This PDE practice question tests your understanding of designing data processing systems. 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.
A team is using BigQuery to analyze petabyte-scale data. They notice that queries are slow and expensive due to full table scans. They have already partitioned by date. What additional optimization should they implement?
Use materialized views
Why wrong: Materialized views pre-aggregate data but do not reduce scan on filtering.
Cluster by frequently filtered columns
Clustering reduces bytes read when filtering on those columns.
Convert to native tables
Why wrong: Tables are already native; this is not an optimization.
Use query caching
Why wrong: Caching benefits repeated queries, not initial full scans.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Cluster by frequently filtered columns
Clustering by frequently filtered columns (option B) organizes data within each partition based on the sort order of those columns. This allows BigQuery to prune blocks during query execution, significantly reducing the amount of data scanned and improving both performance and cost. Since the table is already partitioned by date, clustering adds a secondary ordering that targets the most common filter predicates, avoiding full table scans within each partition.
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 materialized views
Why it's wrong here
Materialized views pre-aggregate data but do not reduce scan on filtering.
Cluster by frequently filtered columns
Why this is correct
Clustering reduces bytes read when filtering on those columns.
Related concept
Read the scenario before looking for a memorised answer.
Convert to native tables
Why it's wrong here
Tables are already native; this is not an optimization.
Use query caching
Why it's wrong here
Caching benefits repeated queries, not initial full scans.
Common exam traps
Google Cloud often tests the distinction between partitioning and clustering, where candidates mistakenly believe partitioning alone is sufficient for all filtering scenarios, but clustering is required to avoid full scans on non-date columns.
Detailed technical explanation
Clustering in BigQuery uses a block-level metadata system where each block (approximately 1 MB) stores the min and max values of clustered columns. When a query includes a filter on a clustered column, BigQuery can skip entire blocks that do not contain matching values, reducing the number of bytes read. This is especially effective for high-cardinality columns like user_id or transaction_id, where partitioning alone cannot provide fine-grained pruning.
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
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Cluster by frequently filtered columns — Clustering by frequently filtered columns (option B) organizes data within each partition based on the sort order of those columns. This allows BigQuery to prune blocks during query execution, significantly reducing the amount of data scanned and improving both performance and cost. Since the table is already partitioned by date, clustering adds a secondary ordering that targets the most common filter predicates, avoiding full table scans within each partition.
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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