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PCDE BigQuery BI Engine Practice Question
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.)
⚠ Common exam trap
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.
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.
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.
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.
- ✓
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.
- ✗
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.
- ✗
Use DML statements to pre-aggregate data.
Why it's wrong here
Using DML statements to pre-aggregate data, whilst achieving aggregation, incurs query costs with each execution, failing to inherently reduce overall expenditure for frequent refreshes. This approach also lacks the automatic query rewriting and cost optimisation benefits that BigQuery's native materialized views provide for predictable analytical queries. It is tempting because pre-aggregation is a valid cost-reduction technique, and DML is used to create and populate tables. This method would be suitable for infrequent, custom batch aggregations where specific transformation logic is required, rather than for optimising frequent query costs.
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Last reviewed: Jul 4, 2026
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