Q
A company has a regulated financial services application that uses AI to assist internal analysts with risk assessments. The AI responses must follow strict domain-specific terminology, consistent reasoning patterns, and predefined decision criteria that rarely change.
You need to implement the solution by using Microsoft Foundry. Accuracy and consistency across responses are more important than responding to frequently changing source content.
How should you implement the AI capability to meet the requirements?
Question Info
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Explanation
Objective:
1.2 Design overall AI strategy for business solutions
What This Item Tests:
Determine when custom AI models should be created
Additional Reading:
Rationale:
Creating a custom fine-tuned model is appropriate when the AI must consistently apply stable, domain-specific terminology and reasoning patterns that do not change frequently, which improves reliability and reduces dependence on complex prompts at runtime. RAG is better suited for scenarios where answers depend on frequently changing source content rather than fixed decision logic. Few-shot prompting can help guide responses but does not provide the same level of consistency as fine-tuning, and increasing temperature reduces determinism rather than improving accuracy for regulated decision-making.
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