A company is building a data lake and wants to implement fine-grained access control. The data includes sensitive columns that should only be visible to certain users. The company uses BigQuery for querying the data. What should the data engineer do?

Use BigQuery column-level security to restrict access to sensitive columns.

Use Cloud Storage object ACLs to restrict access to files containing sensitive columns.

Use IAM conditions on the BigQuery dataset to restrict access to certain columns.

Use Cloud Data Catalog tags to hide sensitive columns.

Google CloudProfessional Data Engineer

Domain 3Objective 3

3.3 Using a Data Lake PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 4)

Part of the Storing the data domain, which accounts for ~20% of the PROFESSIONAL-DATA-ENGINEER exam. Google Cloud does not publish an official question count, but from its 120-minute exam (~50–80 total, ~10–16 in this domain), expect 3–4 from this objective — we provide 16 practice questions to prepare you well beyond it. (estimate)

16questions here

4free pages

3concepts

~20%of the exam

Questions 16–16

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A company is building a data lake and wants to implement fine-grained access control. The data includes sensitive columns that should only be visible to certain users. The company uses BigQuery for querying the data. What should the data engineer do?
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3.3 Using a Data Lake PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 4)

Questions 16–16

A company is building a data lake and wants to implement fine-grained access control. The data includes sensitive columns that should only be visible to certain users. The company uses BigQuery for querying the data. What should the data engineer do?

Keep studying — Storing the data