- A
Apply data anonymization techniques to the training dataset.
Correct. Anonymizing the training dataset removes patient identities, preventing the model from associating outcomes with specific individuals, including those who opted out.
- B
Implement role-based access control (RBAC) on the AI model's inference API.
Why wrong: Incorrect. RBAC limits who can query the model but does not prevent the model from returning sensitive data that it has memorized.
- C
Use differential privacy during model training.
Why wrong: Incorrect. Differential privacy provides a mathematical guarantee of privacy by adding noise, but it does not explicitly remove data of opt-out patients; the model may still leak information if not properly calibrated.
- D
Encrypt the training data at rest and in transit.
Why wrong: Incorrect. Encryption protects data confidentiality during storage and transmission, but once the model is trained, it can still output sensitive information from the training data.
AI0-001 AI Security, Ethics and Governance Practice Question
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.
A healthcare organization deploys an AI system to analyze medical images and detect anomalies. During a routine audit, the security team discovers that the AI model occasionally returns results that include data from patients who have opted out of data sharing. Which security control should be implemented to prevent this violation?
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
Apply data anonymization techniques to the training dataset.
Data anonymization techniques applied to the training dataset remove personally identifiable information (PII) and ensure that data from patients who opted out of data sharing cannot be reconstructed in model outputs. This directly prevents the violation of returning data from opt-out patients. Role-based access control (RBAC) on the inference API controls who can access the model but does not prevent the model from leaking sensitive data. Differential privacy adds noise to training or queries to protect individual contributions, but it does not guarantee removal of specific opt-out data; it may still allow leakage if the model memorizes. Encryption protects data in transit and at rest but does not affect model outputs. Therefore, option A is the most effective control for this specific violation.
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.
- ✓
Apply data anonymization techniques to the training dataset.
Why this is correct
Correct. Anonymizing the training dataset removes patient identities, preventing the model from associating outcomes with specific individuals, including those who opted out.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Implement role-based access control (RBAC) on the AI model's inference API.
Why it's wrong here
Incorrect. RBAC limits who can query the model but does not prevent the model from returning sensitive data that it has memorized.
- ✗
Use differential privacy during model training.
Why it's wrong here
Incorrect. Differential privacy provides a mathematical guarantee of privacy by adding noise, but it does not explicitly remove data of opt-out patients; the model may still leak information if not properly calibrated.
- ✗
Encrypt the training data at rest and in transit.
Why it's wrong here
Incorrect. Encryption protects data confidentiality during storage and transmission, but once the model is trained, it can still output sensitive information from the training data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Trap categories for this question
Command / output trap
Incorrect. Encryption protects data confidentiality during storage and transmission, but once the model is trained, it can still output sensitive information from the training data.
Detailed technical explanation
How to think about this question
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.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Quick reference
Access Control Model Comparison
| Model | Acronym | Who Controls Access? | Best For |
|---|---|---|---|
| Discretionary Access Control | DAC | Resource owner | Small teams, file shares |
| Mandatory Access Control | MAC | System / security labels | Classified govt / military |
| Role-Based Access Control | RBAC | Administrator (via roles) | Enterprise environments |
| Attribute-Based Access Control | ABAC | Policy engine (user + resource attributes) | Fine-grained, dynamic policies |
| Rule-Based Access Control | RuBAC | System rules / ACLs | Firewall rules, network ACLs |
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: Apply data anonymization techniques to the training dataset. — Data anonymization techniques applied to the training dataset remove personally identifiable information (PII) and ensure that data from patients who opted out of data sharing cannot be reconstructed in model outputs. This directly prevents the violation of returning data from opt-out patients. Role-based access control (RBAC) on the inference API controls who can access the model but does not prevent the model from leaking sensitive data. Differential privacy adds noise to training or queries to protect individual contributions, but it does not guarantee removal of specific opt-out data; it may still allow leakage if the model memorizes. Encryption protects data in transit and at rest but does not affect model outputs. Therefore, option A is the most effective control for this specific violation.
What should I do if I get this AI0-001 question wrong?
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.
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: Jun 22, 2026
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