Correct Answer: Implement a Semantic Caching layer (e.g., using Redis) to store and retrieve past completions based on prompt vector similarity
Semantic caching stores past user prompts and completions. When a new prompt is received, it generates an embedding and checks similarity. If there's a highly similar query, it ret...
Correct Answer
Implement a Semantic Caching layer (e.g., using Redis) to store and retrieve past completions based on prompt vector similarity
Why This Answer Is Correct
Semantic caching stores past user prompts and completions. When a new prompt is received, it generates an embedding and checks similarity. If there's a highly similar query, it returns the cached completion, bypassing the OpenAI API to save tokens and minimize latency.
This question evaluates your knowledge of Azure OpenAI Service — a core concept tested in Microsoft Certified: Azure AI Engineer Associate (AI-102). Understanding Azure OpenAI Service is essential for designing scalable, secure, and cost-effective cloud architectures. Mastery of this topic will help you answer scenario-based questions confidently on exam day.
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