Quick Answer
The answer is federated learning, differential privacy, and data anonymization. These three techniques are effective for ensuring data privacy in AI training because they directly prevent the model from exposing individual-level information. Differential privacy injects calibrated noise into the training process so that the model’s output cannot be traced back to any single record, while data anonymization strips out personally identifiable information (PII) before the data is used. Federated learning keeps raw data on local devices, sharing only encrypted model updates, which means sensitive data never leaves its source. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish between privacy-by-design methods and general security controls. A common trap is confusing data encryption at rest with privacy preservation—encryption protects stored data but does nothing to prevent inference attacks during training. Similarly, data replication increases exposure rather than protecting it. For a quick memory tip, think of the acronym FAD: Federated learning keeps data local, Anonymization removes identifiers, and Differential privacy adds noise.
This AI0-001 practice question tests your understanding of ai security, ethics and governance. 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.
Which THREE are effective methods for ensuring data privacy in AI training? (Choose three.)
Data encryption at rest
Why wrong: Encryption protects storage but not inference attacks.
Data anonymization
Removes personally identifiable information.
Differential privacy
Adds noise to protect individual data.
Data replication
Why wrong: Replication increases exposure.
Federated learning
Trains without centralizing data.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Data anonymization
Options A (Differential privacy), C (Data anonymization), and D (Federated learning) are correct. Differential privacy adds noise to protect individual records, anonymization removes PII, and federated learning trains without centralizing data. Option B (Data encryption at rest) protects storage but not against model inference. Option E (Data replication) increases risk.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Data encryption at rest
Why it's wrong here
Encryption protects storage but not inference attacks.
Data anonymization
Why this is correct
Removes personally identifiable information.
Related concept
Read the scenario before looking for a memorised answer.
Differential privacy
Why this is correct
Adds noise to protect individual data.
Related concept
Read the scenario before looking for a memorised answer.
Data replication
Why it's wrong here
Replication increases exposure.
Federated learning
Why this is correct
Trains without centralizing data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
The correct answer is: Data anonymization — Options A (Differential privacy), C (Data anonymization), and D (Federated learning) are correct. Differential privacy adds noise to protect individual records, anonymization removes PII, and federated learning trains without centralizing data. Option B (Data encryption at rest) protects storage but not against model inference. Option E (Data replication) increases risk.
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 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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