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Professional Cloud Data Engineer
Connecting to tools
Your organization uses a Looker dashboard connected to BigQu…

Professional Cloud Data Engineer

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Optimize Bi Engine Materialized Views Joins

Section: Connecting to tools

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Keep the momentum going with these hand-picked practice scenarios

AdvancedProfessional Cloud Data Engineer

Your enterprise stores sensitive sales and customer data in BigQuery. The dataset implements row-level security (RLS) to restrict sales data access based on each manager's region (SESSION_USER()), along with column-level security policy tags to mask personally identifiable information (PII). You need to configure access for business intelligence (BI) visualization dashboards in Looker Studio so that multiple regional sales managers can view their respective metrics securely while strictly adhering to the principle of least privilege. Which configuration should you implement?

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Last updated August 3, 2026

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Professional Cloud Data Engineer

Provider: Google Cloud
Passing score: 70%
Questions: 50
Duration: 90 minutes

Other sections in this exam

Designing data processing systems
Ingesting and processing the data
Storing the data
Preparing and using data for analysis
Maintaining and automating data workloads

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Your organization uses a Looker dashboard connected to BigQuery to analyze sales performance. The dashboard displays the current month's data and relies on a query that joins a massive sales_fact table with multiple smaller dimension tables.

Users are reporting high dashboard latency, and the repeated execution of these complex joins is driving up analytical costs. You need to optimize the data layout and leverage BigQuery BI Engine to minimize query latency and reduce costs.

What should you do?

Create a logical view that joins the tables and partition the base sales_fact table by the sales date. Configure a BI Engine reservation and add the logical view to the preferred tables list.

Create a materialized view that pre-joins the tables and partition it by the sales date. Configure a BI Engine reservation and add both the materialized view and its base tables to the preferred tables list.

Create a materialized view that pre-joins the tables and partition it by the sales date. Configure a BI Engine reservation and add only the materialized view to the preferred tables list.

Export the joined data to a BigLake Iceberg table and partition it by the sales date. Configure a BI Engine reservation and add the BigLake Iceberg table to the preferred tables list.

Create a logical view that joins the tables and partition the base sales_fact table by the sales date. Configure a BI Engine reservation and add the logical view to the preferred tables list.

Create a materialized view that pre-joins the tables and partition it by the sales date. Configure a BI Engine reservation and add both the materialized view and its base tables to the preferred tables list.

What materialized views and BI Engine are

BigQuery BI Engine is a fast, in-memory analysis service that accelerates SQL queries by intelligently caching frequently used data. Materialized views are precomputed views that periodically cache the results of a query, such as complex joins or aggregations.

How it meets the requirements

Minimize query latency: BI Engine caches the data in memory, providing sub-second response times for the dashboard.
Reduce analytical costs: The materialized view pre-joins the massive fact table with the dimension tables, producing a single flat table. This ensures the expensive join operations are not recomputed every time a user interacts with the dashboard.
Optimize data layout: Partitioning the materialized view by the sales date ensures that only the latest partitions (the current month's data) are loaded into BI Engine memory, optimizing memory allocation.
Leverage BI Engine: By adding both the materialized view and its base tables to the preferred tables list, the configuration satisfies the strict requirements for accelerating materialized views.

Key advantages/capabilities

Combines the pre-aggregation benefits of materialized views with the in-memory speed of BI Engine.
Partitioning prevents older, unused data from consuming valuable memory reservations.
Preferred tables prevent other traffic from interfering with the acceleration of high-visibility dashboards.

Why this is optimal

BI Engine works best with pre-joined or pre-aggregated data. Using a materialized view to flatten the data structure is the recommended best practice for optimizing performance. Furthermore, BigQuery requires that both the materialized view and its base tables be designated as preferred tables for the acceleration to occur, making this the only technically correct configuration.

Create a materialized view that pre-joins the tables and partition it by the sales date. Configure a BI Engine reservation and add only the materialized view to the preferred tables list.

Export the joined data to a BigLake Iceberg table and partition it by the sales date. Configure a BI Engine reservation and add the BigLake Iceberg table to the preferred tables list.