- A
Anonymization
Why wrong: Anonymization strips identifiers, but advanced re-identification attacks can still succeed.
- B
Pseudonymization
Why wrong: Pseudonymization replaces identifiers with pseudonyms, but data can often be re-identified.
- C
Differential privacy
Differential privacy provides a mathematical guarantee against re-identification and is suitable for healthcare AI.
- D
Data minimization
Why wrong: Data minimization reduces the amount of data collected, but may not prevent re-identification of individuals.
Question 841 of 1,000
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 startup is developing a diagnostic tool that uses patient data to predict disease risk. To comply with HIPAA and minimize privacy risks while still training accurate models, which privacy-preserving technique should they prioritize?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Differential privacy is the correct choice because it provides a formal mathematical guarantee that the output of a model does not reveal whether any individual's data was included in the training set. This is essential for HIPAA compliance as it prevents re-identification attacks even when an adversary has auxiliary information. Unlike anonymization or pseudonymization, differential privacy adds calibrated noise to the training process or query results, ensuring strong privacy protection while preserving model utility.
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.
- ✗
Anonymization
Why it's wrong here
Anonymization strips identifiers, but advanced re-identification attacks can still succeed.
- ✗
Pseudonymization
Why it's wrong here
Pseudonymization replaces identifiers with pseudonyms, but data can often be re-identified.
- ✓
Differential privacy
Why this is correct
Differential privacy provides a mathematical guarantee against re-identification and is suitable for healthcare AI.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data minimization
Why it's wrong here
Data minimization reduces the amount of data collected, but may not prevent re-identification of individuals.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that anonymization or pseudonymization are sufficient for HIPAA compliance in AI contexts, but the trap here is that these techniques do not protect against inference attacks or re-identification in high-dimensional data, whereas differential privacy provides a provable mathematical guarantee.
Detailed technical explanation
How to think about this question
Differential privacy works by adding random noise (e.g., Laplace or Gaussian noise) to the gradient updates during training or to query responses, calibrated to the sensitivity of the function and the privacy budget epsilon (ε). A lower ε provides stronger privacy but may reduce model accuracy, requiring careful tuning. In healthcare, this is critical because models trained on patient data can inadvertently memorize rare conditions or unique patient records, and differential privacy ensures that even if an attacker has all but one record, they cannot confidently infer the missing record's information.
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 — Differential privacy is the correct choice because it provides a formal mathematical guarantee that the output of a model does not reveal whether any individual's data was included in the training set. This is essential for HIPAA compliance as it prevents re-identification attacks even when an adversary has auxiliary information. Unlike anonymization or pseudonymization, differential privacy adds calibrated noise to the training process or query results, ensuring strong privacy protection while preserving model utility.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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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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