- A
Pseudonymisation by replacing patient names with random IDs
Why wrong: Pseudonymisation is reversible and can be combined with other data to re-identify individuals; it does not satisfy HIPAA's de-identification requirements.
- B
Data augmentation to create synthetic samples
Why wrong: Synthetic data can still leak information from the original data and does not provide formal privacy guarantees.
- C
Differential privacy with a carefully chosen epsilon
Differential privacy adds controlled noise to protect individual records, meeting HIPAA's de-identification standards with formal guarantees.
- D
Data minimisation by removing all features except age and gender
Why wrong: Removing features may still allow re-identification and does not provide formal privacy guarantees required by HIPAA.
AI0-001 AI Governance and Ethics Practice Question
This AI0-001 practice question tests your understanding of ai governance and ethics. 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 healthcare AI system uses patient data to predict disease risk. To comply with HIPAA and reduce the risk of re-identification, which technique should be applied to the training data before model development?
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
Differential privacy with a carefully chosen epsilon
Differential privacy (Option C) is the correct technique because it adds calibrated noise to the training data or model outputs, providing a mathematical guarantee against re-identification even if an attacker has auxiliary information. This directly addresses HIPAA's requirement to protect patient privacy while preserving statistical utility for disease risk prediction.
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.
- ✗
Pseudonymisation by replacing patient names with random IDs
Why it's wrong here
Pseudonymisation is reversible and can be combined with other data to re-identify individuals; it does not satisfy HIPAA's de-identification requirements.
- ✗
Data augmentation to create synthetic samples
Why it's wrong here
Synthetic data can still leak information from the original data and does not provide formal privacy guarantees.
- ✓
Differential privacy with a carefully chosen epsilon
Why this is correct
Differential privacy adds controlled noise to protect individual records, meeting HIPAA's de-identification standards with formal guarantees.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data minimisation by removing all features except age and gender
Why it's wrong here
Removing features may still allow re-identification and does not provide formal privacy guarantees required by HIPAA.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that pseudonymisation (Option A) is equivalent to de-identification under HIPAA, when in fact it is a reversible process that fails against linkage attacks, making differential privacy the only mathematically rigorous option.
Detailed technical explanation
How to think about this question
Differential privacy works by adding random noise (e.g., Laplace or Gaussian) to query results or training gradients, controlled by the privacy budget epsilon (ε). A smaller ε provides stronger privacy but reduces accuracy; for healthcare, ε values between 0.1 and 1 are common. In practice, the technique must be applied during training (e.g., DP-SGD) to ensure that the model itself does not memorise individual patient records, which is a subtle failure mode when noise is only added to the final output.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Governance and Ethics — This question tests AI Governance and Ethics — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Differential privacy with a carefully chosen epsilon — Differential privacy (Option C) is the correct technique because it adds calibrated noise to the training data or model outputs, providing a mathematical guarantee against re-identification even if an attacker has auxiliary information. This directly addresses HIPAA's requirement to protect patient privacy while preserving statistical utility for disease risk prediction.
What should I do if I get this AI0-001 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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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