AI-102 Implement generative AI solutions Practice Question
Which TWO actions should you take to reduce the cost of using Azure OpenAI for a chatbot that handles high traffic?
⚠ Common exam trap
Test-takers frequently confuse token-related parameters (max_tokens, temperature) with cost-saving mechanisms, when in reality cost reduction for high-traffic chatbots relies on architectural patterns like batching and caching, not on tweaking inference parameters.
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 the batch API for non-real-time requests.
The batch API allows you to submit non-real-time requests that are processed asynchronously, often at a lower cost per token compared to real-time inference. This is ideal for chatbot scenarios where some queries (e.g., historical analysis or bulk processing) do not require immediate responses, reducing overall Azure OpenAI consumption costs.
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 max_tokens to reduce the number of requests.
Why it's wrong here
Increasing max_tokens increases tokens per request.
- ✓
Use the batch API for non-real-time requests.
Why this is correct
Batch API has lower cost per token.
- ✓
Implement caching for frequently asked questions.
Why this is correct
Caching reduces API calls.
- ✗
Lower the temperature to 0 to reduce token usage.
Why it's wrong here
Temperature does not affect token count.
- ✗
Fine-tune the model to reduce prompt length.
Why it's wrong here
Fine-tuning incurs additional training cost.
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