AI-102 Implement generative AI solutions Practice Question
A financial services firm wants to use Azure OpenAI to generate investment advice summaries. They must ensure that the model does not produce any advice that could be interpreted as personalized financial advice. What is the most effective strategy?
⚠ Common exam trap
Candidates often assume deterministic parameters (temperature=0, top_p=0) guarantee safe outputs, but they only control randomness, not content compliance—Azure's system message and content filtering are the correct tools for enforcing content policies.
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
✓
Use a system message that instructs the model to avoid personalized advice and apply strict content filtering.
Azure OpenAI's system messages allow you to set the model's behavior and constraints at the conversation level, which is the most direct and effective way to enforce a policy like avoiding personalized financial advice. Combined with Azure's content filtering (which can block harmful or restricted content), this approach provides both instruction-based and filter-based guardrails without requiring model retraining or relying solely on example-based prompting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set temperature to 0 and top_p to 0 to make outputs deterministic.
Why it's wrong here
Temperature and top_p control sampling randomness, not content policy; deterministic output can still contain personalised advice. The requirement is content filtering, met by Azure OpenAI's responsible AI filters or a system message restricting scope. Determinism suits reproducible testing or classification, where identical inputs must yield identical outputs.
- ✓
Use a system message that instructs the model to avoid personalized advice and apply strict content filtering.
Why this is correct
A system message sets persistent behavioural boundaries, instructing the model to decline personalised financial advice, while content filtering blocks prohibited outputs. Together they satisfy the stem's constraint that no output be interpretable as personalised investment advice.
- ✗
Provide few-shot examples of disclaimers in the prompt.
Why it's wrong here
Few-shot disclaimers only shape phrasing; the model can still emit personalised recommendations, so the constraint is not enforced. Few-shot prompting suits steering format or tone. Azure OpenAI content filters with a custom blocklist reliably block personalised financial advice.
- ✗
Fine-tune the model on a dataset of generic financial summaries.
Why it's wrong here
Fine-tuning on generic summaries alters tone and style but does not reliably prevent personalised advice; the model can still generate it. Fine-tuning suits adapting output format or domain vocabulary. Content filtering and system-message guardrails are what actually block prohibited advice.
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