PDE Preparing and Using Data for Analysis Practice Question
A company uses BigQuery and wants to reduce query costs by using BI Engine for Looker Studio dashboards. The data is stored in a BigQuery dataset with 5 TB of frequently accessed tables. The dashboards run dozens of concurrent queries. What is the recommended approach to enable BI Engine acceleration?
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
✓
Create a reservation in the Administration panel and assign it to the project.
BI Engine is a reserved capacity service that caches data in memory. You must reserve capacity (amount of memory) for a specific BigQuery region, and then BI Engine automatically accelerates queries from Looker Studio and other BI tools. It does not require you to grant specific IAM roles to users (they just need BigQuery permissions) or to create materialized views. You do not need to enable it per dataset; it works at the project/region level.
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 BI Engine by setting the dataset option 'enable_bi_engine=TRUE' in the dataset metadata.
Why it's wrong here
BI Engine is not enabled through a dataset metadata flag; acceleration is configured by reserving BI Engine capacity in the project, which then serves the Looker Studio queries. Dataset-level settings are tempting because BigQuery datasets do carry configuration metadata, but that governs storage and access, not in-memory acceleration.
- ✗
Grant all Looker Studio users the 'biengine.user' IAM role on the project.
Why it's wrong here
Granting biengine.user lets users consume existing acceleration but does not create the reservation that actually enables BI Engine. It is tempting because IAM roles are genuinely required for BI Engine access, and would be the right step once a capacity reservation exists, but permission alone leaves queries unaccelerated.
- ✓
Create a reservation in the Administration panel and assign it to the project.
Why this is correct
BI Engine acceleration requires a reservation assigned to the project; the 5 TB dataset and dozens of concurrent dashboard queries exceed the free per-project capacity, so a reservation guarantees dedicated in-memory cache for Looker Studio.
- ✗
Create materialized views of the tables and connect Looker Studio to the views.
Why it's wrong here
Materialised views precompute results but do not provide BI Engine's in-memory acceleration, and Looker Studio queries against them still incur BigQuery processing. They are tempting because materialised views genuinely reduce cost and latency for repeated aggregations, which would suit scheduled reporting rather than dozens of concurrent interactive dashboard queries.
About these practice questions
One of 747 original PDE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.