You are using a SQL-based tool to visualize data stored in BigQuery, which involves outer joins and analytic functions. The visualizations must be based on data that is at least 4 hours old. Business users are expressing dissatisfaction with the slow generation of these visualizations. What strategy can you implement to improve the performance of visualization queries while also reducing the maintenance burden of the data preparation pipeline?

Real Exam

Export the visualization query results as parquet files to a Cloud Storage bucket using a Cloud Function instance, triggered every 4 hours by Cloud Scheduler. Reference these parquet files in the data visualization tool.

0.0%

Create views for the visualization queries and reference these views in the data visualization tool.

11.1%

Create materialized views for the visualization queries, utilizing BigQuery's incremental updates capability to automatically handle changed data. Reference these materialized views in the data visualization tool.

11.1%

Create materialized views with the allow_non_incremental_definition option enabled for the visualization queries. Set the max_staleness parameter to 4 hours and the enable_refresh parameter to true. Reference these materialized views in the data visualization tool._

77.8%

Google Professional Data Engineer

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