The answer is to set the sample method to 'RANDOM' with a percentage. This configuration is correct because the Cloud DLP API’s `sample_method` parameter, when set to `RANDOM` along with a `sampling_percentage`, instructs the service to inspect only a statistically random subset of rows from the data source, directly reducing the volume scanned and thus lowering Cloud DLP costs. On the Google Professional Data Engineer exam, this question tests your understanding of cost optimization strategies for sensitive data inspection, often appearing as a scenario where a company needs to balance budget constraints with compliance requirements. A common trap is confusing random sampling with top-N or hash-based methods, which do not provide the same unbiased coverage for cost reduction. Memory tip: think “Random for Reduction”—random sampling cuts costs while keeping your discovery statistically sound.
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 media company uses Cloud Data Loss Prevention (DLP) API to inspect and de-identify sensitive data before loading into BigQuery. They want to reduce costs by sampling the data during inspection. Which configuration should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set the sample method to 'RANDOM' with a percentage.
Option B is correct because the Cloud DLP API supports a 'sample_method' of 'RANDOM' with a 'sampling_percentage' to inspect only a random subset of rows. This directly reduces the volume of data scanned, lowering costs while still providing statistically representative coverage for sensitive data discovery.
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.
✗
Use the 'ROWS' limit in the inspection job.
Why it's wrong here
ROWS limit restricts total rows but does not sample uniformly.
✓
Set the sample method to 'RANDOM' with a percentage.
Why this is correct
DLP supports random sampling to inspect a subset of data, reducing cost.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a hybrid inspection with a BigQuery sample table.
Why it's wrong here
Hybrid inspection may add extra steps and is not primarily for cost reduction.
✗
Use the 'BYTES_LIMIT' parameter.
Why it's wrong here
BYTES_LIMIT sets a cap on scanned bytes but not a sampling ratio.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'limiting rows/bytes' (which scans sequentially from the start) with 'random sampling' (which distributes inspection across the entire dataset), leading them to pick options A or D, which do not achieve representative cost reduction.
Detailed technical explanation
How to think about this question
Under the hood, Cloud DLP's random sampling uses a deterministic hash of the row identifier to select rows, ensuring consistent sampling across multiple runs. This is crucial for auditability and reproducibility. In a real-world scenario, a media company with petabytes of user-generated content can set a 10% random sample to detect patterns of PII (e.g., credit card numbers) across the entire dataset without scanning every row, balancing cost and coverage.
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.
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set the sample method to 'RANDOM' with a percentage. — Option B is correct because the Cloud DLP API supports a 'sample_method' of 'RANDOM' with a 'sampling_percentage' to inspect only a random subset of rows. This directly reduces the volume of data scanned, lowering costs while still providing statistically representative coverage for sensitive data discovery.
What should I do if I get this PDE 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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