Question 665 of 1,000
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AI0-001 AI Security Practice Question

This AI0-001 practice question tests your understanding of ai security. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data science team needs to implement privacy-preserving ML for a healthcare model. They require that individual patient records cannot be distinguished in the training output. 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

Differential privacy

Differential privacy is the correct technique because it adds calibrated noise to the training process or query outputs, ensuring that the inclusion or exclusion of any single patient record does not significantly affect the model's output. This provides a formal mathematical guarantee that individual records cannot be distinguished, which directly meets the requirement for privacy-preserving ML in healthcare.

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.

  • Differential privacy

    Why this is correct

    Correct. Differential privacy provides formal guarantees against membership inference.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Homomorphic encryption

    Why it's wrong here

    Homomorphic encryption protects data in use but does not prevent membership inference.

  • Model pruning

    Why it's wrong here

    Pruning reduces model size, not privacy.

  • Federated learning

    Why it's wrong here

    Federated learning keeps data local but does not guarantee indistinguishability.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Cisco often tests the misconception that federated learning alone provides privacy, but without differential privacy, federated learning can still leak individual patient data through model inversion or membership inference attacks.

Detailed technical explanation

How to think about this question

Differential privacy works by adding noise drawn from a Laplace or Gaussian distribution to the output of a function (e.g., model weights or query results), with the noise scale calibrated to the sensitivity of the function and the privacy budget epsilon (ε). A lower ε provides stronger privacy but reduces accuracy, requiring careful tuning for healthcare models where both privacy and predictive performance are critical. In practice, techniques like DP-SGD (Differentially Private Stochastic Gradient Descent) clip gradients and add noise during training to achieve differential privacy guarantees.

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 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 is the correct technique because it adds calibrated noise to the training process or query outputs, ensuring that the inclusion or exclusion of any single patient record does not significantly affect the model's output. This provides a formal mathematical guarantee that individual records cannot be distinguished, which directly meets the requirement for privacy-preserving ML in healthcare.

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.

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Last reviewed: Jul 4, 2026

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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.