Google Associate Data Practitioner Practice Exams
Last updated on Oct 21,2026- Exam Code: Associate Data Practitioner
- Exam Name: Google Cloud Associate Data Practitioner ( ADP Exam )
- Certification Provider: Google
- Latest update: Oct 21,2026
Question #40
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 . Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the – -daisy-chain option.
- C . Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
- D . Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the – -no-clobber option.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed in Depth
Why A is correct: Storage Transfer Service with agent-based transfer allows for online migrations and provides the ability to set bandwidth limits.
Agents are installed on-premises and can be configured to respect network constraints.
Why other options are incorrect: B: The –daisy-chain option is not related to bandwidth control.
C: Transfer Appliance is for offline migrations and is not suitable for online transfers with bandwidth constraints.
D: The –no-clobber option prevents overwriting existing files but does not control bandwidth.
Reference: Storage Transfer Service: https://cloud.google.com/storage-transfer-service/docs
Storage Transfer Service Agents: https://cloud.google.com/storage-transfer-service/docs/agent-overview
gcloud storage cp: https://cloud.google.com/storage/docs/gsutil/commands/cp
A
Explanation:
Comprehensive and Detailed in Depth
Why A is correct: Storage Transfer Service with agent-based transfer allows for online migrations and provides the ability to set bandwidth limits.
Agents are installed on-premises and can be configured to respect network constraints.
Why other options are incorrect: B: The –daisy-chain option is not related to bandwidth control.
C: Transfer Appliance is for offline migrations and is not suitable for online transfers with bandwidth constraints.
D: The –no-clobber option prevents overwriting existing files but does not control bandwidth.
Reference: Storage Transfer Service: https://cloud.google.com/storage-transfer-service/docs
Storage Transfer Service Agents: https://cloud.google.com/storage-transfer-service/docs/agent-overview
gcloud storage cp: https://cloud.google.com/storage/docs/gsutil/commands/cp
Question #42
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 . 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 ID.
- C . Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
- D . Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
Correct Answer: D
Question #42
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 . 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 ID.
- C . Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
- D . Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
Correct Answer: D
Question #44
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner’s data in its own BigQuery dataset. Each partner should be able to access only their data. You want to share the data while following Google-recommended practices.
What should you do?
- A . Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
- B . Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub/Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
- C . Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
- D . Grant the partners the bigquery.user IAM role on the BigQuery project.
Correct Answer: A
A
Explanation:
Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.
A
Explanation:
Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.
Question #45
Your team uses Google Sheets to track budget data that is updated daily. The team wants to compare budget data against actual cost data, which is stored in a BigQuery table. You need to create a solution that calculates the difference between each day’s budget and actual costs. You want to ensure that your team has access to daily-updated results in Google Sheets.
What should you do?
- A . Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table as a CSV file and open the file in Google Sheets.
- B . Download the budget data as a CSV file and upload the CSV file to a Cloud Storage bucket. Create a new BigQuery table from Cloud Storage, and join the actual cost table with it. Open the joined BigQuery table by using Connected Sheets.
- C . Download the budget data as a CSV file, and upload the CSV file to create a new BigQuery table. Join the actual cost table with the new BigQuery table, and save the results as a CSV file. Open the CSV file in Google Sheets.
- D . Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table, and open it by using Connected Sheets.
Correct Answer: D
D
Explanation:
Comprehensive and Detailed in Depth
Why D is correct:Creating a BigQuery external table directly from the Google Sheet allows for real-time updates.
Joining the external table with the actual cost table in BigQuery performs the calculation.
Connected Sheets allows the team to access and analyze the results directly in Google Sheets, with the data being updated.
Why other options are incorrect:A: Saving as a CSV file loses the live connection and daily updates.
B: Downloading and uploading as a CSV file adds unnecessary steps and loses the live connection.
C: Same issue as B, losing the live connection.
Reference: BigQuery External Tables: https://cloud.google.com/bigquery/docs/external-tables
Connected Sheets: https://support.google.com/sheets/answer/9054368?hl=en
D
Explanation:
Comprehensive and Detailed in Depth
Why D is correct:Creating a BigQuery external table directly from the Google Sheet allows for real-time updates.
Joining the external table with the actual cost table in BigQuery performs the calculation.
Connected Sheets allows the team to access and analyze the results directly in Google Sheets, with the data being updated.
Why other options are incorrect:A: Saving as a CSV file loses the live connection and daily updates.
B: Downloading and uploading as a CSV file adds unnecessary steps and loses the live connection.
C: Same issue as B, losing the live connection.
Reference: BigQuery External Tables: https://cloud.google.com/bigquery/docs/external-tables
Connected Sheets: https://support.google.com/sheets/answer/9054368?hl=en
Question #46
Your organization’s business analysts require near real-time access to streaming data. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations.
You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible.
What should you do?
- A . Disable BiqQuery query result caching.
- B . Modify the schema to use parameterized data types.
- C . Create a scheduled query to calculate and store intermediate results.
- D . Create materialized views.
Correct Answer: D
D
Explanation:
Creating materialized views is the best solution to improve dashboard loading time while ensuring that the data is as up-to-date as possible. Materialized views precompute and cache the results of complex joins and aggregations, significantly reducing query execution time for dashboards. They also automatically update as the underlying data changes, ensuring near real-time access to fresh data. This approach optimizes query performance and provides an efficient and scalable solution for streaming data dashboards.
For near real-time dashboards with slow queries involving joins and aggregations, Google recommends materialized views in BigQuery to precompute and refresh results automatically.
Option A: Scheduled queries compute intermediate results periodically, but they aren’t near real-time and require manual scheduling, adding latency.
Option B: Parameterized data types don’t address query performance for joins/aggregations; they’re for query flexibility, not optimization.
Option C: Disabling caching worsens performance by forcing full recomputation each time, contrary to the goal.
Option D: Materialized views precompute joins and aggregations, auto-refresh with streaming data (near real-time), and optimize query performance for dashboards.
Extract from Google Documentation: From "Using Materialized Views in BigQuery"
(https://cloud.google.com/bigquery/docs/materialized-views): "Materialized views are precomputed views that periodically cache the results of a query, improving performance for complex queries like joins and aggregations, and they support automatic refresh for near real-time data."
Reference: Google Cloud Documentation – "Materialized Views" (https://cloud.google.com/bigquery/docs/materialized-views).
D
Explanation:
Creating materialized views is the best solution to improve dashboard loading time while ensuring that the data is as up-to-date as possible. Materialized views precompute and cache the results of complex joins and aggregations, significantly reducing query execution time for dashboards. They also automatically update as the underlying data changes, ensuring near real-time access to fresh data. This approach optimizes query performance and provides an efficient and scalable solution for streaming data dashboards.
For near real-time dashboards with slow queries involving joins and aggregations, Google recommends materialized views in BigQuery to precompute and refresh results automatically.
Option A: Scheduled queries compute intermediate results periodically, but they aren’t near real-time and require manual scheduling, adding latency.
Option B: Parameterized data types don’t address query performance for joins/aggregations; they’re for query flexibility, not optimization.
Option C: Disabling caching worsens performance by forcing full recomputation each time, contrary to the goal.
Option D: Materialized views precompute joins and aggregations, auto-refresh with streaming data (near real-time), and optimize query performance for dashboards.
Extract from Google Documentation: From "Using Materialized Views in BigQuery"
(https://cloud.google.com/bigquery/docs/materialized-views): "Materialized views are precomputed views that periodically cache the results of a query, improving performance for complex queries like joins and aggregations, and they support automatic refresh for near real-time data."
Reference: Google Cloud Documentation – "Materialized Views" (https://cloud.google.com/bigquery/docs/materialized-views).
Question #47
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 . Enable Object Versioning.
- B . Change the bucket storage class to Archive.
- C . Set a bucket retention policy.
- D . Set temporary object holds.
Correct Answer: C
C
Explanation:
Comprehensive and Detailed in Depth
Why C is correct:Bucket retention policies are specifically designed to enforce a minimum retention period for objects within a Cloud Storage bucket. This ensures that data cannot be deleted or overwritten before the specified period.
Why other options are incorrect:A: Object versioning allows you to keep multiple versions of an object, but it doesn’t guarantee a minimum retention period.
B: Changing the storage class to Archive is for cost optimization, not data retention enforcement.
D: Object holds are for legal holds, not general retention.
Reference: Cloud Storage Retention Policies: https://cloud.google.com/storage/docs/bucket-lock
Cloud Storage Object Versioning: https://cloud.google.com/storage/docs/object-versioning
Cloud Storage Storage Classes: https://cloud.google.com/storage/docs/storage-classes
Cloud Storage Object Holds: https://cloud.google.com/storage/docs/legal-holds
C
Explanation:
Comprehensive and Detailed in Depth
Why C is correct:Bucket retention policies are specifically designed to enforce a minimum retention period for objects within a Cloud Storage bucket. This ensures that data cannot be deleted or overwritten before the specified period.
Why other options are incorrect:A: Object versioning allows you to keep multiple versions of an object, but it doesn’t guarantee a minimum retention period.
B: Changing the storage class to Archive is for cost optimization, not data retention enforcement.
D: Object holds are for legal holds, not general retention.
Reference: Cloud Storage Retention Policies: https://cloud.google.com/storage/docs/bucket-lock
Cloud Storage Object Versioning: https://cloud.google.com/storage/docs/object-versioning
Cloud Storage Storage Classes: https://cloud.google.com/storage/docs/storage-classes
Cloud Storage Object Holds: https://cloud.google.com/storage/docs/legal-holds
Question #48
You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue.
What should you do?
- A . Use Cloud Logging to identify error groups in the pipeline’s logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
- B . Use Cloud Logging to create a chart displaying the pipeline’s error logs. Use Metrics Explorer to validate the findings from the chart.
- C . Use Cloud Logging to view error messages in the pipeline’s logs. Use Cloud Monitoring to analyze the pipeline’s metrics, such as CPU utilization and memory usage.
- D . Use the Dataflow job monitoring interface to check the pipeline’s status every hour. Use Cloud Profiler to analyze the pipeline’s metrics, such as CPU utilization and memory usage.
Correct Answer: C
C
Explanation:
To troubleshoot intermittent failures in a Dataflow pipeline, you should use Cloud Logging to view detailed error messages in the pipeline’s logs. These logs provide insights into the specific issues causing failures, such as data format errors or resource limitations. Additionally, you should use Cloud Monitoring to analyze the pipeline’s metrics, such as CPU utilization, memory usage, and throughput, to identify performance bottlenecks or resource constraints that may contribute to the failures. This approach provides a comprehensive view of the pipeline’s health and helps pinpoint the root cause of the intermittent issues.
C
Explanation:
To troubleshoot intermittent failures in a Dataflow pipeline, you should use Cloud Logging to view detailed error messages in the pipeline’s logs. These logs provide insights into the specific issues causing failures, such as data format errors or resource limitations. Additionally, you should use Cloud Monitoring to analyze the pipeline’s metrics, such as CPU utilization, memory usage, and throughput, to identify performance bottlenecks or resource constraints that may contribute to the failures. This approach provides a comprehensive view of the pipeline’s health and helps pinpoint the root cause of the intermittent issues.
Question #48
You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue.
What should you do?
- A . Use Cloud Logging to identify error groups in the pipeline’s logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
- B . Use Cloud Logging to create a chart displaying the pipeline’s error logs. Use Metrics Explorer to validate the findings from the chart.
- C . Use Cloud Logging to view error messages in the pipeline’s logs. Use Cloud Monitoring to analyze the pipeline’s metrics, such as CPU utilization and memory usage.
- D . Use the Dataflow job monitoring interface to check the pipeline’s status every hour. Use Cloud Profiler to analyze the pipeline’s metrics, such as CPU utilization and memory usage.
Correct Answer: C
C
Explanation:
To troubleshoot intermittent failures in a Dataflow pipeline, you should use Cloud Logging to view detailed error messages in the pipeline’s logs. These logs provide insights into the specific issues causing failures, such as data format errors or resource limitations. Additionally, you should use Cloud Monitoring to analyze the pipeline’s metrics, such as CPU utilization, memory usage, and throughput, to identify performance bottlenecks or resource constraints that may contribute to the failures. This approach provides a comprehensive view of the pipeline’s health and helps pinpoint the root cause of the intermittent issues.
C
Explanation:
To troubleshoot intermittent failures in a Dataflow pipeline, you should use Cloud Logging to view detailed error messages in the pipeline’s logs. These logs provide insights into the specific issues causing failures, such as data format errors or resource limitations. Additionally, you should use Cloud Monitoring to analyze the pipeline’s metrics, such as CPU utilization, memory usage, and throughput, to identify performance bottlenecks or resource constraints that may contribute to the failures. This approach provides a comprehensive view of the pipeline’s health and helps pinpoint the root cause of the intermittent issues.
Question #50
Your organization’s ecommerce website collects user activity logs using a Pub/Sub topic. Your organization’s leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried.
What should you do?
- A . Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
- B . Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
- C . Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
- D . Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
Correct Answer: A
A
Explanation:
Using Dataflow to subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time. It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.
A
Explanation:
Using Dataflow to subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time. It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.