- A
Implement data masking during preprocessing.
Data masking replaces sensitive information with realistic but fictional data, helping to comply with privacy regulations.
- B
Store all training data in a separate Azure region.
Why wrong: Geographic separation does not automatically ensure compliance; privacy regulations require consent and anonymization.
- C
Use differential privacy during model training.
Differential privacy adds noise to the training process, preventing re-identification of individuals.
- D
Encrypt the model at rest using Azure Key Vault.
Why wrong: Encryption protects against unauthorized access but does not address privacy regulations like GDPR.
- E
Log all raw input data to Azure Monitor for auditing.
Why wrong: Logging raw data may violate privacy regulations by exposing sensitive information.
Quick Answer
The answer is to use differential privacy during model training and apply data masking in preprocessing. Differential privacy works by injecting calibrated noise into the training process, ensuring that the model cannot reveal whether any single individual’s data was included, which directly satisfies data privacy compliance for generative AI on Azure ML. Data masking further anonymizes sensitive fields before they ever reach the model, preventing exposure of personally identifiable information. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding that privacy compliance is about protecting individual data points, not just securing storage—encryption at rest (option E) addresses security, not privacy, and storing data in a different region (option C) does not alter privacy obligations. A common trap is confusing security controls with privacy techniques; remember that privacy requires obscuring the data itself, not just locking it away. Memory tip: “Mask and noise—privacy’s two joys.”
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 TWO actions should you take to ensure that a generative AI model deployed on Azure Machine Learning is compliant with data privacy regulations?
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
Implement data masking during preprocessing.
Options A and D are correct. A: Data masking in preprocessing helps anonymize sensitive data. D: Differential privacy adds noise to protect individual data points. B is wrong because exposing raw data is not compliant. C is wrong because storing data in a different region does not address privacy. E is wrong because encryption at rest is about security, not privacy compliance.
Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement data masking during preprocessing.
Why this is correct
Data masking replaces sensitive information with realistic but fictional data, helping to comply with privacy regulations.
Related concept
CIDR notation defines the prefix length.
- ✗
Store all training data in a separate Azure region.
Why it's wrong here
Geographic separation does not automatically ensure compliance; privacy regulations require consent and anonymization.
- ✓
Use differential privacy during model training.
Why this is correct
Differential privacy adds noise to the training process, preventing re-identification of individuals.
Related concept
CIDR notation defines the prefix length.
- ✗
Encrypt the model at rest using Azure Key Vault.
Why it's wrong here
Encryption protects against unauthorized access but does not address privacy regulations like GDPR.
- ✗
Log all raw input data to Azure Monitor for auditing.
Why it's wrong here
Logging raw data may violate privacy regulations by exposing sensitive information.
Common exam traps
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Detailed technical explanation
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Key takeaway
Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related AI-102 subnetting questions on CIDR, address ranges, and subnet selection.
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement generative AI solutions — This question tests Implement generative AI solutions — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: Implement data masking during preprocessing. — Options A and D are correct. A: Data masking in preprocessing helps anonymize sensitive data. D: Differential privacy adds noise to protect individual data points. B is wrong because exposing raw data is not compliant. C is wrong because storing data in a different region does not address privacy. E is wrong because encryption at rest is about security, not privacy compliance.
What should I do if I get this AI-102 question wrong?
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related AI-102 subnetting questions on CIDR, address ranges, and subnet selection.
What is the key concept behind this question?
CIDR notation defines the prefix length.
About these practice questions
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Last reviewed: Jun 20, 2026
This AI-102 practice question is part of Courseiva's free Microsoft 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 AI-102 exam.
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