PDE practice questions 191–200

1. Question 191 · 340 in bank

   You have a BigQuery dataset named “customers”. All tables will be tagged by using a Data Catalog tag template named “gdpr”. The template contains one mandatory field, “has_sensitive_data”, with a boolean value. All employees must be able to do a simple search and find tables in the dataset that have either true or false in the “has_sensitive_data’ field. However, only the Human Resources (HR) group should be able to see the data inside the tables for which “has_sensitive data” is true. You give the all employees group the bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize configuration overhead. What should you do next?

   • ACreate the “gdpr” tag template with private visibility. Assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
   • BCreate the “gdpr” tag template with private visibility. Assign the datacatalog.tagTemplateViewer role on this tag to the all employees group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
   • CCreate the “gdpr” tag template with public visibility. Assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
   • DCreate the “gdpr” tag template with public visibility. Assign the datacatalog.tagTemplateViewer role on this tag to the all employees group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

   Show answer

   Answer: C

   Explanation

   - The most straightforward solution with minimal configuration overhead. - By creating the "gdpr" tag template with public visibility, you ensure that all employees can search and find tables based on the "has_sensitive_data" field. - Assigning the bigquery.dataViewer role to the HR group on tables with sensitive data ensures that only they can view the actual data in these tables.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

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2. Question 192 · 340 in bank

   You are creating the CI/CD cycle for the code of the directed acyclic graphs (DAGs) running in Cloud Composer. Your team has two Cloud Composer instances: one instance for development and another instance for production. Your team is using a Git repository to maintain and develop the code of the DAGs. You want to deploy the DAGs automatically to Cloud Composer when a certain tag is pushed to the Git repository. What should you do?

   • A1. Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing. 2. If the tests pass, use Cloud Build to copy the code to the bucket of the production instance.
   • B1. Use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the code to the Google Kubernetes Engine (GKE) cluster of the development instance for testing. 2. If the tests pass, use the KubernetesPodOperator to deploy the container to the GKE cluster of the production instance.
   • C1. Use Cloud Build to build a container and the KubernetesPodOperator to deploy the code of the DAG to the Google Kubernetes Engine (GKE) cluster of the development instance for testing. 2. If the tests pass, copy the code to the Cloud Storage bucket of the production instance.
   • D1. Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing. 2. If the tests pass, use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the container to the Google Kubernetes Engine (GKE) cluster of the production instance.

   Show answer

   Answer: A

   Explanation

   The Answer is A. Given that there are two instances (development and production) already available, and the goal is to deploy DAGs to Cloud Composer not entire composer infra build. Explanation: - This approach leverages Cloud Build to manage the deployment process. - It first deploys the code to the Cloud Storage bucket of the development instance for testing purposes. - If the tests are successful in the development environment, the same Cloud Build process is used to copy the code to the Cloud Storage bucket of the production instance. B. GKE-based approach is not standard for Cloud Composer. C. GKE used for testing is unconventional for DAG deployments. D. Involves unnecessary GKE deployment for production. Testing DAGs should use Composer instances directly, not Kubernetes containers in GKE.

   Source: Adapted from community discussion — BIGQUERY_ALT_ALT

   References

   • Google Cloud documentation

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3. Question 193 · 340 in bank

   You have a BigQuery table that ingests data directly from a Pub/Sub subscription. The ingested data is encrypted with a Google-managed encryption key. You need to meet a new organization policy that requires you to use keys from a centralized Cloud Key Management Service (Cloud KMS) project to encrypt data at rest. What should you do?

   • AUse Cloud KMS encryption key with Dataflow to ingest the existing Pub/Sub subscription to the existing BigQuery table.
   • BCreate a new BigQuery table by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.
   • CCreate a new Pub/Sub topic with CMEK and use the existing BigQuery table by using Google-managed encryption key.
   • DCreate a new BigQuery table and Pub/Sub topic by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.

   Show answer

   Answer: B

   Explanation

   - New BigQuery Table with CMEK: This option involves creating a new BigQuery table configured to use a CMEK from Cloud KMS. It directly addresses the need to use a CMEK for data at rest in BigQuery. - Migrate Data: Migrating data from the old table (encrypted with a Google-managed key) to the new table (encrypted with CMEK) ensures that all existing data complies with the new policy.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

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4. Question 194 · 340 in bank

   You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on-premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution. What should you do?

   • AImport the ORC files to Bigtable tables for the data scientist team.
   • BImport the ORC files to BigQuery tables for the data scientist team.
   • CCopy the ORC files on Cloud Storage, then deploy a Dataproc cluster for the data scientist team.
   • DCopy the ORC files on Cloud Storage, then create external BigQuery tables for the data scientist team.

   Show answer

   Answer: D

   Explanation

   - It leverages the strengths of BigQuery for SQL-based exploration while avoiding additional costs and complexity associated with data transformation or migration. - The data remains in ORC format in Cloud Storage, and BigQuery's external tables feature allows direct querying of this data.

   Source: Adapted from community discussion — raaad

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5. Question 195 · 340 in bank

   You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?

   • ASubmit duplicate pipelines in two different zones by using the --zone flag.
   • BSet the pipeline staging location as a regional Cloud Storage bucket.
   • CSpecify a worker region by using the --region flag.
   • DCreate an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.

   Show answer

   Answer: C

   Explanation

   - Specifying a worker region (instead of a specific zone) allows Google Cloud's Dataflow service to manage the distribution of resources across multiple zones within that region

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

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6. Question 196 · 340 in bank

   You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub/Sub, processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?

   • AGroup the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore.
   • BGroup the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore.
   • CGroup the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
   • DGroup the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.

   Show answer

   Answer: B

   Explanation

   - Hopping Window: Hopping windows are fixed-sized, overlapping intervals. - Aggregate data over the last 30 seconds, every 2 seconds, as hopping windows allow for overlapping data analysis. - Memorystore: Ideal for low-latency access required for real-time visualization and analysis.

   Source: Adapted from community discussion — raaad

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7. Question 197 · 340 in bank

   Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job, that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment to Pub/Sub, applies some custom business logic in a DoFn instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?

   • AEnable retaining of acknowledged messages in your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the subscription/num_retained_acked_messages metric on this subscription.
   • BUse an exception handling block in your Dataflow’s DoFn code to push the messages that failed to be transformed through a side output and to a new Pub/Sub topic. Use Cloud Monitoring to monitor the topic/num_unacked_messages_by_region metric on this new topic.
   • CEnable dead lettering in your Pub/Sub pull subscription, and specify a new Pub/Sub topic as the dead letter topic. Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.
   • DCreate a snapshot of your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the snapshot/num_messages metric on this snapshot.

   Show answer

   Answer: B

   Explanation

   - Exception Handling in DoFn: Implementing an exception handling block within DoFn in Dataflow to catch failures during processing is a direct way to manage errors. - Side Output to New Topic: Using a side output to redirect failed messages to a new Pub/Sub topic is an effective way to isolate and manage these messages. - Monitoring: Monitoring the num_unacked_messages_by_region on the new topic can alert you to the presence of failed messages.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation
   • Google Cloud documentation

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8. Question 198 · 340 in bank

   You want to store your team’s shared tables in a single dataset to make data easily accessible to various analysts. You want to make this data readable but unmodifiable by analysts. At the same time, you want to provide the analysts with individual workspaces in the same project, where they can create and store tables for their own use, without the tables being accessible by other analysts. What should you do?

   • AGive analysts the BigQuery Data Viewer role at the project level. Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.
   • BGive analysts the BigQuery Data Viewer role at the project level. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.
   • CGive analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset.
   • DGive analysts the BigQuery Data Viewer role on the shared dataset. Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.

   Show answer

   Answer: C

   Explanation

   - Data Viewer on Shared Dataset: Grants read-only access to the shared dataset. - Data Editor on Individual Datasets: Giving each analyst Data Editor role on their respective dataset creates private workspaces where they can create and store personal tables without exposing them to other analysts.

   Source: Adapted from community discussion — raaad

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9. Question 199 · 340 in bank

   Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.

   

   You want to improve the performance of this data read. What should you do?

   • ASpecify the TableReference object in the code.
   • BUse .fromQuery operation to read specific fields from the table.
   • CUse of both the Google BigQuery TableSchema and TableFieldSchema classes.
   • DCall a transform that returns TableRow objects, where each element in the PCollection represents a single row in the table.

   Show answer

   Answer: B

   Explanation

   B BigQueryIO.read.fromQuery() executes a query and then reads the results received after the query execution. Therefore, this function is more time-consuming, given that it requires that a query is first executed (which will incur in the corresponding economic and computational costs).

   Source: Adapted from community discussion — arthur2385

   References

   • Google Cloud documentation

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10. Question 200 · 340 in bank

    You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window. What should you do?

    • AUse watermarks to define the expected data arrival window. Allow late data as it arrives.
    • BChange your windowing function to tumbling windows to avoid overlapping window periods.
    • CChange your windowing function to session windows to define your windows based on certain activity.
    • DExpand your hopping window so that the late data has more time to arrive within the grouping.

    Show answer

    Answer: A

    Explanation

    - Watermarks: Watermarks in a streaming pipeline are used to specify the point in time when Dataflow expects all data up to that point to have arrived. - Allow Late Data: configure the pipeline to accept and correctly process data that arrives after the watermark, ensuring it's captured in the appropriate window.

    Source: Adapted from community discussion — raaad

    References

    • Google Cloud documentation

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PDE practice, question 191. 340 questions available.