- A
Data augmentation
Why wrong: Data augmentation expands training data but does not protect privacy.
- B
Differential privacy
Differential privacy limits information leakage about individuals.
- C
Model quantization
Why wrong: Model quantization reduces model size but does not provide privacy guarantees.
- D
Federated learning
Federated learning keeps data on local devices, sharing only model updates.
- E
Dropout regularization
Why wrong: Dropout prevents overfitting but does not address privacy.
Question 122 of 1,000
AI0-001 AI Security Practice Question
This AI0-001 practice question tests your understanding of ai security. 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 organization is deploying an AI model to predict patient readmission risk. They must comply with regulations that protect patient privacy. Which TWO techniques should they implement to enhance privacy preservation?
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 (B) is correct because it adds calibrated noise to the training data or model outputs, ensuring that the inclusion or exclusion of any single patient's record does not significantly affect the model's predictions. This provides a formal mathematical guarantee of privacy, which is essential for complying with regulations like HIPAA that protect patient data.
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.
- ✗
Data augmentation
Why it's wrong here
Data augmentation expands training data but does not protect privacy.
- ✓
Differential privacy
Why this is correct
Differential privacy limits information leakage about individuals.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Model quantization
Why it's wrong here
Model quantization reduces model size but does not provide privacy guarantees.
- ✓
Federated learning
Why this is correct
Federated learning keeps data on local devices, sharing only model updates.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Dropout regularization
Why it's wrong here
Dropout prevents overfitting but does not address privacy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that any regularization or optimization technique (like dropout or quantization) can provide privacy, when in fact only methods that explicitly limit information leakage (like differential privacy and federated learning) are designed for that purpose.
Detailed technical explanation
How to think about this question
Differential privacy works by adding noise drawn from a Laplace or Gaussian distribution, with the noise magnitude calibrated to the model's sensitivity (the maximum change in output due to a single data point). Federated learning (D) complements this by keeping patient data on local devices (e.g., hospital servers) and only sharing model updates (gradients) with a central server, which can then be further privatized using differential privacy before aggregation. In a real-world scenario, a hospital network might use federated learning to train a readmission risk model across multiple sites without ever centralizing sensitive patient records, then apply differential privacy to the aggregated model to prevent inference attacks on individual patients.
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.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security — This question tests AI Security — 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 (B) is correct because it adds calibrated noise to the training data or model outputs, ensuring that the inclusion or exclusion of any single patient's record does not significantly affect the model's predictions. This provides a formal mathematical guarantee of privacy, which is essential for complying with regulations like HIPAA that protect patient data.
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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