AIF-C01 Applications of Foundation Models Practice Question

A financial services company is deploying a foundation model to analyze customer sentiment from call transcripts. The model outputs must be consistent and deterministic for auditing purposes. Which parameter configuration should the company use?

⚠ Common exam trap

AWS often tests the misconception that low temperature (e.g., 0.1) is 'deterministic enough,' but only temperature exactly 0 guarantees deterministic outputs, and top_p must be 1 to avoid interfering with the argmax selection.

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 and top_p to 1.

Setting temperature to 0 and top_p to 1 forces the model to always select the highest-probability token at each step, producing deterministic and repeatable outputs. This is essential for auditing and compliance in financial services, where consistency is required. Any nonzero temperature introduces randomness, which undermines determinism.

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 0.1 and top_p to 0.9.
Why it's wrong here
Still allows some randomness; not fully deterministic.
✗
Set temperature to 0.7 and top_p to 1.0.
Why it's wrong here
Higher temperature increases variability.
✗
Set temperature to 0.5 and top_p to 0.5.
Why it's wrong here
Both parameters introduce randomness.
✓
Set temperature to 0 and top_p to 1.
Why this is correct
Temperature 0 makes the model deterministic.

Go deeper

AIF-C01 Applications of Foundation Models Practice Question

Why each option matters

Option-by-option breakdown

Related to this question