This GCDL practice question tests your understanding of fundamental cloud concepts. 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 data analytics company uses BigQuery for large-scale queries. They notice that some queries are very expensive due to scanning large amounts of data. They want to reduce costs without changing query logic. Which feature should they use?
Query caching
Why wrong: Caching speeds up repeated queries but does not reduce cost of initial scans.
Partitioning and clustering tables
Partitioning prunes partitions, clustering orders data; both reduce bytes processed.
Authorized views
Why wrong: Authorized views control access, not cost.
Flat-rate pricing with reservations
Why wrong: Reservations provide predictable costs but do not reduce data scanned.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Partitioning and clustering tables
Partitioning and clustering tables in BigQuery physically organize data into smaller, manageable segments based on specified columns (e.g., date or timestamp). This allows queries to use partition pruning and clustering-based block pruning to scan only the relevant data, drastically reducing the amount of data processed and thus lowering costs without altering the query logic.
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.
Query caching
Why it's wrong here
Caching speeds up repeated queries but does not reduce cost of initial scans.
Partitioning and clustering tables
Why this is correct
Partitioning prunes partitions, clustering orders data; both reduce bytes processed.
Related concept
Read the scenario before looking for a memorised answer.
Authorized views
Why it's wrong here
Authorized views control access, not cost.
Flat-rate pricing with reservations
Why it's wrong here
Reservations provide predictable costs but do not reduce data scanned.
Common exam traps
Google Cloud often tests the misconception that cost reduction must come from changing pricing models (like flat-rate) rather than from data organization techniques that reduce the actual amount of data processed.
Detailed technical explanation
Under the hood, BigQuery partitions tables by a column (commonly a TIMESTAMP or DATE) into separate storage blocks, and clustering sorts data within each partition by one or more columns. When a query includes a filter on the partition column, BigQuery uses partition pruning to skip entire partitions, and clustering further limits scanning to specific blocks within a partition via a metadata-based block index. In a real-world scenario, a table with 1 TB of data partitioned by day and clustered by user_id can reduce a query filtering on a single day and a specific user to scanning only a few MB, whereas without these features the full 1 TB would be billed.
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.
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FAQ
Fundamental cloud concepts — This question tests Fundamental cloud concepts — Read the scenario before looking for a memorised answer..
The correct answer is: Partitioning and clustering tables — Partitioning and clustering tables in BigQuery physically organize data into smaller, manageable segments based on specified columns (e.g., date or timestamp). This allows queries to use partition pruning and clustering-based block pruning to scan only the relevant data, drastically reducing the amount of data processed and thus lowering costs without altering the query logic.
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
This GCDL 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 GCDL exam.
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