PDE Preparing and Using Data for Analysis Practice Question
A company uses Looker Studio to build dashboards from BigQuery data. They notice that queries take several seconds to return. They want to improve performance without changing the schema or adding materialized views. Which option should they use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable BigQuery BI Engine on the relevant project.
BI Engine accelerates sub-second query response times in Looker Studio by caching data in memory within the BigQuery region.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable BigQuery BI Engine on the relevant project.
Why this is correct
BI Engine provides an in-memory analysis layer that caches frequently accessed data, accelerating Looker Studio queries without schema changes or materialised views. It satisfies the stem's constraint of improving dashboard performance while leaving the underlying BigQuery tables untouched, and integrates natively with Looker Studio.
- ✗
Move the data to Cloud SQL.
Why it's wrong here
Cloud SQL is a transactional row-store, so it cannot serve BigQuery datasets and would require exporting and duplicating data, contradicting the no-schema-change constraint. Looker Studio's BigQuery connector needs BigQuery-native tuning. Cloud SQL would be the right target for a small OLTP application, not analytical dashboards.
- ✗
Switch to BigQuery Omni for cross-cloud queries.
Why it's wrong here
BigQuery Omni queries data held in Amazon S3 or Azure Blob Storage, so it adds cross-cloud network latency rather than reducing it for data already resident in BigQuery. It would be the right choice only when the source datasets physically live in another cloud provider's object storage.
- ✗
Use APPROX_COUNT_DISTINCT to speed up distinct counts.
Why it's wrong here
APPROX_COUNT_DISTINCT only accelerates queries containing COUNT(DISTINCT ...) expressions; it leaves the several-second latency of other aggregations and scans untouched. The stem requires a broadly applicable fix. This function would be the correct choice when distinct-count queries dominate the workload.
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This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.