PDE Designing Data Processing Systems Practice Question

A data engineer needs to create a BigQuery table that is partitioned by ingestion time and clustered by customer_id and transaction_date. They also want to limit access so that only users from a specific domain can query the table. Which approach should they use?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Create the table with partitioning and clustering, then create an authorized view on the table and grant the view access to the domain users

Authorized views allow sharing query results with specific users/groups without giving direct table access. Clustering and partitioning are defined at table creation. IAM roles at dataset level are too broad. Row-level security filters rows but doesn't restrict domain.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Create the table with partitioning only, then use a materialized view to restrict access
Why it's wrong here
Materialized views are for performance, not access control.
✗
Create the table without clustering, use row-level security to filter by domain, and grant access to the table
Why it's wrong here
Row-level security filters rows but does not restrict access by domain.
✓
Create the table with partitioning and clustering, then create an authorized view on the table and grant the view access to the domain users
Why this is correct
Authorized views allow controlled access without granting direct table access.
✗
Create the table with partitioning and clustering, then grant bigquery.dataViewer to the domain via IAM at the dataset level
Why it's wrong here
Granting at dataset level gives access to all tables, not just this one.

Go deeper

PDE Designing Data Processing Systems Practice Question

Why each option matters

Option-by-option breakdown

Related to this question