PDE practice questions 21–30

1. Question 21 · 340 in bank

   You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour. How should you design the solution?

   • AUse Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
   • BUse Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
   • CUse Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
   • DUse Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

   Show answer

   Answer: D

   Explanation

   Careful doing that: I got a question where you had to choose between Kafka and Pub/Sub... and the solution required to be able to replay all messages without time limit. So no Pub/Sub there. This being a Google cert does not mean that they always force Google solutions.

   Source: Adapted from community discussion — hendrixlives

   References

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

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2. Question 22 · 340 in bank

   You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements: ✑ Each department should have access only to their data. ✑ Each department will have one or more leads who need to be able to create and update tables and provide them to their team. ✑ Each department has data analysts who need to be able to query but not modify data. How should you set access to the data in BigQuery?

   • ACreate a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.
   • BCreate a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.
   • CCreate a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.
   • DCreate a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.

   Show answer

   Answer: B

   Explanation

   I vote B because C and D says that the role is on the project that the table is in, this mean that the role is at the project level that implies that: If you create a dataset in a project that contains any editors, BigQuery grants those users the bigquery.dataEditor predefined role for the new dataset. (from https://cloud.google.com/bigquery/docs/access-control-basic-roles#project-basic-roles) A can't not be because the analysts, in this case, can access the data. B grant to the leads update their datasets, that's mean create tables, and the analysts only read their datasets.

   Source: Adapted from community discussion — josrojgra

   References

   • Google Cloud documentation
   • Google Cloud documentation

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3. Question 23 · 340 in bank

   You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

   • AChange the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
   • BChange the row key syntax in your Cloud Bigtable table to begin with a random number per second.
   • CChange the data pipeline to use BigQuery for storing stock trades, and update your application.
   • DUse Cloud Dataflow to write a summary of each day's stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

   Show answer

   Answer: A

   Explanation

   A is the answer. https://cloud.google.com/bigtable/docs/schema-design#row-keys It's important to create a row key that makes it possible to retrieve a well-defined range of rows. Otherwise, your query requires a table scan, which is much slower than retrieving specific rows. https://cloud.google.com/bigtable/docs/schema-design#row-keys-avoid Some types of row keys can make it difficult to query your data, and some result in poor performance. This section describes some types of row keys that you should avoid using in Bigtable. - Row keys that start with a timestamp. This pattern causes sequential writes to be pushed onto a single node, creating a hotspot. If you put a timestamp in a row key, precede it with a high-cardinality value like a user ID to avoid hotspots.

   Source: Adapted from community discussion — zellck

   References

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

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4. Question 24 · 340 in bank

   Select 3

   Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other's data. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

   • ALoad data into different partitions.
   • BLoad data into a different dataset for each client.
   • CPut each client's BigQuery dataset into a different table.
   • DRestrict a client's dataset to approved users.
   • EOnly allow a service account to access the datasets.
   • FUse the appropriate identity and access management (IAM) roles for each client's users.

   Show answer

   Answer: BDF

   Explanation

   B. Load data into a different dataset for each client: Organize the data into separate datasets for each client. This ensures data isolation and simplifies access control. D. Restrict a client's dataset to approved users: Implement access controls by specifying which users or groups are allowed to access each client's dataset. This restricts data access to approved users only. F. Use the appropriate identity and access management (IAM) roles for each client's users: Assign IAM roles based on client-specific requirements to manage permissions effectively. IAM roles help control access at a more granular level, allowing you to tailor access to specific users or groups within each client's dataset. These steps ensure that each client's data is separated, and access is controlled based on client-specific requirements. Options A, C, and E, while important in other contexts, are not sufficient on their own to ensure client data isolation and access control in a multi-client environment.

   Source: Adapted from community discussion — rtcpost

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5. Question 25 · 340 in bank

   You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?

   • AAn alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/ used_bytes for the destination
   • BAn alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/ used_bytes for the destination
   • CAn alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/ num_undelivered_messages for the destination
   • DAn alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/ num_undelivered_messages for the destination

   Show answer

   Answer: B

   Explanation

   You would want to get alerted only if Pipeline fails & not if it is running fine. I think Option [B] is correct, because in event of Pipeline failure : 1) subscription/ num_undelivered_messages would pile up at a constant rate as the source has consistent throughput 2) instance/storage/ used_bytes will get closer to zero. Hence need to monitor it's rate of change

   Source: Adapted from community discussion — dambilwa

   References

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

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6. Question 26 · 340 in bank

   You currently have a single on-premises Kafka cluster in a data center in the us-east region that is responsible for ingesting messages from IoT devices globally. Because large parts of globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your Kafka cluster. This is becoming difficult to manage and prohibitively expensive. What is the Google-recommended cloud native architecture for this scenario?

   • AEdge TPUs as sensor devices for storing and transmitting the messages.
   • BCloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.
   • CAn IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.
   • DA Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.

   Show answer

   Answer: C

   Explanation

   C is the answer. https://cloud.google.com/architecture/iot-overview#cloud-pubsub Pub/Sub can act like a shock absorber and rate leveller for both incoming data streams and application architecture changes. Many devices have limited ability to store and retry sending telemetry data. Pub/Sub scales to handle data spikes that can occur when swarms of devices respond to events in the physical world, and buffers these spikes to help isolate them from applications monitoring the data.

   Source: Adapted from community discussion — zellck

   References

   • Google Cloud documentation

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7. Question 27 · 340 in bank

   Select 2

   You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this? (Choose two.)

   • AUse managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
   • BUse managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
   • CUse managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
   • DWrite an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
   • EWrite an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.

   Show answer

   Answer: AB

   Explanation

   AC https://cloud.google.com/datastore/docs/export-import-entities C: To import only a subset of entities or to import data into BigQuery, you must specify an entity filter in your export. B: Not correct since you want to store in a different environment than Datastore. Tho this statment is true: Data exported from one Datastore mode database can be imported into another Datastore mode database, even one in another project. A is correct Billing and pricing for managed exports and imports in Datastore Output files stored in Cloud Storage count towards your Cloud Storage data storage costs. Steps to Export all the entities 1. Go to the Datastore Entities Export page in the Google Cloud Console. 2. Go to the Datastore Export page 2. Set the Namespace field to All Namespaces, and set the Kind field to All Kinds. 3. Below Destination, enter the name of your "Cloud Storage bucket". 4. Click Export.

   Source: Adapted from community discussion — atnafu2020

   References

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

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8. Question 28 · 340 in bank

   Select 2

   You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the initial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? (Choose two.)

   • ADenormalize the data as must as possible.
   • BPreserve the structure of the data as much as possible.
   • CUse BigQuery UPDATE to further reduce the size of the dataset.
   • DDevelop a data pipeline where status updates are appended to BigQuery instead of updated.
   • ECopy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.

   Show answer

   Answer: AD

   Explanation

   A, D: Using BigQuery as an OLTP store is considered an anti-pattern. Because OLTP stores have a high volume of updates and deletes, they are a mismatch for the data warehouse use case. To decide which storage option best fits your use case, review the Cloud storage products table. BigQuery is built for scale and can scale out as the size of the warehouse grows, so there is no need to delete older data. By keeping the entire history, you can deliver more insight on your business. If the storage cost is a concern, you can take advantage of BigQuery's long term storage pricing by archiving older data and using it for special analysis when the need arises. If you still have good reasons for dropping older data, you can use BigQuery's native support for date-partitioned tables and partition expiration. In other words, BigQuery can automatically delete older data. https://cloud.google.com/solutions/bigquery-data-warehouse#handling_change

   Source: Adapted from community discussion — daghayeghi

   References

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

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9. Question 29 · 340 in bank

   You are designing a cloud-native historical data processing system to meet the following conditions: ✑ The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Dataproc, BigQuery, and Compute Engine. ✑ A batch pipeline moves daily data. ✑ Performance is not a factor in the solution. ✑ The solution design should maximize availability. How should you design data storage for this solution?

   • ACreate a Dataproc cluster with high availability. Store the data in HDFS, and perform analysis as needed.
   • BStore the data in BigQuery. Access the data using the BigQuery Connector on Dataproc and Compute Engine.
   • CStore the data in a regional Cloud Storage bucket. Access the bucket directly using Dataproc, BigQuery, and Compute Engine.
   • DStore the data in a multi-regional Cloud Storage bucket. Access the data directly using Dataproc, BigQuery, and Compute Engine.

   Show answer

   Answer: D

   Explanation

   Problem: How to store data? Considerations: High availability, performance not an issue A → avoid HDFS C → multi-regional > regional in terms of availability B could be the answer but we’re dealing with PDF documents, we need blob storage (cloud storage). If we only have csv or Avro, this may be the answer

   Source: Adapted from community discussion — jkhong

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10. Question 30 · 340 in bank

    You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

    • AStore and process the entire dataset in BigQuery.
    • BStore and process the entire dataset in Bigtable.
    • CStore the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
    • DStore the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as 80% warm and 20% active.

    Show answer

    Answer: C

    Explanation

    A and B can be eliminated right away as they do not talk about providing for other cloud providers. between C and D. The question says nothing about warm or cold data-rather that data should be made available for other providers--C--can fulfill this condition. Answer C.

    Source: Adapted from community discussion — AJKumar

    References

    • Google Cloud documentation

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PDE practice, question 21. 340 questions available.