You are using Workflows to call an API that returns a 1 KB JSON response, apply some complex business logic on this response, wait for the logic to complete, and then perform a load from a Cloud Storage file to BigQuery. The Workflows standard library does not have sufficient capabilities to perform your complex logic, and you want to use Python's standard library instead.
You want to optimize your workflow for simplicity and speed of execution. What should you do?

• A. Create a Cloud Composer environment and run the logic in Cloud Composer.
• B. Invoke a Cloud Function instance that uses Python to apply the logic on your JSON file.
• C. Invoke a subworkflow in Workflows to apply the logic on your JSON file.
• D. Create a Dataproc cluster, and use PySpark to apply the logic on your JSON file.

You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

• A. Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks.
• B. Log debug information in each ParDo function, and analyze the logs at execution time.
• C. Insert output sinks after each key processing step, and observe the writing throughput of each block.
• D. Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.

Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

• A. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/ allocated_for_project
• B. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
• C. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
• D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

You are migrating your data warehouse to Google Cloud and decommissioning your on-premises data center. Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud. The files being transferred are not large in number, but each file is 90 GB Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time. What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

• A. BigQuery Data Transfer Service for the migration, Pub/Sub and Dataproc for the real-time updates
• B. gsutil for the migration; Pub/Sub and Dataflow for the real-time updates
• C. gsutil for both the migration and the real-time updates
• D. Storage Transfer Service for the migration, Pub/Sub and Cloud Data Fusion for the real-time updates

Which methods can be used to reduce the number of rows processed by BigQuery?

• A. Splitting tables into multiple tables; putting data in partitions; using the LIMIT clause
• B. Putting data in partitions; using the LIMIT clause
• C. Splitting tables into multiple tables; putting data in partitions
• D. Splitting tables into multiple tables; using the LIMIT clause