PDE practice questions 211–220

1. Question 211 · 340 in bank

   You are designing a messaging system by using Pub/Sub to process clickstream data with an event-driven consumer app that relies on a push subscription. You need to configure the messaging system that is reliable enough to handle temporary downtime of the consumer app. You also need the messaging system to store the input messages that cannot be consumed by the subscriber. The system needs to retry failed messages gradually, avoiding overloading the consumer app, and store the failed messages after a maximum of 10 retries in a topic. How should you configure the Pub/Sub subscription?

   • AIncrease the acknowledgement deadline to 10 minutes.
   • BUse immediate redelivery as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.
   • CUse exponential backoff as the subscription retry policy, and configure dead lettering to the same source topic with maximum delivery attempts set to 10.
   • DUse exponential backoff as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.

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   Answer: D

   Explanation

   - Exponential Backoff: This retry policy gradually increases the delay between retries, which helps to avoid overloading the consumer app. - Dead Lettering to a Different Topic: Configuring dead lettering sends messages that couldn't be processed after the specified number of delivery attempts (10 in this case) to a separate topic. This allows for handling of failed messages without interrupting the regular flow of new messages. - Maximum Delivery Attempts Set to 10: This setting ensures that the system retries each message up to 10 times before considering it a failure and moving it to the dead letter topic.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

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2. Question 212 · 340 in bank

   You designed a data warehouse in BigQuery to analyze sales data. You want a self-serving, low-maintenance, and cost- effective solution to share the sales dataset to other business units in your organization. What should you do?

   • ACreate an Analytics Hub private exchange, and publish the sales dataset.
   • BEnable the other business units’ projects to access the authorized views of the sales dataset.
   • CCreate and share views with the users in the other business units.
   • DUse the BigQuery Data Transfer Service to create a schedule that copies the sales dataset to the other business units’ projects.

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   Answer: A

   Explanation

   Analytics Hub offers a centralized platform for managing data sharing and access within the organization. This simplifies access control management.

   Source: Adapted from community discussion — raaad

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3. Question 213 · 340 in bank

   You have terabytes of customer behavioral data streaming from Google Analytics into BigQuery daily. Your customers’ information, such as their preferences, is hosted on a Cloud SQL for MySQL database. Your CRM database is hosted on a Cloud SQL for PostgreSQL instance. The marketing team wants to use your customers’ information from the two databases and the customer behavioral data to create marketing campaigns for yearly active customers. You need to ensure that the marketing team can run the campaigns over 100 times a day on typical days and up to 300 during sales. At the same time, you want to keep the load on the Cloud SQL databases to a minimum. What should you do?

   • ACreate BigQuery connections to both Cloud SQL databases. Use BigQuery federated queries on the two databases and the Google Analytics data on BigQuery to run these queries.
   • BCreate a job on Apache Spark with Dataproc Serverless to query both Cloud SQL databases and the Google Analytics data on BigQuery for these queries.
   • CCreate streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.
   • DCreate a Dataproc cluster with Trino to establish connections to both Cloud SQL databases and BigQuery, to execute the queries.

   Show answer

   Answer: C

   Explanation

   - Datastream: It's a fully managed, serverless service for real-time data replication. It allows to stream data from various sources, including Cloud SQL, into BigQuery. - Reduced Load on Cloud SQL: By replicating the required tables from both Cloud SQL databases into BigQuery, you minimize the load on the Cloud SQL instances. The marketing team's queries will be run against BigQuery, which is designed to handle high-volume analytics workloads. - Frequency of Queries: BigQuery can easily handle the high frequency of queries (100 times daily, up to 300 during sales events) due to its powerful data processing capabilities. - Combining Data Sources: Once the data is in BigQuery, you can efficiently combine it with the Google Analytics data for comprehensive analysis and campaign planning.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

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4. Question 214 · 340 in bank

   Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments. What should you do?

   • A1. Create a project for storage of the data for each of your departments. 2. Enable each department to create Cloud Storage buckets and BigQuery datasets. 3. Create user groups for authorized readers for each bucket and dataset. 4. Enable the IT team to administer the user groups to add or remove users as the departments’ request.
   • B1. Create multiple projects for storage of the data for each of your departments’ applications. 2. Enable each department to create Cloud Storage buckets and BigQuery datasets. 3. Publish the data that each department shared in Analytics Hub. 4. Enable all departments to discover and subscribe to the data they need in Analytics Hub.
   • C1. Create a project for storage of the data for your organization. 2. Create a central Cloud Storage bucket with three folders to store the files for each department. 3. Create a central BigQuery dataset with tables prefixed with the department name. 4. Give viewer rights for the storage project for the users of your departments.
   • D1. Create multiple projects for storage of the data for each of your departments’ applications. 2. Enable each department to create Cloud Storage buckets and BigQuery datasets. 3. In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones. 4. Enable each department to own and share the data of their data lakes.

   Show answer

   Answer: D

   Explanation

   - Decentralized ownership: Each department controls its data lake, aligning with the core principle of data ownership in a data mesh. - Self-service data access: Departments can create and manage their own Cloud Storage buckets and BigQuery datasets within their data lakes, enabling self-service data access. - Interdepartmental sharing: Dataplex facilitates data sharing by enabling departments to publish their data products from their data lakes, making it easily discoverable and usable by other departments.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 215 · 340 in bank

   You work for a large ecommerce company. You are using Pub/Sub to ingest the clickstream data to Google Cloud for analytics. You observe that when a new subscriber connects to an existing topic to analyze data, they are unable to subscribe to older data. For an upcoming yearly sale event in two months, you need a solution that, once implemented, will enable any new subscriber to read the last 30 days of data. What should you do?

   • ACreate a new topic, and publish the last 30 days of data each time a new subscriber connects to an existing topic.
   • BSet the topic retention policy to 30 days.
   • CSet the subscriber retention policy to 30 days.
   • DAsk the source system to re-push the data to Pub/Sub, and subscribe to it.

   Show answer

   Answer: B

   Explanation

   - Topic Retention Policy: This policy determines how long messages are retained by Pub/Sub after they are published, even if they have not been acknowledged (consumed) by any subscriber. - 30 Days Retention: By setting the retention policy of the topic to 30 days, all messages published to this topic will be available for consumption for 30 days. This means any new subscriber connecting to the topic can access and analyze data from the past 30 days.

   Source: Adapted from community discussion — raaad

   References

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

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6. Question 216 · 340 in bank

   You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow. The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines. You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?

   • AAssign the compute.networkUser role to the Dataflow service agent.
   • BAssign the compute.networkUser role to the service account that executes the Dataflow pipeline.
   • CAssign the dataflow.admin role to the Dataflow service agent.
   • DAssign the dataflow.admin role to the service account that executes the Dataflow pipeline.

   Show answer

   Answer: B

   Explanation

   I believe the answer is B. All authentication documentation points to Service Accounts. https://cloud.google.com/dataflow/docs/concepts/authentication#on-gcp Dataflow service agent typically manages general interactions with the Dataflow service but does not execute the actual jobs.

   Source: Adapted from community discussion — chrissamharris

   References

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

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7. Question 217 · 340 in bank

   Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network. You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on- premises. You want to store the data in BigQuery, with as minimal latency as possible. What should you do?

   • ASetup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.
   • BUse a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.
   • CUse Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.
   • DSetup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data to BigQuery.

   Show answer

   Answer: C

   Explanation

   but Option A introduces additional replication into Pub/Sub and the question states with minimal latency. In my opinion subscribing to Kafka via Dataflow has a lower latency than replicating the messages first to Pub/Sub and subscribing with Dataflow to it.

   Source: Adapted from community discussion — saschak94

   References

   • Google Cloud documentation

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8. Question 218 · 340 in bank

   You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage. The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

   • A1. Deploy a long-living Dataproc cluster with Apache Hive and Ranger enabled. 2. Configure Ranger for column level security. 3. Process with Dataproc Spark or Hive SQL.
   • B1. Define a BigLake table. 2. Create a taxonomy of policy tags in Data Catalog. 3. Add policy tags to columns. 4. Process with the Spark-BigQuery connector or BigQuery SQL.
   • C1. Load the data to BigQuery tables. 2. Create a taxonomy of policy tags in Data Catalog. 3. Add policy tags to columns. 4. Process with the Spark-BigQuery connector or BigQuery SQL.
   • D1. Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage. 2. Define a BigQuery external table for SQL processing. 3. Use Dataproc Spark to process the Cloud Storage files.

   Show answer

   Answer: B

   Explanation

   - BigLake Integration: BigLake allows you to define tables on top of data in Cloud Storage, providing a bridge between data lake storage and BigQuery's powerful analytics capabilities. This approach is cost-effective and scalable. - Data Catalog for Governance: Creating a taxonomy of policy tags in Google Cloud's Data Catalog and applying these tags to specific columns in your BigLake tables enables fine-grained, column-level access control. - Processing with Spark and SQL: The Spark-BigQuery connector allows data scientists to process data using Apache Spark directly against BigQuery (and BigLake tables). This supports both Spark and SQL processing needs. - Scalability into a Data Mesh: BigLake and Data Catalog are designed to scale and support the data mesh architecture, which involves decentralized data ownership and governance.

   Source: Adapted from community discussion — raaad

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9. Question 219 · 340 in bank

   One of your encryption keys stored in Cloud Key Management Service (Cloud KMS) was exposed. You need to re- encrypt all of your CMEK-protected Cloud Storage data that used that key, and then delete the compromised key. You also want to reduce the risk of objects getting written without customer-managed encryption key (CMEK) protection in the future. What should you do?

   • ARotate the Cloud KMS key version. Continue to use the same Cloud Storage bucket.
   • BCreate a new Cloud KMS key. Set the default CMEK key on the existing Cloud Storage bucket to the new one.
   • CCreate a new Cloud KMS key. Create a new Cloud Storage bucket. Copy all objects from the old bucket to the new one bucket while specifying the new Cloud KMS key in the copy command.
   • DCreate a new Cloud KMS key. Create a new Cloud Storage bucket configured to use the new key as the default CMEK key. Copy all objects from the old bucket to the new bucket without specifying a key.

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   Answer: D

   Explanation

   - New Key Creation: A new Cloud KMS key ensures a secure replacement for the compromised one. - New Bucket: A separate bucket prevents potential conflicts with existing objects and configurations. - Default CMEK: Setting the new key as default enforces encryption for all objects in the bucket, reducing the risk of unencrypted data. - Copy Without Key Specification: Copying objects without specifying a key leverages the default key, simplifying the process and ensuring consistent encryption. - Old Key Deletion: After copying, the compromised key can be safely deleted.

   Source: Adapted from community discussion — raaad

   References

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

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10. Question 220 · 340 in bank

    You have an upstream process that writes data to Cloud Storage. This data is then read by an Apache Spark job that runs on Dataproc. These jobs are run in the us-central1 region, but the data could be stored anywhere in the United States. You need to have a recovery process in place in case of a catastrophic single region failure. You need an approach with a maximum of 15 minutes of data loss (RPO=15 mins). You want to ensure that there is minimal latency when reading the data. What should you do?

    • A1. Create two regional Cloud Storage buckets, one in the us-central1 region and one in the us-south1 region. 2. Have the upstream process write data to the us-central1 bucket. Use the Storage Transfer Service to copy data hourly from the us-central1 bucket to the us-south1 bucket. 3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in that region. 4. In case of regional failure, redeploy your Dataproc clusters to the us-south1 region and read from the bucket in that region instead.
    • B1. Create a Cloud Storage bucket in the US multi-region. 2. Run the Dataproc cluster in a zone in the us-central1 region, reading data from the US multi-region bucket. 3. In case of a regional failure, redeploy the Dataproc cluster to the us-central2 region and continue reading from the same bucket.
    • C1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions. 2. Enable turbo replication. 3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the us-south1 region. 4. In case of a regional failure, redeploy your Dataproc cluster to the us-south1 region and continue reading from the same bucket.
    • D1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions. 2. Enable turbo replication. 3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the same region. 4. In case of a regional failure, redeploy the Dataproc clusters to the us-south1 region and read from the same bucket.

    Show answer

    Answer: D

    Explanation

    - Rapid Replication: Turbo replication ensures near-real-time data synchronization between regions, achieving an RPO of 15 minutes or less. - Minimal Latency: Dataproc clusters can read from the bucket in the same region, minimizing data transfer latency and optimizing performance. - Disaster Recovery: In case of regional failure, Dataproc clusters can seamlessly redeploy to the other region and continue reading from the same bucket, ensuring business continuity.

    Source: Adapted from community discussion — raaad

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PDE practice, question 211. 340 questions available.