Quick Answer
The answer is to set temperature to 0 and top_p to 1. This configuration forces the foundation model to always select the single highest-probability token at each generation step, eliminating all randomness from the sampling process and making the output deterministic and repeatable. For the AWS Certified AI Practitioner AIF-C01 exam, this scenario tests your understanding of how inference parameters control creativity versus consistency—a critical distinction for regulated industries like finance where auditing requires identical outputs from identical inputs. A common trap is assuming top_p alone can ensure determinism, but without temperature at 0, the model can still sample from a narrow pool of high-probability tokens. Remember the mnemonic “Zero heat, full pool” to recall that temperature must be zeroed out while top_p remains at its maximum of 1 to lock in deterministic behavior.
This AIF-C01 practice question tests your understanding of applications of foundation models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Set temperature to 0.1 and top_p to 0.9.
Why wrong: Still allows some randomness; not fully deterministic.
Set temperature to 0.7 and top_p to 1.0.
Why wrong: Higher temperature increases variability.
Set temperature to 0.5 and top_p to 0.5.
Why wrong: Both parameters introduce randomness.
Set temperature to 0 and top_p to 1.
Temperature 0 makes the model deterministic.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Temperature controls the probability distribution's sharpness: at 0, the model always picks the argmax token, making output deterministic. Top_p (nucleus sampling) selects from the smallest set of tokens whose cumulative probability exceeds p; at 1.0, it includes all tokens, but with temperature 0, the top_p setting is irrelevant because the model always chooses the single highest-probability token. In practice, even temperature 0.0001 can cause different outputs across runs due to floating-point precision or hardware differences, so strict 0 is required for true determinism.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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FAQ
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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