This PCDE practice question tests your understanding of define data structures and implement sql for business intelligence. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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.
Which TWO strategies reduce query costs for ad-hoc analysis in BigQuery? (Choose two.)
Use LIMIT 10 to preview data.
Why wrong: LIMIT does not reduce bytes scanned.
Use clustered tables on frequently filtered columns.
Clustering allows pruning of blocks.
Use a flat table without partitioning.
Why wrong: Flat tables increase scanned bytes.
Use SELECT * in all queries.
Why wrong: SELECT * reads all columns, increasing cost.
Use materialized views for common aggregations.
Materialized views provide pre-computed results.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use clustered tables on frequently filtered columns.
Option B is correct because clustered tables in BigQuery physically sort data based on the specified columns, which allows the query engine to skip entire blocks of data that don't match filter predicates. This reduces the amount of data scanned and thus lowers query costs for ad-hoc analysis. Option E is correct because materialized views precompute and store the results of common aggregations, so queries against them only read the precomputed results rather than scanning the base table, significantly reducing bytes processed.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use LIMIT 10 to preview data.
Why it's wrong here
LIMIT does not reduce bytes scanned.
Use clustered tables on frequently filtered columns.
Why this is correct
Clustering allows pruning of blocks.
Related concept
Read the scenario before looking for a memorised answer.
Use a flat table without partitioning.
Why it's wrong here
Flat tables increase scanned bytes.
Use SELECT * in all queries.
Why it's wrong here
SELECT * reads all columns, increasing cost.
Use materialized views for common aggregations.
Why this is correct
Materialized views provide pre-computed results.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the misconception that LIMIT reduces cost (it does not in BigQuery's serverless architecture) and that denormalized or flat tables are cheaper (they are not because they increase scan size).
Detailed technical explanation
Under the hood, BigQuery uses a columnar storage format (Capacitor) and a distributed query engine. Clustering leverages the natural sort order to create block-level metadata (min/max values), enabling the engine to prune entire storage blocks during scan. Materialized views in BigQuery are automatically maintained and can be queried directly or used by the optimizer to rewrite queries, providing cost savings without requiring users to change their SQL.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Define data structures and implement SQL for Business Intelligence — This question tests Define data structures and implement SQL for Business Intelligence — Read the scenario before looking for a memorised answer..
The correct answer is: Use clustered tables on frequently filtered columns. — Option B is correct because clustered tables in BigQuery physically sort data based on the specified columns, which allows the query engine to skip entire blocks of data that don't match filter predicates. This reduces the amount of data scanned and thus lowers query costs for ad-hoc analysis. Option E is correct because materialized views precompute and store the results of common aggregations, so queries against them only read the precomputed results rather than scanning the base table, significantly reducing bytes processed.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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