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Answer: 1. Create a dataset for the data science team. 2. Create views of tables that you want to share, excluding PII. 3. Assign an appropriate project-level IAM role to the members of the data science team. 4. Assign access controls to the dataset that contains the view. 5. Authorize the view to access the source dataset.
The correct answer is C. Creating a separate dataset specifically for the data science team provides better organization and access control management for different teams. By creating views that exclude PII within the new dataset, and then authorizing these views to access the source dataset, you can ensure that the data science team only has access to the non-PII data without granting them access to the underlying tables containing PII. This approach aligns with Google Cloud's best practices for managing access to sensitive data, making it more secure and compliant. Furthermore, using views rather than materialized views helps in minimizing costs.
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Your company has a Google Cloud project that leverages BigQuery for data warehousing, storing a large volume of data including personally identifiable information (PII) in some tables. According to internal policies and compliance requirements, only the compliance team is permitted to access the PII, while the data science team needs access to non-PII data from the same tables. You are responsible for ensuring that access controls are implemented in a cost-effective and efficient manner. What should you do to ensure that the compliance team has exclusive access to PII and the data science team has access to non-PII data, while minimizing cost and the time required to assign appropriate access?
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