Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

• A. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
• B. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
• C. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
• D. Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I

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. Create a view that filters out rows that are older than one year.
• C. Require users to specify a partition filter using the alter table statement in SQL.
• D. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

• A. Compare the confusion matrix.
• B. Compare the two different models.
• C. Evaluate data drift.
• D. Evaluate the data skewness.

You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

• A. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
• B. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
• C. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
• D. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

• A. Set temporary object holds.
• B. Enable Object Versioning.
• C. Set a bucket retention policy.
• D. Change the bucket storage class to Archive.