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Google Associate Data Practitioner Exam Questions
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Free Google Associate Data Practitioner Exam Actual Questions
Note: Premium Questions for Associate Data Practitioner were last updated On Jun. 02, 2026 (see below)
You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?
Configuring a Cloud Storage lifecycle rule to automatically delete objects older than seven days is the best solution because:
Built-in feature: Cloud Storage lifecycle rules are specifically designed to manage object lifecycles, such as automatically deleting or transitioning objects based on age.
No additional setup: It requires no external services or custom code, reducing complexity and maintenance.
Cost-effective: It directly achieves the goal of deleting files after seven days without incurring additional compute costs.
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?
Using the 'Pub/Sub to BigQuery' Dataflow template with a UDF (User-Defined Function) is the optimal choice because it combines near real-time processing, minimal code for transformations, and scalability. The UDF allows for efficient implementation of custom transformations, such as capitalizing letters in the serial number field, while Dataflow handles the rest of the managed pipeline seamlessly.
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?
Creating a materialized view in BigQuery with the SUM() function and the DATE_SUB() function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using the DATE_SUB() function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.
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?
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).
Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of dat
a. You also want to create a reusable framework in case you need to share this data with other teams in the future. What should you do?
Using Analytics Hub to create a private exchange with data egress restrictions ensures controlled sharing of the dataset while minimizing the risk of unauthorized copying. This approach allows you to provide secure, managed access to the dataset without giving direct access to the raw data. The egress restriction ensures that data cannot be exported or copied outside the designated boundaries. Additionally, this solution provides a reusable framework that simplifies future data sharing with other teams or projects while maintaining strict data governance.
Extract from Google Documentation: From 'Analytics Hub Overview' (https://cloud.google.com/analytics-hub/docs): 'Analytics Hub enables secure, controlled data sharing with private exchanges. Combine with organization policies like restrictDataEgress to prevent data copying, providing a reusable framework for sharing BigQuery datasets across teams.' Reference: Google Cloud Documentation - 'Analytics Hub' (https://cloud.google.com/analytics-hub).
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