data-insights

    Adapted from community discussion by NicolasN · 2022-11-06 · 20 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 20 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. N

       NicolasN

       2022-11-06

       Selected C20 upvotes

       [C] I found the correct answer based on a real case, where Google's Solutions Architect team decided to move an internal process to use BigQuery. The related doc is here: https://cloud.google.com/blog/products/data-analytics/moving-a-publishing-workflow-to-bigquery-for-new-data-insights

    2. N

       NicolasN

       2022-11-06

       18 upvotes

       The interesting excerpts: "Following common extract, transform, load (ETL) best practices, we used a staging table and a separate production table so that we could load data into the staging table without impacting users of the data. The design we created based on ETL best practices called for first deleting all the records from the staging table, loading the staging table, and then replacing the production table with the contents." "When using the streaming API, the BigQuery streaming buffer remains active for about 30 to 60 minutes or more after use, which means that you can’t delete or change data during that time. Since we used the streaming API, we scheduled the load every three hours to balance getting data into BigQuery quickly and being able to subsequently delete the data from the staging table during the load process."

    3. N

       nwk

       2022-09-05

       11 upvotes

       Vote B - "Some recently streamed rows might not be available for table copy typically for a few minutes. In rare cases, this can take up to 90 minutes" https://cloud.google.com/bigquery/docs/streaming-data-into-bigquery#dataavailability

    4. Z

       zellck

       2022-11-29

       Selected C7 upvotes

       C is the answer. https://cloud.google.com/blog/products/data-analytics/moving-a-publishing-workflow-to-bigquery-for-new-data-insights Following common extract, transform, load (ETL) best practices, we used a staging table and a separate production table so that we could load data into the staging table without impacting users of the data. The design we created based on ETL best practices called for first deleting all the records from the staging table, loading the staging table, and then replacing the production table with the contents. When using the streaming API, the BigQuery streaming buffer remains active for about 30 to 60 minutes or more after use, which means that you can’t delete or change data during that time. Since we used the streaming API, we scheduled the load every three hours to balance getting data into BigQuery quickly and being able to subsequently delete the data from the staging table during the load process.

21. Question 21 of 25

    Your new customer has requested daily reports that show their net consumption of Google Cloud compute resources and who used the resources. You need to quickly and efficiently generate these daily reports. What should you do?

    • ADo daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.
    • BFilter data in Cloud Logging by project, resource, and user; then export the data in CSV format.
    • CFilter data in Cloud Logging by project, log type, resource, and user, then import the data into BigQuery.
    • DExport Cloud Logging data to Cloud Storage in CSV format. Cleanse the data using Dataprep, filtering by project, resource, and user.

    Show answer

    Correct answer: A

    How others answered

    • A78%

    Explanation

    A. Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user. You cannot import custom or filtered billing criteria into BigQuery. There are three types of Cloud Billing data tables with a fixed schema that must further drilled-down via BigQuery views. Reference: https://cloud.google.com/billing/docs/how-to/export-data-bigquery#setup

    Adapted from community discussion by AWSandeep · 2023-03-02 · 8 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 21 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. A

       AWSandeep

       2023-03-02

       8 upvotes

       A. Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user. You cannot import custom or filtered billing criteria into BigQuery. There are three types of Cloud Billing data tables with a fixed schema that must further drilled-down via BigQuery views. Reference: https://cloud.google.com/billing/docs/how-to/export-data-bigquery#setup

    2. M

       maci_f

       2023-07-25

       Selected A4 upvotes

       B and D do not consider the log type field. C looks good and I would go for it. However, A looks equally good and I've found a CloudSkillsBoost lab that is exactly describing what answer A does, i.e. exporting logs to BQ and then creating a VIEW. https://www.cloudskillsboost.google/focuses/6100?parent=catalog I think the advantage of exporting complete logs (i.e. filtering them after they reach BQ) is that in case we would want to adjust the reporting in the future, we would have the complete logs with all fields available, whereas with C we would need to take extra steps.

    3. D

       devaid

       2023-04-17

       5 upvotes

       2nd tought: Definitely A. If you go to google documentation for export billing, you see a message that "Exporting to JSON or CSV is obsolet. Use Big Query instead". Also why A? Look https://cloud.google.com/billing/docs/how-to/export-data-bigquery https://cloud.google.com/billing/docs/how-to/bq-examples#total-costs-on-invoice You can make a fast report template al Data Studio that read a Big Query view.

    4. T

       TNT87

       2023-03-13

       4 upvotes

       Ans is C https://cloud.google.com/logging/docs/export/aggregated_sinks D isn't correct because Cloud storage is used as a sink when logs are in json format not csv. https://cloud.google.com/logging/docs/export/aggregated_sinks#supported-destinations

22. Question 22 of 25

    You need to migrate 1 PB of data from an on-premises data center to Google Cloud. Data transfer time during the migration should take only a few hours. You want to follow Google-recommended practices to facilitate the large data transfer over a secure connection. What should you do?

    • AEstablish a Cloud Interconnect connection between the on-premises data center and Google Cloud, and then use the Storage Transfer Service.
    • BUse a Transfer Appliance and have engineers manually encrypt, decrypt, and verify the data.
    • CEstablish a Cloud VPN connection, start gcloud compute scp jobs in parallel, and run checksums to verify the data.
    • DReduce the data into 3 TB batches, transfer the data using gsutil, and run checksums to verify the data.

    Show answer

    Correct answer: A

    How others answered

    • A63%
    • B37%

    Only 63% agreed on this answer — treat it as contested rather than settled.

    Explanation

    A is the answer. https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets#storage-transfer-service-for-large-transfers-of-on-premises-data Like gsutil, Storage Transfer Service for on-premises data enables transfers from network file system (NFS) storage to Cloud Storage. Although gsutil can support small transfer sizes (up to 1 TB), Storage Transfer Service for on-premises data is designed for large-scale transfers (up to petabytes of data, billions of files).

    Adapted from community discussion by zellck · 2022-11-28 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    • Google Cloud documentation
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    Question 22 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. D

       devaid

       2022-10-05

       Selected A5 upvotes

       Well it doesn't mentions anything about not enough bandwidth to meet your project deadline. I guess you can assume they have 200GBps+ of bandwith, otherwise it shouldn't take only a few hours.

    2. M

       MaxNRG

       2023-12-21

       Selected A2 upvotes

       Cloud Interconnect provides a dedicated private connection between on-prem and Google Cloud for high bandwidth (up to 100 Gbps) and low latency. This facilitates large, fast data transfers. Storage Transfer Service supports parallel data transfers over Cloud Interconnect. It can transfer petabyte-scale datasets faster by transferring objects in parallel. Storage Transfer Service uses HTTPS encryption in transit and at rest by default for secure data transfers. It follows Google-recommended practices for large data migrations vs ad hoc methods like gsutil or scp. The other options would take too long for a 1 PB transfer (VPN capped at 3 Gbps, manual transfers) or introduce extra steps like batching and checksums. Cloud Interconnect + Storage Transfer is the recommended Google solution.

    3. A

       Atnafu

       2022-11-25

       4 upvotes

       B It takes 30hrs with 100Gbps bandwidth- more than a day to transfer https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets#:~:text=addresses%20or%20NATs.-,Online%20versus%20offline%20transfer,A%20certain%20amount%20of%20management%20overhead%20is%20built%20into%20these%20calculations.,-As%20noted%20earlier

    4. N

       Nandhu95

       2023-03-19

       Selected A1 upvote

       Expected time via transfer appliance is around 20 days , and achieving the same using Storage transfer service with highest bandwidth of 100GPS is 30 hrs, so hence its been asked for hrs .. its A Acquiring a Transfer Appliance is straightforward. In the Google Cloud console, you request a Transfer Appliance, indicate how much data you have, and then Google ships one or more appliances to your requested location. You're given a number of days to transfer your data to the appliance ("data capture") and ship it back to Google. The expected turnaround time for a network appliance to be shipped, loaded with your data, shipped back, and rehydrated on Google Cloud is 20 days. If your online transfer timeframe is calculated to be substantially more than this timeframe, consider Transfer Appliance. The total cost for the 300 TB device process is less than $2,500.

23. Question 23 of 25

    You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?

    • AThe message body for the sensor event is too large.
    • BYour custom endpoint has an out-of-date SSL certificate.
    • CThe Cloud Pub/Sub topic has too many messages published to it.
    • DYour custom endpoint is not acknowledging messages within the acknowledgement deadline.

    Show answer

    Correct answer: D

    How others answered

    • D86%

    Explanation

    The Answer should be D. The custom endpoint is not acknowledging the message, that is the reason for Pub/Sub to send the message again and again. Not B.

    Adapted from community discussion by jvg637 · 2020-03-11 · 88 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 23 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. J

       jvg637

       2020-03-11

       88 upvotes

       The Answer should be D. The custom endpoint is not acknowledging the message, that is the reason for Pub/Sub to send the message again and again. Not B.

    2. M

       MauryaSushil

       2020-04-05

       10 upvotes

       D : Doubt should be only between B & D. But B is not possible because if SSL is expired then endpoint URL will not receive any messages forget about duplicates. So It should be D for Duplicates.

    3. R

       Radhika7983

       2024-09-24

       6 upvotes

       The correct answer is D. Look for the link https://cloud.google.com/pubsub/docs/faq Why are there too many duplicate messages? Pub/Sub guarantees at-least-once message delivery, which means that occasional duplicates are to be expected. However, a high rate of duplicates may indicate that the client is not acknowledging messages within the configured ack_deadline_seconds, and Pub/Sub is retrying the message delivery. This can be observed in the monitoring metrics pubsub.googleapis.com/subscription/pull_ack_message_operation_count for pull subscriptions, and pubsub.googleapis.com/subscription/push_request_count for push subscriptions. Look for elevated expired or webhook_timeout values in the /response_code. This is particularly likely if there are many small messages, since Pub/Sub may batch messages internally and a partially acknowledged batch will be fully redelivered. Another possibility is that the subscriber is not acknowledging some messages because the code path processing those specific messages fails, and the Acknowledge call is never made; or the push endpoint never responds or responds with an error.

    4. A

       atnafu2020

       2024-09-24

       5 upvotes

       D Why are there too many duplicate messages? Pub/Sub guarantees at-least-once message delivery, which means that occasional duplicates are to be expected. However, a high rate of duplicates may indicate that the client is not acknowledging messages within the configured ack_deadline_seconds, and Pub/Sub is retrying the message delivery. This can be observed in the monitoring metrics pubsub.googleapis.com/subscription/pull_ack_message_operation_count for pull subscriptions, and pubsub.googleapis.com/subscription/push_request_count for push subscriptions. Look for elevated expired or webhook_timeout values in the /response_code. This is particularly likely if there are many small messages, since Pub/Sub may batch messages internally and a partially acknowledged batch will be fully redelivered.

24. Question 24 of 25

    Your platform on your on-premises environment generates 100 GB of data daily, composed of millions of structured JSON text files. Your on-premises environment cannot be accessed from the public internet. You want to use Google Cloud products to query and explore the platform data. What should you do?

    • AUse Cloud Scheduler to copy data daily from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
    • BUse a Transfer Appliance to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
    • CUse Transfer Service for on-premises data to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
    • DUse the BigQuery Data Transfer Service dataset copy to transfer all data into BigQuery.

    Show answer

    Correct answer: C

    How others answered

    • C90%

    Explanation

    "Your on-premises environment cannot be accessed from the public internet" statement suggests that inbound traffic from internet is NOT allowed however, it doesn't mean that outbound internet connectivity from on-prem resources is not possible. Any on-prem system with outbound internet access can copy/transfer the CSV files. CSV files are located on a filesystem, therefore you cannot copy them with BQ Transfer Service. Leaving only possible option; first copy CSVs to cloud storage then run BQ Transfer Service pls refer to https://cloud.google.com/bigquery/docs/dts-introduction#supported_data_sources

    Adapted from community discussion by cetanx · 2023-06-27 · 7 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 24 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. M

       muhusman

       2023-04-23

       10 upvotes

       Therefore, the correct option is C. Use Transfer Service for on-premises data to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery. Option A is incorrect because Cloud Scheduler is not designed for data transfer, but rather for scheduling the execution of Cloud Functions, Cloud Run, or App Engine applications. Option B is incorrect because Transfer Appliance is designed for large-scale data transfers from on-premises environments to Google Cloud and is not suitable for transferring data on a daily basis. Option D is also incorrect because the BigQuery Data Transfer Service dataset copy feature is designed for copying datasets between BigQuery projects and not suitable for copying data from on-premises environments to BigQuery.

    2. C

       cetanx

       2023-06-27

       Selected C7 upvotes

       "Your on-premises environment cannot be accessed from the public internet" statement suggests that inbound traffic from internet is NOT allowed however, it doesn't mean that outbound internet connectivity from on-prem resources is not possible. Any on-prem system with outbound internet access can copy/transfer the CSV files. CSV files are located on a filesystem, therefore you cannot copy them with BQ Transfer Service. Leaving only possible option; first copy CSVs to cloud storage then run BQ Transfer Service pls refer to https://cloud.google.com/bigquery/docs/dts-introduction#supported_data_sources

    3. Z

       zellck

       2022-11-28

       Selected C3 upvotes

       C is the answer. https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets#storage-transfer-service-for-large-transfers-of-on-premises-data Storage Transfer Service for on-premises data enables transfers from network file system (NFS) storage to Cloud Storage. https://cloud.google.com/bigquery/docs/cloud-storage-transfer-overview The BigQuery Data Transfer Service for Cloud Storage lets you schedule recurring data loads from Cloud Storage buckets to BigQuery.

    4. W

       wjtb

       2023-03-09

       3 upvotes

       I would say B. It is the ONLY option that is possible without data being accessible over the public (unless we assume that a direct interconnect is already set up, which seems farfetched). Also, nowhere does it say how up-to-date the data needs to be that we are querying or how often we need to query, only that the data increases in size by 100gb per day (indicating that its going to be a lot of data)

25. Question 25 of 25

    Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

    • ARun a local version of Jupiter on the laptop.
    • BGrant the user access to Google Cloud Shell.
    • CHost a visualization tool on a VM on Google Compute Engine.
    • DDeploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

    Show answer

    Correct answer: D

    How others answered

    • D98%

    Explanation

    correct answer -> Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine. Google says: Use Cloud Datalab to easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively. Reference: https://cloud.google.com/datalab/docs ***This is exactly the tool needed in this scenario*** But what about her laptop slowing her down? Well, Cloud Datalab is packaged as a container and run in a VM (Virtual Machine) instance. Reference: https://cloud.google.com/datalab/docs/concepts/key-concepts

    Adapted from community discussion by samdhimal · 2022-07-22 · 5 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

    Supporting references

    • Google Cloud documentation
    • Google Cloud documentation
    • Google Cloud documentation
    • Google Cloud documentation
    • Google Cloud documentation

    View community discussion4

    Candidate context

    Question 25 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by PDE or CertExamHQ.

    1. [

       [Removed]

       2020-09-27

       14 upvotes

       Answer: D Description: Datalab provides Jupyter for this kind of work

    2. D

       dem2021

       2021-11-12

       10 upvotes

       Datalab runs as a container inside a VM !! https://cloud.google.com/datalab/docs/concepts/key-concepts

    3. D

       daghayeghi

       2021-09-09

       7 upvotes

       The correct answer to this question is B. Here is why:- Option A : Not correct, running a local version of Jupiter will not help her to work with the existing Casandra DB hosted in a VM in Google Cloud Option B : This is the correct answer, you can access google cloud SHELL either by installing local SDK's or you can access through Web. Go to the console page, login with your credential and access cloud shell. Cloud shell will allow you to create a Data Studio for visualization or any other machine learning tools (Cloud AI API) - all through command lines Option C- Not correct, does not specify which visualization tool - its too generic Option D - Not correct, Google cloud Data Lab is a managed service, you dont need a VM (Compute instance) to load Data Studio

    4. K

       kino2020

       2021-03-12

       5 upvotes

       Problem statement. ・ She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. ・ You want to help her perform her tasks. ------------------------------------------------------------------------https://console.cloud.google.com/marketplace/details/click-to-deploy-images/cassandra?filter=category%3Adatabase&id=25ca0967-cd8e-419e-b554-fe32e87f04be&walkthrough_tutorial_id=java_gae_quickstart ------------------------------------------------------------------------ Build and run your "Hello, world!" App You will learn how to run your app using Cloud Shell, right in your browser. At the end, you'll deploy your app to the web using the App Engine Maven plugin. ------------------------------------------------------------------------ Therefore, B is the correct answer.

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