AIF-C01 Guidelines for Responsible AI Practice Question
A healthcare organization is developing a clinical decision support system using Amazon Bedrock with a large language model (LLM) to analyze patient symptoms and suggest potential diagnoses. The system must comply with HIPAA and internal responsible AI guidelines. During testing, the model occasionally generates diagnoses that are inconsistent with established medical guidelines and shows a tendency to recommend more aggressive treatments for patients from certain demographic groups. The team has already implemented data encryption, access controls, and basic content filtering. They need to further reduce biased and unsafe outputs without delaying the deployment timeline. What should the team do next?
⚠ Common exam trap
A common mix-up: candidates confuse monitoring and logging (Option A) with actual bias mitigation, or assume that a different pre-trained model (Option B) will inherently solve domain-specific bias without requiring additional fine-tuning or validation.
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
✓
Fine-tune the model using a curated dataset of anonymized patient records that is balanced across demographic groups and aligned with clinical guidelines.
Fine-tuning the model with a balanced, curated dataset directly addresses both the bias and clinical accuracy issues at the model level, which is the most effective approach for reducing biased and unsafe outputs without delaying deployment. This method adjusts the model's internal weights to align with established medical guidelines and demographic fairness, rather than relying on post-processing filters or logging that do not fix the root cause. Since the team has already implemented basic content filtering, fine-tuning provides a targeted, efficient solution that can be completed within a reasonable timeline.
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 logging of all model inputs and outputs to Amazon CloudWatch and set up alarms for any mentions of protected attributes.
Why it's wrong here
Logging helps with monitoring but does not prevent biased outputs from being generated.
- ✗
Replace the current LLM with a different pre-trained model that has been benchmarked for lower bias on medical datasets.
Why it's wrong here
Switching models without fine-tuning may still result in biased or unsafe outputs, and benchmarking does not guarantee suitability.
- ✓
Fine-tune the model using a curated dataset of anonymized patient records that is balanced across demographic groups and aligned with clinical guidelines.
Why this is correct
Fine-tuning on a balanced, guideline-aligned dataset reduces both bias and inaccuracy by teaching the model correct patterns.
- ✗
Apply stronger content filtering rules using Amazon Comprehend Medical to block any diagnosis that contains demographic-related terms.
Why it's wrong here
Filtering based on demographic terms does not address the underlying model bias and may block legitimate clinical 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.