Google Professional Data Engineer

Explanation

This question addresses data isolation and access control in Google BigQuery for multi-tenant environments. Here's why options B, D, and F are correct:

B. Load data into a different dataset for each client

• Logical Separation: Creating separate datasets for each client provides logical isolation at the dataset level
• Access Control: Datasets in BigQuery can have independent access controls and permissions
• Organization: This approach maintains clean data organization and prevents accidental cross-client data access

D. Restrict a client's dataset to approved users

• Principle of Least Privilege: Only authorized users should have access to specific client data
• User-Level Security: Ensures that individual users can only access datasets they are explicitly permitted to
• Audit Trail: Makes it easier to track and monitor who has access to which client's data

F. Use the appropriate identity and access management (IAM) roles for each client's users

• Granular Permissions: IAM roles allow fine-grained control over what actions users can perform
• Role-Based Access: Different roles (viewer, editor, admin) can be assigned based on user needs
• Centralized Management: IAM provides a centralized way to manage access across Google Cloud services

Why other options are incorrect:

• A. Load data into different partitions: Partitions are for data organization within a table, not for security isolation between clients
• C. Put each client's BigQuery dataset into a different table: This is redundant since datasets already contain tables; the proper approach is dataset-level separation
• E. Only allow a service account to access the datasets: This is too restrictive and doesn't allow for the direct query access that clients need for their analytics tools

This approach ensures proper data isolation while maintaining the flexibility clients need for their analytics workflows.