Getting a dashboard or report in front of the people who need it

For the Professional Data Engineer exam, you need to know how to publish data insights using Google Cloud's native BI tools, primarily Looker Studio and Looker, and how these tools connect to and query underlying data sources efficiently. Understanding the trade-offs between direct connections, extracts, and cached data is key to designing performant, cost-effective reporting solutions.

Must-know

• Looker Studio (formerly Data Studio) connects directly to BigQuery, Cloud SQL, Sheets, and other sources, and can cache query results to reduce redundant queries and costs.
• BigQuery BI Engine is an in-memory analysis service that accelerates SQL queries from Looker Studio and other BI tools by caching data in memory, reducing latency for dashboards.
• Looker uses LookML, a modeling layer, to define reusable metrics and dimensions, enabling governed, consistent reporting across an organization rather than duplicating SQL logic in each report.
• For large or frequently accessed datasets, materializing results into a BigQuery table (rather than querying raw data live) can significantly reduce dashboard load times and query costs.
• Row-level and column-level security should be enforced at the data warehouse level (e.g., BigQuery authorized views, row-level access policies) rather than solely in the BI tool, to ensure consistent governance across all reporting layers.
• Scheduling data refreshes (via scheduled queries, Cloud Composer, or Dataform) ensures reports reflect current data without requiring live queries on every dashboard view, balancing freshness against cost and performance.

Coverage checked against the published exam guide on Aug 11, 2026.

These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.