CDPSE Privacy Engineering Practice Question
A privacy engineer is implementing differential privacy in a data analytics pipeline. To ensure the privacy budget remains intact over multiple queries, which technique should be applied?
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
✓
Utilizing composition theorems to track cumulative privacy loss
Composition theorems in differential privacy allow for the tracking of the cumulative privacy loss (epsilon) as multiple queries are performed on the same dataset.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implementing k-anonymity on the original dataset
Why it's wrong here
k-anonymity is a generalization technique, not a method for managing privacy budgets in differential privacy.
- ✗
Increasing the sensitivity of the query function
Why it's wrong here
Increasing sensitivity actually makes it harder to maintain a privacy budget.
- ✓
Utilizing composition theorems to track cumulative privacy loss
Why this is correct
Composition theorems provide the mathematical framework to calculate the total privacy budget spent.
- ✗
Applying a fixed noise distribution regardless of query count
Why it's wrong here
This does not account for the cumulative privacy leakage over multiple queries.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official ISACA exam blueprint
This CDPSE practice question is part of Courseiva's free ISACA 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 CDPSE exam.