PDE Preparing and Using Data for Analysis Practice Question
A company uses Looker Studio to create dashboards from BigQuery data. They notice that dashboard queries take several seconds to load. They want to improve performance without changing the underlying data or creating 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 for the project
BigQuery BI Engine is an in-memory analysis service that accelerates queries from Looker Studio (and other BI tools) by caching data in memory, significantly reducing latency. Replicating data to Cloud SQL would add complexity and may not handle the volume. Using Looker instead of Looker Studio doesn't inherently speed up queries. Increasing BigQuery slots would help but is more expensive and not as targeted for BI tools.
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 for the project
Why this is correct
BI Engine is an in-memory analysis layer that caches BigQuery data and accelerates dashboard queries, requiring no changes to the underlying tables or materialised views. Enabling it for the project directly reduces the several-second load times observed in Looker Studio.
- ✗
Increase the number of BigQuery slots
Why it's wrong here
Adding slots increases BigQuery compute capacity, but the delay stems from Looker Studio's query pattern and caching behaviour, so extra slots do not fix it. It is tempting because slots genuinely accelerate slow BigQuery jobs, and would be correct when the underlying SQL itself is compute-bound.
- ✗
Switch to Looker instead of Looker Studio
Why it's wrong here
Migrating to Looker changes the BI platform and modelling layer; it does not address how Looker Studio issues queries to BigQuery, so latency persists. It is tempting because Looker offers governed, cached explores, and would be correct where semantic modelling and consistent metrics across teams are the actual requirement.
- ✗
Replicate the data to Cloud SQL for faster queries
Why it's wrong here
Copying data to Cloud SQL introduces replication latency and a second copy outside BigQuery, while Looker Studio's connector still queries per interaction. It is tempting because Cloud SQL serves indexed row lookups quickly, and would be correct for transactional, low-latency application queries rather than analytical dashboards.
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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.