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AIF-C01 Practice Question: A developer is using Amazon Bedrock to generate…
A developer is using Amazon Bedrock to generate product descriptions. The developer notices that the model sometimes outputs descriptions that contradict the provided product specifications. Which parameter adjustment would MOST directly reduce factual inconsistencies?
⚠ Common exam trap
AWS often tests the misconception that increasing output length (maxTokens) or expanding token selection (topP, topK) improves accuracy, when in fact these parameters increase variability and the risk of factual errors, whereas lowering temperature is the direct control for reducing randomness.
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
✓
Decrease temperature to a value close to 0
Decreasing temperature to a value close to 0 makes the model more deterministic and less creative, which reduces the likelihood of generating random or contradictory content. In Amazon Bedrock, temperature controls the randomness of token selection; lower values cause the model to choose the most probable tokens, aligning outputs more closely with the provided product specifications and minimizing factual inconsistencies.
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 maxTokens to allow longer descriptions
Why it's wrong here
maxTokens caps output length only; it neither grounds the model in the supplied specifications nor reduces contradiction. Longer outputs can even add unsupported claims. Raising maxTokens is correct when responses are being truncated mid-sentence and the task needs complete descriptions, not factual alignment.
- ✓
Decrease temperature to a value close to 0
Why this is correct
Lowering temperature sharpens the token probability distribution, so the model samples near-deterministic, highest-likelihood continuations rather than exploring alternatives. This directly constrains the randomness that produces specification-contradicting output, satisfying the stem's requirement to reduce factual inconsistency most directly.
- ✗
Increase topP to 1.0
Why it's wrong here
topP=1.0 admits the full probability mass, including low-probability tokens that drift from the specifications, so inconsistency can increase. topP is correctly tuned when balancing diversity against coherence in open-ended creative generation, not when factual grounding against source data is required.
- ✗
Set topK to a higher value
Why it's wrong here
Raising topK widens the sampling pool, admitting more low-probability tokens and increasing deviation from the product specifications. topK is the right control when you want varied, creative wording while keeping outputs coherent, not when the requirement is strict adherence to supplied factual content.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.