Google Associate Data Practitioner Practice Exams
Last updated on Oct 22,2026- Exam Code: Associate Data Practitioner
- Exam Name: Google Cloud Associate Data Practitioner ( ADP Exam )
- Certification Provider: Google
- Latest update: Oct 22,2026
Question #81
You are responsible for managing Cloud Storage buckets for a research company. Your company has well-defined data tiering and retention rules. You need to optimize storage costs while achieving your data retention needs.
What should you do?
- A . Configure the buckets to use the Archive storage class.
- B . Configure a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age.
- C . Configure the buckets to use the Standard storage class and enable Object Versioning.
- D . Configure the buckets to use the Autoclass feature.
Correct Answer: B
B
Explanation:
Configuring a lifecycle management policy on each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well-defined data tiering and retention needs, providing cost efficiency and automation.
Extract from Google Documentation: From "Object Lifecycle Management"
(https://cloud.google.com/storage/docs/lifecycle): "Use lifecycle management policies to automatically transition objects to lower-cost storage classes (e.g., Nearline, Coldline) and delete them based on age, optimizing costs according to your specific tiering and retention requirements."
Reference: Google Cloud Documentation – "Cloud Storage Lifecycle" (https://cloud.google.com/storage/docs/lifecycle).
B
Explanation:
Configuring a lifecycle management policy on each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well-defined data tiering and retention needs, providing cost efficiency and automation.
Extract from Google Documentation: From "Object Lifecycle Management"
(https://cloud.google.com/storage/docs/lifecycle): "Use lifecycle management policies to automatically transition objects to lower-cost storage classes (e.g., Nearline, Coldline) and delete them based on age, optimizing costs according to your specific tiering and retention requirements."
Reference: Google Cloud Documentation – "Cloud Storage Lifecycle" (https://cloud.google.com/storage/docs/lifecycle).
Question #82
You have a BigQuery dataset containing sales data. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum.
What should you do?
- A . Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
- B . Partition a BigQuery table by month. After 6 months, export the data to Coldline storage.
Implement a lifecycle policy to delete the data from Cloud Storage after 3 years. - C . Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
- D . Store all data in a single BigQuery table without partitioning or lifecycle policies.
Correct Answer: B
B
Explanation:
Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data to Coldline storage minimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.
B
Explanation:
Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data to Coldline storage minimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.
Question #83
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive data.
What should you do?
- A . Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
- B . Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
- C . Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
- D . Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
Correct Answer: B
B
Explanation:
Creating a materialized view with the limited data in a new dataset and granting the data analyst the BigQuery Data Viewer role on the dataset and the BigQuery Job User role in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.
B
Explanation:
Creating a materialized view with the limited data in a new dataset and granting the data analyst the BigQuery Data Viewer role on the dataset and the BigQuery Job User role in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.
Question #84
Your organization’s website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime.
What should you do?
- A . Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
- B . Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
- C . Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
- D . Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.
Correct Answer: B
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:Database Migration Service (DMS) is designed for migrating databases to Cloud SQL with minimal downtime and administrative overhead.
Cloud SQL for MySQL is a fully managed MySQL service, which aligns with the requirement to minimize administrative overhead.
Why other options are incorrect:A: Installing MySQL on Compute Engine requires manual management of the database instance, which increases administrative overhead.
C: BigQuery is not a direct replacement for a relational MySQL database. It’s an analytical data warehouse.
D: Spanner is a globally distributed, scalable database, but it requires schema conversion and is not a direct replacement for MySQL, and it is also much more complex than cloud SQL.
Reference: Database Migration Service: https://cloud.google.com/database-migration
Cloud SQL for MySQL: https://cloud.google.com/sql/docs/mysql
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:Database Migration Service (DMS) is designed for migrating databases to Cloud SQL with minimal downtime and administrative overhead.
Cloud SQL for MySQL is a fully managed MySQL service, which aligns with the requirement to minimize administrative overhead.
Why other options are incorrect:A: Installing MySQL on Compute Engine requires manual management of the database instance, which increases administrative overhead.
C: BigQuery is not a direct replacement for a relational MySQL database. It’s an analytical data warehouse.
D: Spanner is a globally distributed, scalable database, but it requires schema conversion and is not a direct replacement for MySQL, and it is also much more complex than cloud SQL.
Reference: Database Migration Service: https://cloud.google.com/database-migration
Cloud SQL for MySQL: https://cloud.google.com/sql/docs/mysql
Question #85
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery.
What should you do?
- A . Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
- B . Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
- C . Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
- D . Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
Correct Answer: C
C
Explanation:
Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.
C
Explanation:
Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.
Question #86
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost-effective, fully-managed, serverless solution to build ELT processes with SQL pipelines. Your solution must include source code control, environment parameterization, and data quality checks.
What should you do?
- A . Use Cloud Data Fusion to visually design and manage the pipelines.
- B . Use Dataform to build, orchestrate, and monitor the pipelines.
- C . Use Dataproc to run MapReduce jobs for distributed data processing.
- D . Use Cloud Composer to orchestrate and run data workflows.
Correct Answer: B
B
Explanation:
Comprehensive and Detailed In-Depth
The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let’s dive in:
Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn’t fully serverless (requires instance management). It lacks native source code control and parameterization.
Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality―all meeting the requirements cost-effectively.
Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.
Option D: Cloud Composer orchestrates workflows (Python DAGs), not SQL pipelines natively. It’s
managed but not optimized for ELT within BigQuery alone.
Why B is Best: Dataform leverages your team’s SQL skills, runs in BigQuery (no extra infrastructure), and provides Git integration (e.g., GitHub), parameterization (e.g., DECLARE env STRING DEFAULT ‘prod’;), and quality checks (e.g., assert ‘no_nulls’ AS SELECT COUNT(*) FROM table WHERE col IS NULL). It’s the perfect fit.
Extract from Google Documentation: From "Dataform Overview"
(https://cloud.google.com/dataform/docs): "Dataform is a fully managed, serverless solution for building SQL-based ELT pipelines in BigQuery, with built-in Git version control, environment parameterization, and data quality assertions for robust data warehouse management."
Reference: Google Cloud Documentation – "Dataform" (https://cloud.google.com/dataform).
B
Explanation:
Comprehensive and Detailed In-Depth
The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let’s dive in:
Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn’t fully serverless (requires instance management). It lacks native source code control and parameterization.
Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality―all meeting the requirements cost-effectively.
Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.
Option D: Cloud Composer orchestrates workflows (Python DAGs), not SQL pipelines natively. It’s
managed but not optimized for ELT within BigQuery alone.
Why B is Best: Dataform leverages your team’s SQL skills, runs in BigQuery (no extra infrastructure), and provides Git integration (e.g., GitHub), parameterization (e.g., DECLARE env STRING DEFAULT ‘prod’;), and quality checks (e.g., assert ‘no_nulls’ AS SELECT COUNT(*) FROM table WHERE col IS NULL). It’s the perfect fit.
Extract from Google Documentation: From "Dataform Overview"
(https://cloud.google.com/dataform/docs): "Dataform is a fully managed, serverless solution for building SQL-based ELT pipelines in BigQuery, with built-in Git version control, environment parameterization, and data quality assertions for robust data warehouse management."
Reference: Google Cloud Documentation – "Dataform" (https://cloud.google.com/dataform).
Question #86
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost-effective, fully-managed, serverless solution to build ELT processes with SQL pipelines. Your solution must include source code control, environment parameterization, and data quality checks.
What should you do?
- A . Use Cloud Data Fusion to visually design and manage the pipelines.
- B . Use Dataform to build, orchestrate, and monitor the pipelines.
- C . Use Dataproc to run MapReduce jobs for distributed data processing.
- D . Use Cloud Composer to orchestrate and run data workflows.
Correct Answer: B
B
Explanation:
Comprehensive and Detailed In-Depth
The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let’s dive in:
Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn’t fully serverless (requires instance management). It lacks native source code control and parameterization.
Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality―all meeting the requirements cost-effectively.
Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.
Option D: Cloud Composer orchestrates workflows (Python DAGs), not SQL pipelines natively. It’s
managed but not optimized for ELT within BigQuery alone.
Why B is Best: Dataform leverages your team’s SQL skills, runs in BigQuery (no extra infrastructure), and provides Git integration (e.g., GitHub), parameterization (e.g., DECLARE env STRING DEFAULT ‘prod’;), and quality checks (e.g., assert ‘no_nulls’ AS SELECT COUNT(*) FROM table WHERE col IS NULL). It’s the perfect fit.
Extract from Google Documentation: From "Dataform Overview"
(https://cloud.google.com/dataform/docs): "Dataform is a fully managed, serverless solution for building SQL-based ELT pipelines in BigQuery, with built-in Git version control, environment parameterization, and data quality assertions for robust data warehouse management."
Reference: Google Cloud Documentation – "Dataform" (https://cloud.google.com/dataform).
B
Explanation:
Comprehensive and Detailed In-Depth
The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let’s dive in:
Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn’t fully serverless (requires instance management). It lacks native source code control and parameterization.
Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality―all meeting the requirements cost-effectively.
Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.
Option D: Cloud Composer orchestrates workflows (Python DAGs), not SQL pipelines natively. It’s
managed but not optimized for ELT within BigQuery alone.
Why B is Best: Dataform leverages your team’s SQL skills, runs in BigQuery (no extra infrastructure), and provides Git integration (e.g., GitHub), parameterization (e.g., DECLARE env STRING DEFAULT ‘prod’;), and quality checks (e.g., assert ‘no_nulls’ AS SELECT COUNT(*) FROM table WHERE col IS NULL). It’s the perfect fit.
Extract from Google Documentation: From "Dataform Overview"
(https://cloud.google.com/dataform/docs): "Dataform is a fully managed, serverless solution for building SQL-based ELT pipelines in BigQuery, with built-in Git version control, environment parameterization, and data quality assertions for robust data warehouse management."
Reference: Google Cloud Documentation – "Dataform" (https://cloud.google.com/dataform).
Question #88
Your organization plans to move their on-premises environment to Google Cloud. Your organization’s network bandwidth is less than 1 Gbps. You need to move over 500 ТВ of data to Cloud Storage securely, and only have a few days to move the data.
What should you do?
- A . Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
- B . Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
- C . Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
- D . Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
Correct Answer: A
A
Explanation:
Using Transfer Appliances is the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.
A
Explanation:
Using Transfer Appliances is the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.
Question #89
You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region.
What should you do?
- A . Add a policy tag in BigQuery.
- B . Create a row-level access policy.
- C . Create a data masking rule.
- D . Grant the appropriate 1AM permissions on the dataset.
Correct Answer: B
B
Explanation:
Creating a row-level access policy in BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative’s region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.
Extract from Google Documentation: From "Row-Level Security in BigQuery"
(https://cloud.google.com/bigquery/docs/row-level-security): "Row-level access policies let you restrict access to specific rows in a table based on a filter condition, such as a user’s region, providing fine-grained control over data visibility without creating separate tables or views."
Reference: Google Cloud Documentation – "BigQuery Row-Level Security"
(https://cloud.google.com/bigquery/docs/row-level-security).
B
Explanation:
Creating a row-level access policy in BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative’s region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.
Extract from Google Documentation: From "Row-Level Security in BigQuery"
(https://cloud.google.com/bigquery/docs/row-level-security): "Row-level access policies let you restrict access to specific rows in a table based on a filter condition, such as a user’s region, providing fine-grained control over data visibility without creating separate tables or views."
Reference: Google Cloud Documentation – "BigQuery Row-Level Security"
(https://cloud.google.com/bigquery/docs/row-level-security).
Question #89
You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region.
What should you do?
- A . Add a policy tag in BigQuery.
- B . Create a row-level access policy.
- C . Create a data masking rule.
- D . Grant the appropriate 1AM permissions on the dataset.
Correct Answer: B
B
Explanation:
Creating a row-level access policy in BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative’s region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.
Extract from Google Documentation: From "Row-Level Security in BigQuery"
(https://cloud.google.com/bigquery/docs/row-level-security): "Row-level access policies let you restrict access to specific rows in a table based on a filter condition, such as a user’s region, providing fine-grained control over data visibility without creating separate tables or views."
Reference: Google Cloud Documentation – "BigQuery Row-Level Security"
(https://cloud.google.com/bigquery/docs/row-level-security).
B
Explanation:
Creating a row-level access policy in BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative’s region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.
Extract from Google Documentation: From "Row-Level Security in BigQuery"
(https://cloud.google.com/bigquery/docs/row-level-security): "Row-level access policies let you restrict access to specific rows in a table based on a filter condition, such as a user’s region, providing fine-grained control over data visibility without creating separate tables or views."
Reference: Google Cloud Documentation – "BigQuery Row-Level Security"
(https://cloud.google.com/bigquery/docs/row-level-security).