- A
Use BI Engine to cache results of frequent queries.
Correct: BI Engine caches results of frequent, predictable queries in memory, reducing slot consumption and storage scans.
- B
Create materialized views for common aggregations.
Correct: Materialized views store pre‑computed aggregation results, so queries read only the view instead of scanning the full table.
- C
Partition tables by ingestion time.
Why wrong: Incorrect: Ingestion‑time partitioning prunes by arrival time, but predictable queries often filter on other columns, making partitioning less effective for cost reduction.
- D
Cluster tables on frequently filtered columns.
Correct: Clustering on frequently filtered columns minimizes bytes scanned for those queries, directly lowering cost.
- E
Use DML statements to pre-aggregate data.
Why wrong: Incorrect: DML pre‑aggregation requires manual maintenance and does not automatically update; materialized views or BI Engine are more efficient for predictable workloads.
PCDOE BigQuery BI Engine Practice Question
This PCDOE practice question tests your understanding of design and plan database solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: bigQuery BI Engine. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company runs a financial analytics platform on BigQuery. They need to reduce query costs for frequent, predictable queries. Which three strategies can help? (Choose THREE.)
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
Use BI Engine to cache results of frequent queries.
Option A is correct because BigQuery BI Engine provides an in-memory analysis service that caches results of frequent and predictable queries, reducing the need to scan data in BigQuery storage and thereby lowering query costs. By serving cached results directly from memory, BI Engine avoids repeated data processing and slot consumption for recurring queries. Option B is correct because materialized views allow you to pre-compute and store the results of common aggregations. When you query a materialized view, BigQuery uses the pre-computed results instead of scanning the base tables, which reduces the amount of data processed and thus lowers query costs, especially for frequent, predictable aggregations. Option D is correct because clustering tables on frequently filtered columns can significantly reduce the amount of data scanned by queries that filter on those columns. By organizing data based on the clustering columns, BigQuery can efficiently prune partitions and only scan relevant blocks. This reduces the bytes billed for each query, leading to cost savings for frequent, predictable queries with filtering predicates. Option C is incorrect because partitioning by ingestion time is primarily used for managing data lifecycle and improving query performance on time-based ranges, but it does not directly address cost reduction for frequent, predictable queries. While it can reduce scanned data for time-range queries, it is not as targeted as the other strategies for predictable, repeated access patterns. Option E is incorrect because using DML statements to pre-aggregate data would require additional processing and storage costs for the aggregated tables, and the queries against those tables would still incur costs. This approach does not inherently reduce query costs compared to using materialized views or BI Engine, and it adds complexity and maintenance overhead.
Key principle: BigQuery BI Engine
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use BI Engine to cache results of frequent queries.
Why this is correct
Correct: BI Engine caches results of frequent, predictable queries in memory, reducing slot consumption and storage scans.
Related concept
BigQuery BI Engine
- ✓
Create materialized views for common aggregations.
Why this is correct
Correct: Materialized views store pre‑computed aggregation results, so queries read only the view instead of scanning the full table.
Related concept
BigQuery BI Engine
- ✗
Partition tables by ingestion time.
Why it's wrong here
Incorrect: Ingestion‑time partitioning prunes by arrival time, but predictable queries often filter on other columns, making partitioning less effective for cost reduction.
- ✓
Cluster tables on frequently filtered columns.
Why this is correct
Correct: Clustering on frequently filtered columns minimizes bytes scanned for those queries, directly lowering cost.
Related concept
BigQuery BI Engine
- ✗
Use DML statements to pre-aggregate data.
Why it's wrong here
Incorrect: DML pre‑aggregation requires manual maintenance and does not automatically update; materialized views or BI Engine are more efficient for predictable workloads.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse cost-reduction techniques that directly cache or precompute results (BI Engine, materialized views) with performance optimizations that also reduce scanned data (clustering). Here, clustering is correct because it reduces bytes billed for frequent filters, but partitioning by ingestion time is less effective for predictable queries unless they always filter on that timestamp.
Detailed technical explanation
How to think about this question
BigQuery BI Engine uses a columnar in-memory cache that integrates with the BigQuery query engine, automatically caching results of queries that match specific patterns, such as those with repeated filters and aggregations. Materialized views in BigQuery are pre-computed tables that are automatically refreshed based on base table changes, allowing queries to read from the view instead of scanning the entire table, which reduces slot usage and cost. Clustering tables on frequently filtered columns organizes data by sort order, enabling the query engine to skip irrelevant blocks during scans, which lowers the bytes processed and thus cost for queries with those filters.
KKey Concepts to Remember
- BigQuery BI Engine
- Materialized views
- Clustering
- Cost optimization
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
BigQuery BI Engine
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review bigQuery BI Engine, then practise related PCDOE questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this PCDOE question test?
Design and Plan Database Solutions — This question tests Design and Plan Database Solutions — BigQuery BI Engine.
What is the correct answer to this question?
The correct answer is: Use BI Engine to cache results of frequent queries. — Option A is correct because BigQuery BI Engine provides an in-memory analysis service that caches results of frequent and predictable queries, reducing the need to scan data in BigQuery storage and thereby lowering query costs. By serving cached results directly from memory, BI Engine avoids repeated data processing and slot consumption for recurring queries. Option B is correct because materialized views allow you to pre-compute and store the results of common aggregations. When you query a materialized view, BigQuery uses the pre-computed results instead of scanning the base tables, which reduces the amount of data processed and thus lowers query costs, especially for frequent, predictable aggregations. Option D is correct because clustering tables on frequently filtered columns can significantly reduce the amount of data scanned by queries that filter on those columns. By organizing data based on the clustering columns, BigQuery can efficiently prune partitions and only scan relevant blocks. This reduces the bytes billed for each query, leading to cost savings for frequent, predictable queries with filtering predicates. Option C is incorrect because partitioning by ingestion time is primarily used for managing data lifecycle and improving query performance on time-based ranges, but it does not directly address cost reduction for frequent, predictable queries. While it can reduce scanned data for time-range queries, it is not as targeted as the other strategies for predictable, repeated access patterns. Option E is incorrect because using DML statements to pre-aggregate data would require additional processing and storage costs for the aggregated tables, and the queries against those tables would still incur costs. This approach does not inherently reduce query costs compared to using materialized views or BI Engine, and it adds complexity and maintenance overhead.
What should I do if I get this PCDOE question wrong?
Review bigQuery BI Engine, then practise related PCDOE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
BigQuery BI Engine
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Last reviewed: Jul 4, 2026
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