PDE practice questions 161–170

1. Question 161 · 340 in bank

   You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?

   • AUse your model to run predictions on fresh customer input data.
   • BMonitor your model performance, and make any adjustments needed.
   • CDelineate what data will be used for testing and what will be used for training the model.
   • DTest and evaluate your model on your curated data to determine how well the model performs.

   Show answer

   Answer: C

   Explanation

   Option C - you've just concluded processing data, ending up with clean and prepared data for the model. Now you need to decide how to split the data for testing and for training. Only afterwards, you can train the model, evaluate it, fine tune it and, eventually, predict with it

   Source: Adapted from community discussion — Matt_108

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2. Question 162 · 340 in bank

   You have one BigQuery dataset which includes customers’ street addresses. You want to retrieve all occurrences of street addresses from the dataset. What should you do?

   • AWrite a SQL query in BigQuery by using REGEXP_CONTAINS on all tables in your dataset to find rows where the word “street” appears.
   • BCreate a deep inspection job on each table in your dataset with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.
   • CCreate a discovery scan configuration on your organization with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.
   • DCreate a de-identification job in Cloud Data Loss Prevention and use the masking transformation.

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   Answer: B

   Explanation

   - Cloud Data Loss Prevention (Cloud DLP) provides powerful inspection capabilities for sensitive data, including predefined detectors for infoTypes such as STREET_ADDRESS. - By creating a deep inspection job for each table with the STREET_ADDRESS infoType, you can accurately identify and retrieve rows that contain street addresses.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

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3. Question 163 · 340 in bank

   Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform team. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?

   • A1. Create one lake for each team. Inside each lake, create one zone for each domain. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Have the central data platform team manage all zones’ data assets.
   • B1. Create one lake for each team. Inside each lake, create one zone for each domain. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Direct each domain to manage their own zone’s data assets.
   • C1. Create one lake for each domain. Inside each lake, create one zone for each team. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Direct each domain to manage their own lake’s data assets.
   • D1. Create one lake for each domain. Inside each lake, create one zone for each team. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Have the central data platform team manage all lakes’ data assets.

   Show answer

   Answer: C

   References

   • Google Cloud documentation

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4. Question 164 · 340 in bank

   dataset.inventory_vm sample records:

   

   You have an inventory of VM data stored in the BigQuery table. You want to prepare the data for regular reporting in the most cost-effective way. You need to exclude VM rows with fewer than 8 vCPU in your report. What should you do?

   • ACreate a view with a filter to drop rows with fewer than 8 vCPU, and use the UNNEST operator.
   • BCreate a materialized view with a filter to drop rows with fewer than 8 vCPU, and use the WITH common table expression.
   • CCreate a view with a filter to drop rows with fewer than 8 vCPU, and use the WITH common table expression.
   • DUse Dataflow to batch process and write the result to another BigQuery table.

   Show answer

   Answer: A

   Explanation

   - The table structure shows that the vCPU data is stored in a nested field within the components column. - Using the UNNEST operator to flatten the nested field and apply the filter.

   Source: Adapted from community discussion — raaad

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5. Question 165 · 340 in bank

   Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage. You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are: • The old data can be deleted anytime. • There is no predefined access pattern of the old data. • The old data should be available instantly when accessed. • There should not be any charges for data retrieval. What should you do to optimize for cost?

   • ACreate the bucket with the Autoclass storage class feature.
   • BCreate an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 90 days to coldline, and 365 days to archive storage class. Delete old data as needed.
   • CCreate an Object Lifecycle Management policy to modify the storage class for data older than 30 days to coldline, 90 days to nearline, and 365 days to archive storage class. Delete old data as needed.
   • DCreate an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 45 days to coldline, and 60 days to archive storage class. Delete old data as needed.

   Show answer

   Answer: A

   Explanation

   - Autoclass automatically moves objects between storage classes without impacting performance or availability, nor incurring retrieval costs. - It continuously optimizes storage costs based on access patterns without the need to set specific lifecycle management policies.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

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6. Question 166 · 340 in bank

   You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

   • AMake a call to the Stackdriver API to list all logs, and apply an advanced filter.
   • BIn the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
   • CIn the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
   • DUsing the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

   Show answer

   Answer: D

   Explanation

   I would choose D. A and B are wrong since don't notify anything to the monitoring tool. C has no filter on what will be notified. We want only some tables.

   Source: Adapted from community discussion — jvg637

   References

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

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7. Question 167 · 340 in bank

   Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

   • A1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type. 2. Load the booking and user profile data into a BigQuery table.
   • B1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type. 2. Load the booking and user profile data into a BigQuery table.
   • C1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking. 2. Create a policy tag with the email mask as the data masking rule. 3. Assign the policy to the email field in both tables. A 4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.
   • D1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking. 2. Create a policy tag with the default masking value as the data masking rule. 3. Assign the policy to the email field in both tables. 4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts

   Show answer

   Answer: B

   Explanation

   Format-preserving encryption (FPE) with FFX in Cloud DLP is a strong choice for de-identifying PII like email addresses. FPE maintains the format of the data and ensures that the same input results in the same encrypted output consistently. This means the email fields in both datasets can be encrypted to the same value, allowing for accurate joins in BigQuery while keeping the actual email addresses hidden.

   Source: Adapted from community discussion — lipa31

   References

   • Google Cloud documentation

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8. Question 168 · 340 in bank

   You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?

   • ASet a retention policy. Lock the retention policy.
   • BSet a retention policy. Set the default storage class to Archive for long-term digital preservation.
   • CEnable the Object Versioning feature. Add a lifecycle rule.
   • DEnable the Object Versioning feature. Create a copy in a bucket in a different region.

   Show answer

   Answer: A

   Explanation

   - Setting a retention policy on a Cloud Storage bucket prevents objects from being deleted for the duration of the retention period. - Locking the policy makes it immutable, meaning that the retention period cannot be reduced or removed, thus ensuring that the documents cannot be deleted or overwritten until the retention period expires.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

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9. Question 169 · 340 in bank

   You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service provider. You need to create a data model for customers, products, and subscriptions. All customers, products, and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to use the visualization layer for current and historical reporting. You need to ensure that the data model is simple, easy-to-use, and cost-effective. What should you do?

   • ACreate a normalized model with tables for each entity. Use snapshots before updates to track historical data.
   • BCreate a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data.
   • CCreate a denormalized model with nested and repeated fields. Update the table and use snapshots to track historical data.
   • DCreate a denormalized, append-only model with nested and repeated fields. Use the ingestion timestamp to track historical data.

   Show answer

   Answer: D

   Explanation

   - A denormalized, append-only model simplifies query complexity by eliminating the need for joins. - Adding data with an ingestion timestamp allows for easy retrieval of both current and historical states. - Instead of updating records, new records are appended, which maintains historical information without the need to create separate snapshots.

   Source: Adapted from community discussion — raaad

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10. Question 170 · 340 in bank

    You are deploying a batch pipeline in Dataflow. This pipeline reads data from Cloud Storage, transforms the data, and then writes the data into BigQuery. The security team has enabled an organizational constraint in Google Cloud, requiring all Compute Engine instances to use only internal IP addresses and no external IP addresses. What should you do?

    • AEnsure that your workers have network tags to access Cloud Storage and BigQuery. Use Dataflow with only internal IP addresses.
    • BEnsure that the firewall rules allow access to Cloud Storage and BigQuery. Use Dataflow with only internal IPs.
    • CCreate a VPC Service Controls perimeter that contains the VPC network and add Dataflow, Cloud Storage, and BigQuery as allowed services in the perimeter. Use Dataflow with only internal IP addresses.
    • DEnsure that Private Google Access is enabled in the subnetwork. Use Dataflow with only internal IP addresses.

    Show answer

    Answer: D

    Explanation

    - Private Google Access for services allows VM instances with only internal IP addresses in a VPC network or on-premises networks (via Cloud VPN or Cloud Interconnect) to reach Google APIs and services. - When you launch a Dataflow job, you can specify that it should use worker instances without external IP addresses if Private Google Access is enabled on the subnetwork where these instances are launched. - This way, your Dataflow workers will be able to access Cloud Storage and BigQuery without violating the organizational constraint of no external IPs.

    Source: Adapted from community discussion — raaad

    References

    • Google Cloud documentation
    • Google Cloud documentation

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PDE practice, question 161. 340 questions available.