The answer is partition tables by date and use partition pruning in queries. This is the most effective approach for BigQuery cost reduction because partition pruning directly minimizes the amount of data scanned per query—the primary cost driver under on-demand pricing. By filtering on the partition column, BigQuery intelligently skips entire partitions that don’t match the query criteria, drastically reducing bytes processed. On the Google Professional Cloud DevOps Engineer exam, this concept tests your understanding of cost optimization through data architecture rather than pricing model changes; a common trap is suggesting flat-rate reservations or caching as the primary fix. Remember the memory tip: “Partition to prune, scan to lose”—if you don’t filter on the partition key, you pay for the whole table.
PCDOE Managing Google Cloud costs Practice Question
This PCDOE practice question tests your understanding of managing google cloud costs. 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 company is using BigQuery for analytics and wants to control costs. They have many queries that scan large amounts of data. Which approach is most effective in reducing query costs?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Partition tables by date and use partition pruning in queries.
Partitioning tables by date and using partition pruning in queries directly reduces the amount of data scanned by BigQuery, which is the primary driver of on-demand query costs. By filtering on the partition column, BigQuery can skip entire partitions that do not match the query criteria, minimizing the bytes processed. This is the most effective cost-control measure because it addresses the root cause of high costs—excessive data scanning—without requiring a pricing model change or additional resource commitments.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Switch to flat-rate pricing to cap costs.
Why it's wrong here
Flat-rate pricing caps total cost but does not reduce per-query cost; it may be more expensive for low usage.
✓
Partition tables by date and use partition pruning in queries.
Why this is correct
Partitioning limits the data scanned, reducing query costs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reserve BigQuery slots for dedicated capacity.
Why it's wrong here
Reserving slots is for dedicated capacity, not cost reduction; it may increase costs.
✗
Use clustering to organize data within partitions.
Why it's wrong here
Clustering improves performance but does not directly reduce the amount of data scanned.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that clustering alone is sufficient for cost reduction, but clustering only optimizes data within a partition and cannot skip entire partitions, making partitioning the primary mechanism for cost control.
Detailed technical explanation
How to think about this question
BigQuery charges for on-demand queries based on the number of bytes processed, and partition pruning leverages the table's metadata to read only the relevant storage blocks. When a table is partitioned by a DATE or TIMESTAMP column, BigQuery stores each partition as a separate set of storage blocks; a query with a WHERE clause on that column allows the query engine to skip entire partitions at the storage layer, dramatically reducing I/O and cost. In a real-world scenario, a table with 365 daily partitions scanning only 1 day of data would process approximately 1/365th of the data compared to a full table scan, yielding near-linear cost savings.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Managing Google Cloud costs — This question tests Managing Google Cloud costs — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Partition tables by date and use partition pruning in queries. — Partitioning tables by date and using partition pruning in queries directly reduces the amount of data scanned by BigQuery, which is the primary driver of on-demand query costs. By filtering on the partition column, BigQuery can skip entire partitions that do not match the query criteria, minimizing the bytes processed. This is the most effective cost-control measure because it addresses the root cause of high costs—excessive data scanning—without requiring a pricing model change or additional resource commitments.
What should I do if I get this PCDOE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A DevOps team is analyzing Google Cloud costs and notices that spending on BigQuery has increased significantly. They want to reduce costs without impacting ongoing analytical workloads. Which TWO actions should they take? (Choose two.)
medium
A.Switch to on-demand pricing to pay only for queries run.
B.Enable column-level security to restrict access to sensitive data.
✓ C.Set custom cost controls like query quotas and maximum bytes billed per query.
D.Delete unused datasets to reduce storage costs.
✓ E.Implement flat-rate pricing with reservations for consistent workloads.
Why C: Option C is correct because BigQuery allows you to set custom cost controls such as query quotas (e.g., concurrent queries per project) and maximum bytes billed per query. These controls cap resource usage at the query level, preventing runaway costs while still allowing analytical workloads to run within defined limits. This directly addresses cost spikes without blocking ongoing operations.
Last reviewed: Jun 30, 2026
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