- A
Data encryption at rest
Why wrong: Encryption protects storage but not inference attacks.
- B
Data anonymization
Removes personally identifiable information.
- C
Differential privacy
Adds noise to protect individual data.
- D
Data replication
Why wrong: Replication increases exposure.
- E
Federated learning
Trains without centralizing data.
Question 123 of 1,000
Data Privacy Techniques for AI Training
This AI0-001 practice question tests your understanding of ai security, ethics and governance. 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.
Which THREE are effective methods for ensuring data privacy in AI training? (Choose three.)
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.
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
✓
Data anonymization
Data anonymization (B) is correct because it removes or obfuscates personally identifiable information (PII) from training datasets, ensuring that individuals cannot be re-identified. This is a foundational privacy technique that directly addresses regulatory requirements like GDPR and CCPA by breaking the link between data and specific individuals.
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 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
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between security controls (like encryption) and privacy-preserving techniques, trapping candidates who confuse data protection at rest with privacy during model training.
Detailed technical explanation
How to think about this question
Differential privacy (C) works by adding calibrated noise to gradients or query outputs, typically using mechanisms like the Laplace or Gaussian mechanism with a privacy budget (ε) parameter. Federated learning (E) keeps raw data on local devices, sharing only model updates (e.g., weight gradients) with a central server, which can be further secured with secure aggregation protocols to prevent inference of individual contributions. In practice, combining differential privacy with federated learning provides layered protection against membership inference attacks.
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, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Data anonymization — Data anonymization (B) is correct because it removes or obfuscates personally identifiable information (PII) from training datasets, ensuring that individuals cannot be re-identified. This is a foundational privacy technique that directly addresses regulatory requirements like GDPR and CCPA by breaking the link between data and specific individuals.
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 →
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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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