- A
Differential privacy
Differential privacy adds noise during training to bound the influence of any single data point, protecting individual privacy.
- B
Federated learning
Why wrong: Federated learning trains across decentralized data but doesn't inherently prevent memorization; it still risks leakage from gradients.
- C
Homomorphic encryption
Why wrong: Homomorphic encryption allows computation on encrypted data but doesn't address memorization during training.
- D
Model quantization
Why wrong: Quantization reduces model size and speeds up inference but does not provide privacy guarantees.
Question 981 of 1,000
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 scientist is training a model to detect fraudulent transactions. To protect customer privacy, the team wants to ensure that the model does not inadvertently memorize and reveal sensitive information about individuals in the training set. Which technique should be applied during training?
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 output, ensuring that the model cannot infer whether any specific individual's data was included in the training set. This directly addresses the goal of preventing memorization and leakage of sensitive information while still allowing the model to learn useful patterns for fraud detection.
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
Differential privacy adds noise during training to bound the influence of any single data point, protecting individual privacy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Federated learning
Why it's wrong here
Federated learning trains across decentralized data but doesn't inherently prevent memorization; it still risks leakage from gradients.
- ✗
Homomorphic encryption
Why it's wrong here
Homomorphic encryption allows computation on encrypted data but doesn't address memorization during training.
- ✗
Model quantization
Why it's wrong here
Quantization reduces model size and speeds up inference but does not provide privacy guarantees.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that federated learning alone guarantees privacy, when in fact it only addresses data locality and must be combined with differential privacy to prevent 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 gradients or query responses, with the noise magnitude calibrated to the sensitivity of the function and the privacy budget (epsilon, ε). In practice, a lower ε (e.g., 0.1–1.0) provides stronger privacy but may reduce model accuracy, requiring careful tuning for fraud detection where both privacy and precision are critical.
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 is the correct technique because it adds calibrated noise to the training process or output, ensuring that the model cannot infer whether any specific individual's data was included in the training set. This directly addresses the goal of preventing memorization and leakage of sensitive information while still allowing the model to learn useful patterns for fraud detection.
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
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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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