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  • Questions: 106 Q&A's With Detailed Explanation
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  • Exam Name: Google Cloud Associate Data Practitioner (ADP Exam)
  • Last Update: 02-Dec-2025
  • Questions and Answers: 106
  • Single Choice: 106 Q&A's

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Associate-Data-Practitioner Questions and Answers

Question # 1

You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach. What should you do?

A.

Create a partition by transaction date, and set the partition expiration policy to seven years.

B.

Set the table-level retention policy in BigQuery to seven years.

C.

Set the dataset-level retention policy in BigQuery to seven years.

D.

Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.

Question # 2

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 data. 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.

Use authorized views to share query results with the payroll specialist.

B.

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.

C.

Create a table with the aggregated performance data. Use table-level permissions to grant access to 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.

Question # 3

You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rowsand transactional data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A.

Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

B.

Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.

C.

Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

D.

Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

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