Implement generative AI solutions →hardMultiple SelectObjective-mapped

AI-102 Implement generative AI solutions Practice Question

You are deploying a solution that uses Azure OpenAI Service to generate financial reports. You need to ensure the outputs are accurate and consistent. Which TWO parameters should you adjust? (Choose two.)

⚠ Common exam trap

Candidates often confuse parameters that control creativity (temperature, top_p) with those that control repetition (frequency_penalty, presence_penalty) or output length (max_tokens), leading them to select options that do not directly address accuracy and consistency.

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

✓

Set temperature to 0.

Setting temperature to 0 makes the model deterministic, always choosing the most likely next token. This is critical for financial reports where consistency and reproducibility are required, as it eliminates randomness in the output.

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 presence_penalty to 0.5.
Why it's wrong here
Presence penalty encourages new topics, reducing consistency.
✓
Set temperature to 0.
Why this is correct
Low temperature makes output more deterministic.
✗
Set max_tokens to 2000.
Why it's wrong here
Max_tokens doesn't affect accuracy or consistency.
✗
Set frequency_penalty to 0.7.
Why it's wrong here
Frequency penalty reduces repetition, which may affect consistency.
✓
Set top_p to 0.1.
Why this is correct
Low top_p restricts to high-probability tokens, improving consistency.

Go deeper

AI-102 Implement generative AI solutions Practice Question

Why each option matters

Option-by-option breakdown

Related to this question