- A
Switch from file store inspection to content inspection (items) and send each file individually via the API to bypass the file-level quota.
Why wrong: Content inspection has its own quota limits and is not designed for bulk scanning of millions of files; it would likely still hit quota.
- B
Create de-identification templates to automatically redact the found sensitive data, which reduces the need for inspection.
Why wrong: De-identification templates do not reduce inspection load; they are applied after inspection or as a separate transformation.
- C
Submit a quota increase request and pause the inspection job until the increase is approved.
Why wrong: This would stop inspection for weeks, violating the compliance requirement to continue inspecting all files.
- D
Reconfigure the DLP job to use a smaller sampling size (e.g., 'bytesLimitPerFile' or 'filesLimitPercent') to stay within quota limits, and schedule more frequent runs to cover all files over time.
Sampling reduces per-job usage, and more frequent runs ensure eventual full coverage within quota.
Quick Answer
The correct approach is to reconfigure the DLP job to use a smaller sampling size, such as `bytesLimitPerFile` or `filesLimitPercent`, and schedule more frequent runs to cover all files over time. This works because DLP inspection quota management via per-job sampling allows you to reduce the volume of data inspected in a single execution, staying within the quota ceiling while still inspecting every file across multiple job runs. On the Google Professional Cloud Security Engineer exam, this scenario tests your understanding of how to balance quota limits with compliance requirements without requesting a quota increase—a common trap is assuming you must skip files or increase quotas, but the key is leveraging sampling per job, not per file. A useful memory tip: think of it as “small bites, more meals”—reduce each job’s inspection load and increase frequency to eat the whole bucket over time.
PCSE Ensuring data protection Practice Question
This PCSE practice question tests your understanding of ensuring data protection. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 large enterprise is using Cloud Data Loss Prevention (DLP) to inspect a Cloud Storage bucket containing millions of files for sensitive data like credit card numbers and health information. The DLP inspection job is configured to scan the entire bucket with a schedule. Recently, the job has been failing with a 'Quota exceeded' error for the DLP inspect requests quota. The team needs to continue inspecting all files without increasing the quota limit, as the quota increase request would take weeks. They cannot skip any files due to compliance requirements. What should they do to work around the quota limit while inspecting all files?
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
Reconfigure the DLP job to use a smaller sampling size (e.g., 'bytesLimitPerFile' or 'filesLimitPercent') to stay within quota limits, and schedule more frequent runs to cover all files over time.
Option D is correct because by reducing the per-job scan volume (e.g., via 'bytesLimitPerFile' or 'filesLimitPercent'), the job stays within the DLP inspect requests quota while still covering all files over multiple scheduled runs. This approach respects the quota ceiling without skipping any files, as the sampling is applied per job execution, not per file permanently. The team can increase the job frequency to ensure the entire bucket is eventually inspected, meeting compliance requirements.
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 from file store inspection to content inspection (items) and send each file individually via the API to bypass the file-level quota.
Why it's wrong here
Content inspection has its own quota limits and is not designed for bulk scanning of millions of files; it would likely still hit quota.
- ✗
Create de-identification templates to automatically redact the found sensitive data, which reduces the need for inspection.
Why it's wrong here
De-identification templates do not reduce inspection load; they are applied after inspection or as a separate transformation.
- ✗
Submit a quota increase request and pause the inspection job until the increase is approved.
Why it's wrong here
This would stop inspection for weeks, violating the compliance requirement to continue inspecting all files.
- ✓
Reconfigure the DLP job to use a smaller sampling size (e.g., 'bytesLimitPerFile' or 'filesLimitPercent') to stay within quota limits, and schedule more frequent runs to cover all files over time.
Why this is correct
Sampling reduces per-job usage, and more frequent runs ensure eventual full coverage within quota.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that reducing per-job sampling means permanently skipping files, but the key is that sampling is per execution, and multiple runs over time can cover all files without exceeding quota.
Detailed technical explanation
How to think about this question
Cloud DLP inspection quotas are typically measured in bytes scanned per day or number of inspect requests per day, and the 'bytesLimitPerFile' parameter allows capping the amount of data scanned per file, while 'filesLimitPercent' controls the percentage of files scanned per job execution. By reducing these values and increasing the job frequency (e.g., from daily to hourly), the total scanned data per day remains under quota, but the entire bucket is eventually covered. This technique is often used in large-scale environments where quota increases are slow, and it ensures no files are permanently excluded as long as the sampling is random and the schedule is comprehensive.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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Ensuring data protection — study guide chapter
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FAQ
Questions learners often ask
What does this PCSE question test?
Ensuring data protection — This question tests Ensuring data protection — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reconfigure the DLP job to use a smaller sampling size (e.g., 'bytesLimitPerFile' or 'filesLimitPercent') to stay within quota limits, and schedule more frequent runs to cover all files over time. — Option D is correct because by reducing the per-job scan volume (e.g., via 'bytesLimitPerFile' or 'filesLimitPercent'), the job stays within the DLP inspect requests quota while still covering all files over multiple scheduled runs. This approach respects the quota ceiling without skipping any files, as the sampling is applied per job execution, not per file permanently. The team can increase the job frequency to ensure the entire bucket is eventually inspected, meeting compliance requirements.
What should I do if I get this PCSE 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.
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Last reviewed: Jun 30, 2026
This PCSE 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 PCSE exam.
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