The correct two practices are including a system message that instructs the model to follow secure coding practices and using Azure AI Content Safety to filter the generated output. The system message acts as a foundational guardrail, explicitly directing the Azure OpenAI Service to avoid insecure patterns like hardcoded credentials or SQL injection, while Azure AI Content Safety provides a dedicated, policy-driven layer that scans the generated code snippets for harmful content and security vulnerabilities, allowing you to set custom severity thresholds and blocklists. On the AI-102 exam, this pairing tests your understanding of defense-in-depth for generative AI: the model’s instruction-based safety versus an external content filter. A common trap is choosing only one—either relying solely on the system message or assuming the model inherently avoids vulnerabilities. Remember the mnemonic “Message then Filter” to recall that the system message sets the tone, and Content Safety enforces the policy before output reaches the user.
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.
You are using Azure OpenAI Service to generate code snippets. The output must be safe and free of security vulnerabilities. Which TWO practices should you follow? (Select TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Azure AI Content Safety to filter outputs
Option C is correct because Azure AI Content Safety is a dedicated service that provides an additional layer of filtering for harmful or inappropriate content, including security vulnerabilities, beyond what the model itself offers. It allows you to define custom severity thresholds and blocklists, ensuring that generated code snippets are safe before they reach the user. This is a recommended practice for production deployments to mitigate risks like injection attacks or exposure of sensitive patterns.
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.
✗
Increase the temperature parameter to encourage diversity
Why it's wrong here
Higher temperature may increase errors and security risks.
✗
Rely on the model's built-in safety features
Why it's wrong here
Built-in safety is not sufficient; additional measures are needed.
✓
Use Azure AI Content Safety to filter outputs
Why this is correct
Content safety can detect and block harmful code.
Related concept
Read the scenario before looking for a memorised answer.
✗
Fine-tune the model with a dataset of secure code examples
Why it's wrong here
Fine-tuning is costly and may not guarantee security for all prompts.
✓
Include a system message instructing the model to follow secure coding practices
Why this is correct
Guides the model to generate secure code.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume the model's built-in safety features are sufficient (Option B) or that increasing temperature (Option A) improves safety by adding randomness, when in fact Azure AI Content Safety is the explicit, exam-tested tool for output filtering in generative AI solutions.
Detailed technical explanation
How to think about this question
Azure AI Content Safety operates by analyzing text for categories such as hate, self-harm, sexual, and violence content, but it can also be extended to detect code-specific risks via custom blocklists or integration with tools like Microsoft Defender for Cloud. Under the hood, it uses multi-class classification models with configurable severity levels (0-7) to filter outputs in real-time, reducing latency compared to post-processing scans. In a real-world scenario, a developer might use it to block generated code containing dangerous functions like `eval()` or `exec()` in Python, which the base model might otherwise produce.
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 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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Azure AI Content Safety to filter outputs — Option C is correct because Azure AI Content Safety is a dedicated service that provides an additional layer of filtering for harmful or inappropriate content, including security vulnerabilities, beyond what the model itself offers. It allows you to define custom severity thresholds and blocklists, ensuring that generated code snippets are safe before they reach the user. This is a recommended practice for production deployments to mitigate risks like injection attacks or exposure of sensitive patterns.
What should I do if I get this AI-102 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.
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