Google Professional Data Engineer exam dumps

Google Professional Data Engineer practice question 172 of 279

Professional Data Engineer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Data Engineer Question 172

Select 3Google Cloud Platform

Your organization has a distributed team that works with data stored in Google BigQuery. Each team should only access data relevant to their specific domain while ensuring compliance with data governance policies. Which approaches can you use to segment the data effectively for the distributed teams?

  1. A

    Create separate datasets for each team and grant dataset-level permissions.

  2. B

    Use BigQuery row-level security to restrict access based on team-specific criteria.

  3. C

    Duplicate the data into separate tables for each team to isolate access control.

  4. D

    Apply BigQuery column-level security to restrict specific columns based on team roles.

  5. E

    Use Cloud Storage buckets with team-specific JSON exports instead of BigQuery.

Show answer and explanation

Correct answers: A, B, D

Explanation

To segment data for distributed teams, you can use BigQuery's built-in features like dataset-level permissions, row-level security, and column-level security to ensure access control and compliance. These methods allow for efficient and scalable management of permissions without duplicating data or resorting to inefficient practices like exporting data to external storage solutions.

  • A. Correct.

    Creating separate datasets for each team and granting permissions is a valid approach to segment data while maintaining logical separation and access control.

  • B. Correct.

    BigQuery row-level security is an effective way to restrict access dynamically based on team-specific criteria, ensuring that users only see relevant rows.

  • C. Incorrect.

    Duplicating data into separate tables is inefficient, increases storage costs, and makes it harder to manage. It is not a best practice.

  • D. Correct.

    BigQuery column-level security allows you to restrict access to sensitive columns based on team roles, ensuring compliance with data governance policies.

  • E. Incorrect.

    Using Cloud Storage buckets with JSON exports is not an efficient or scalable solution compared to BigQuery's built-in security and segmentation features.

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