AIF-C01 Fundamentals of Generative AI Practice Question
An enterprise wants to ensure that generative AI applications built on AWS comply with data privacy regulations. They need to prevent the model from using customer data in future training. Which feature of Amazon Bedrock should they enable?
⚠ Common exam trap
Candidates often confuse data protection mechanisms (encryption, access control) with data usage controls (opt-out of model improvement) on Amazon Bedrock.
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
✓
Opt-out of model improvement
Amazon Bedrock's opt-out of model improvement feature allows customers to prevent AWS from using their data (including prompts, completions, and associated metadata) for model training or service improvement. This is essential for compliance with data privacy regulations like GDPR or CCPA, as it ensures customer data is not retained or used beyond the immediate inference request.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Policy-based data governance
Why it's wrong here
Policy-based data governance is not a Bedrock feature that stops a model training on your data; Bedrock never trains foundation models on customer inputs unless you opt in. The actual control is the data retention and opt-out setting. Governance policies suit controlling access and usage across accounts, not preventing training.
- ✓
Opt-out of model improvement
Why this is correct
Opting out of model improvement prevents Amazon from using the customer's inputs and outputs to train or refine foundation models. This satisfies the data privacy requirement by contractually and technically excluding customer data from future training.
- ✗
Data encryption at rest
Why it's wrong here
Encryption at rest protects stored data from unauthorised reading, but does not govern whether Bedrock uses prompts or completions for training. The relevant control is the opt-out from model improvement, which prevents data retention for training. Encryption at rest is correct when the requirement is protecting data confidentiality on disk.
- ✗
Model customization with customer data
Why it's wrong here
Model customisation with customer data is the opposite of the requirement: fine-tuning deliberately feeds your data into a custom model. The stem needs the opt-out that stops Bedrock retaining inputs for training foundation models. Customisation is correct when you want a tailored model for domain-specific tasks.
Go deeper
Related to this question
About these practice questions
One of 862 original AIF-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.