AIF-C01 Applications of Foundation Models Practice Question
A company fine-tunes a foundation model on SageMaker JumpStart for sentiment analysis. After deployment, the model shows bias toward positive sentiment. Which action should be taken to mitigate bias?
⚠ Common exam trap
AWS often tests the misconception that bias is solely a data quantity issue, leading candidates to incorrectly choose adding more examples (Option B) instead of recognizing that alignment techniques like RLHF are required to correct model behavior after training.
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
✓
Perform RLHF (Reinforcement Learning from Human Feedback) to align outputs
RLHF (Reinforcement Learning from Human Feedback) is the correct approach because it directly addresses the misalignment between the model's outputs and desired human values. By collecting human feedback on model outputs and using it to train a reward model, RLHF fine-tunes the foundation model to reduce biased behavior, such as the over-prediction of positive sentiment, without simply reweighting the training data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a different foundation model
Why it's wrong here
A different model may still have biases; RLHF is targeted.
- ✗
Add more positive examples to training data
Why it's wrong here
This may increase bias toward positive sentiment.
- ✗
Increase training epochs
Why it's wrong here
More epochs can lead to overfitting and reinforce bias.
- ✓
Perform RLHF (Reinforcement Learning from Human Feedback) to align outputs
Why this is correct
RLHF uses human feedback to reduce undesirable biases.
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JA
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.