You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

• A. Encrypt the data by using customer-managed encryption keys (CMEK).
• B. Update dataset privileges by using the SQL GRANT statement.
• C. Export the data to Cloud Storage, and use signed URLs to authorize access.
• D. Enable access control by using IAM roles.

Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

• A. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
• B. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
• C. Create a view that filters out rows that are older than one year.
• D. Require users to specify a partition filter using the alter table statement in SQL.

Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?

• A.
  
• B.
  
• C.
  
• D.
  

Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

• A. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
• B. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
• C. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
• D. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

• A. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
• B. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
• C. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
• D. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.