PDE practice questions 151–160

1. Question 151 · 340 in bank

   You want to schedule a number of sequential load and transformation jobs. Data files will be added to a Cloud Storage bucket by an upstream process. There is no fixed schedule for when the new data arrives. Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery. The transformation jobs are different for every table. These jobs might take hours to complete. You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

   • A1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators. 2. Use a single shared DAG for all tables that need to go through the pipeline. 3. Schedule the DAG to run hourly.
   • B1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators. 2. Create a separate DAG for each table that needs to go through the pipeline. 3. Schedule the DAGs to run hourly.
   • C1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators. 2. Use a single shared DAG for all tables that need to go through the pipeline. 3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG.
   • D1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators. 2. Create a separate DAG for each table that needs to go through the pipeline. 3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG.

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   Answer: D

   Explanation

   D * Transformations are in Dataproc and BigQuery. So you don't need operators for GCS (A and B can be discard) * "There is no fixed schedule for when the new data arrives." so you trigger the DAG when a file arrives * "The transformation jobs are different for every table. " so you need a DAG for each table. Then, D is the most suitable answer

   Source: Adapted from community discussion — cuadradobertolinisebastiancami

   References

   • Google Cloud documentation

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2. Question 152 · 340 in bank

   You are deploying a MySQL database workload onto Cloud SQL. The database must be able to scale up to support several readers from various geographic regions. The database must be highly available and meet low RTO and RPO requirements, even in the event of a regional outage. You need to ensure that interruptions to the readers are minimal during a database failover. What should you do?

   • ACreate a highly available Cloud SQL instance in region Create a highly available read replica in region B. Scale up read workloads by creating cascading read replicas in multiple regions. Backup the Cloud SQL instances to a multi-regional Cloud Storage bucket. Restore the Cloud SQL backup to a new instance in another region when Region A is down.
   • BCreate a highly available Cloud SQL instance in region A. Scale up read workloads by creating read replicas in multiple regions. Promote one of the read replicas when region A is down.
   • CCreate a highly available Cloud SQL instance in region A. Create a highly available read replica in region B. Scale up read workloads by creating cascading read replicas in multiple regions. Promote the read replica in region B when region A is down.
   • DCreate a highly available Cloud SQL instance in region A. Scale up read workloads by creating read replicas in the same region. Failover to the standby Cloud SQL instance when the primary instance fails.

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   Answer: C

   Explanation

   Option C: Because HA read replica in multiple regions. NotA: Coz restore from back up is time taking NotB: No HA in Multiple regions read replica Not D: Only one region mentioned.

   Source: Adapted from community discussion — rohan.sahi

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3. Question 153 · 340 in bank

   You are planning to load some of your existing on-premises data into BigQuery on Google Cloud. You want to either stream or batch-load data, depending on your use case. Additionally, you want to mask some sensitive data before loading into BigQuery. You need to do this in a programmatic way while keeping costs to a minimum. What should you do?

   • AUse Cloud Data Fusion to design your pipeline, use the Cloud DLP plug-in to de-identify data within your pipeline, and then move the data into BigQuery.
   • BUse the BigQuery Data Transfer Service to schedule your migration. After the data is populated in BigQuery, use the connection to the Cloud Data Loss Prevention (Cloud DLP) API to de-identify the necessary data.
   • CCreate your pipeline with Dataflow through the Apache Beam SDK for Python, customizing separate options within your code for streaming, batch processing, and Cloud DLP. Select BigQuery as your data sink.
   • DSet up Datastream to replicate your on-premise data on BigQuery.

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   Answer: C

   Explanation

   - Programmatic Flexibility: Apache Beam provides extensive control over pipeline design, allowing for customization of data transformations, including integration with Cloud DLP for sensitive data masking. - Streaming and Batch Support: Beam seamlessly supports both streaming and batch data processing modes, enabling flexibility in data loading patterns. - Cost-Effective Processing: Dataflow offers a serverless model, scaling resources as needed, and only charging for resources used, helping optimize costs. - Integration with Cloud DLP: Beam integrates well with Cloud DLP for sensitive data masking, ensuring data privacy before loading into BigQuery.

   Source: Adapted from community discussion — raaad

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4. Question 154 · 340 in bank

   You want to encrypt the customer data stored in BigQuery. You need to implement per-user crypto-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?

   • AImplement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.
   • BCreate a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.
   • CCreate a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.
   • DEncrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.

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   Answer: A

   Explanation

   - AEAD cryptographic functions in BigQuery allow for encryption and decryption of data at the column level. - You can encrypt specific data fields using a unique key per user and manage these keys outside of BigQuery (for example, in your application or using a key management system). - By "deleting" or revoking access to the key for a specific user, you effectively make their data unreadable, achieving crypto-deletion. - This method provides fine-grained encryption control but requires careful key management and integration with your applications.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 155 · 340 in bank

   The data analyst team at your company uses BigQuery for ad-hoc queries and scheduled SQL pipelines in a Google Cloud project with a slot reservation of 2000 slots. However, with the recent introduction of hundreds of new non time-sensitive SQL pipelines, the team is encountering frequent quota errors. You examine the logs and notice that approximately 1500 queries are being triggered concurrently during peak time. You need to resolve the concurrency issue. What should you do?

   • AIncrease the slot capacity of the project with baseline as 0 and maximum reservation size as 3000.
   • BUpdate SQL pipelines to run as a batch query, and run ad-hoc queries as interactive query jobs.
   • CIncrease the slot capacity of the project with baseline as 2000 and maximum reservation size as 3000.
   • DUpdate SQL pipelines and ad-hoc queries to run as interactive query jobs.

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   Answer: B

   Explanation

   - BigQuery allows you to specify job priority as either BATCH or INTERACTIVE. - Batch queries are queued and then started when idle resources are available, making them suitable for non-time-sensitive workloads. - Running ad-hoc queries as interactive ensures they have prompt access to resources.

   Source: Adapted from community discussion — raaad

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6. Question 156 · 340 in bank

   You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

   • ADelete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
   • BAdd a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.
   • CCreate a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
   • DAdd two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
   • EConstruct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

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   Answer: E

   Explanation

   "E" looks better. For D, the database will be double in size (which increases the storage price) and the user has to spend some more days reloading all the data.

   Source: Adapted from community discussion — jvg637

   References

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

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7. Question 157 · 340 in bank

   You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups: • Data engineers, which require full data lake access • Analytic users, which require access to curated data You need to assign access rights to these two groups. What should you do?

   • A1. Grant the dataplex.dataOwner role to the data engineer group on the customer data lake. 2. Grant the dataplex.dataReader role to the analytic user group on the customer curated zone.
   • B1. Grant the dataplex.dataReader role to the data engineer group on the customer data lake. 2. Grant the dataplex.dataOwner to the analytic user group on the customer curated zone.
   • C1. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectCreator role on Cloud Storage buckets to data engineers. 2. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to analytic users.
   • D1. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to data engineers. 2. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectEditor role on Cloud Storage buckets to analytic users.

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   Answer: A

   Explanation

   - dataplex.dataOwner: Grants full control over data assets, including reading, writing, managing, and granting access to others. - dataplex.dataReader: Allows users to read data but not modify it.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

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8. Question 158 · 340 in bank

   You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write the data to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?

   • AAdopt multi-regional Cloud Storage buckets in your architecture.
   • BAdopt two regional Cloud Storage buckets, and update your application to write the output on both buckets.
   • CAdopt a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.
   • DAdopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.

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   Answer: C

   Explanation

   - Dual-region buckets are a specific type of storage that automatically replicates data between two geographically distinct regions. - Turbo replication is an enhanced feature that provides faster replication between the two regions, thus minimizing RPO. - This option ensures that your data is resilient to regional failures and is replicated quickly, meeting the needs for low RPO and no impact on application performance.

   Source: Adapted from community discussion — raaad

   References

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

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9. Question 159 · 340 in bank

   You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

   • A1. Use a dual-region Cloud Storage bucket. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. 3. Seek the subscription back in time by 15 minutes to recover the acknowledged messages. 4. Start the Dataflow job in a secondary region.
   • B1. Use a multi-regional Cloud Storage bucket. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. 3. Seek the subscription back in time by 60 minutes to recover the acknowledged messages. 4. Start the Dataflow job in a secondary region.
   • C1. Use a regional Cloud Storage bucket. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. 3. Seek the subscription back in time by one day to recover the acknowledged messages. 4. Start the Dataflow job in a secondary region and write in a bucket in the same region.
   • D1. Use a dual-region Cloud Storage bucket with turbo replication enabled. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. 3. Seek the subscription back in time by 60 minutes to recover the acknowledged messages. 4. Start the Dataflow job in a secondary region.

   Show answer

   Answer: D

   Explanation

   D. 1. Use a dual-region Cloud Storage bucket with turbo replication enabled. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. 3. Seek the subscription back in time by 60 minutes to recover the acknowledged messages. 4. Start the Dataflow job in a secondary region. RPO of 15 minutes is guaranteed when turbo replication is used https://cloud.google.com/storage/docs/availability-durability

   Source: Adapted from community discussion — datapassionate

   References

   • Google Cloud documentation

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10. Question 160 · 340 in bank

    Different teams in your organization store customer and performance data in BigQuery. Each team needs to keep full control of their collected data, be able to query data within their projects, and be able to exchange their data with other teams. You need to implement an organization-wide solution, while minimizing operational tasks and costs. What should you do?

    • AAsk each team to create authorized views of their data. Grant the biquery.jobUser role to each team.
    • BCreate a BigQuery scheduled query to replicate all customer data into team projects.
    • CAsk each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.
    • DEnable each team to create materialized views of the data they need to access in their projects.

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    Answer: C

    Explanation

    - Analytics Hub allows organizations to create and manage exchanges where producers can publish their data and consumers can discover and subscribe to data products. - Asking each team to publish their data in Analytics Hub and having other teams subscribe to them is a scalable and controlled way of sharing data. - It minimizes operational tasks because data doesn't need to be duplicated or manually managed after setup, and teams can maintain full control over their datasets.

    Source: Adapted from community discussion — raaad

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PDE practice, question 151. 340 questions available.