A retail analytics team builds a Looker Studio dashboard on BigQuery. They create a materialized view that aggregates daily sales, enable BI Engine capacity, protect PII columns with policy-tag column security and dynamic masking, and grant analysts access only through an authorized view that selects non-PII columns. When analysts refresh the dashboard, how will BigQuery handle query acceleration and security?
BI Engine ignores materialized views and always scans the base tables, so the dashboard gains no performance benefit.
BI Engine returns unmasked PII data because its cache is populated before security policies are applied.
BI Engine caches and accelerates the authorized view, automatically leveraging the materialized view while honoring column-level security and data masking.
BI Engine must be disabled because policy tags or dynamic masking prevent it from caching results.
BI Engine can cache and accelerate queries executed through authorized views. Because analysts access the dashboard through an authorized view, BigQuery can transparently use the pre-computed results in the materialized view for faster performance. BI Engine enforces all BigQuery security layers-authorized views, policy-tag column security, and dynamic data masking-so analysts never see masked or restricted data. Direct access to base tables is not required, and materialized views are used unless stale.
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What is a Looker Studio dashboard, and how does it integrate with BigQuery?
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What is a materialized view in BigQuery, and how does it improve query performance?
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What is dynamic masking, and how does BigQuery enforce data security with policy tags?
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What is a materialized view in BigQuery?
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How does policy-tag column security and dynamic masking work in BigQuery?
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What is BI Engine in BigQuery, and how does it accelerate queries?
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