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. Change the bucket storage class to Archive.
• D. Set a bucket retention policy.

Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

• A. Store the data in Cloud Storage using Archive storage.
• B. Store the data in Cloud Storage using Standard storage.
• C. Store the data in Cloud Storage using Nearline storage.
• D. Store the data in Cloud Storage using Coldline storage.

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 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.
• B. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
• C. Use authorized views to share query results with the payroll specialist.
• 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.

Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

• A. Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
• B. Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
• C. Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
• D. Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.

You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

• A. Use Cloud Data Fusion to visually design and manage the pipeline.
• B. Use Dataform to define the transformations in SQLX.
• C. Use Dataproc to run the transformations on a cluster.
• D. Use Dataflow with a custom template for the transformation logic.