You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

• A. Use Cloud Client Libraries to transfer the data over the internet.
• B. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
• C. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
• D. Use the gcloud storage command to transfer the data over the internet.

Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

• A. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
• B. Use authorized views to share query results with the payroll specialist.
• C. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
• D. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.

You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

• A. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
• B. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
• C. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
• D. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.

Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

• A. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
• B. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
• C. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
• D. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.

You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

• A. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
• B. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
• C. Use Cloud Composer to orchestrate the Spark job and email the report.
• D. Use Cloud Run functions to trigger the Spark job and email the report.