AIF-C01 Applications of Foundation Models Practice Question
An e-commerce company uses Amazon Bedrock to generate product descriptions from keywords. Some descriptions contain inaccurate details about product specifications. Which approach should the company take to reduce factual errors?
⚠ Common exam trap
AWS often tests the misconception that increasing model parameters or changing models alone improves factual accuracy, when in fact prompt engineering with grounded data is the most effective and efficient method to reduce hallucinations.
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
✓
Include the product specifications in the prompt and instruct the model to base the description on the provided data.
Providing the product specifications directly in the prompt and instructing the model to base the description on that data grounds the generation in factual information, reducing hallucinations. This technique, known as prompt engineering with in-context learning, ensures the model uses the given data rather than relying on its training data, which may contain inaccuracies.
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 the maxTokens parameter to allow more detailed descriptions.
Why it's wrong here
More tokens give the model more freedom to generate incorrect information; it doesn't improve accuracy.
- ✗
Use a different foundation model from Bedrock for each product category.
Why it's wrong here
Rotating models does not guarantee accuracy; all models may hallucinate without proper context.
- ✗
Deploy the model to a SageMaker endpoint and use human-in-the-loop validation.
Why it's wrong here
Human validation adds cost and delay; it's a safety measure but not a proactive fix for hallucination.
- ✓
Include the product specifications in the prompt and instruct the model to base the description on the provided data.
Why this is correct
Providing facts in the prompt grounds the model's output and reduces fabrication.
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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.