AIF-C01 Fundamentals of Generative AI Practice Question
A company is using Amazon Bedrock to generate marketing copy. They want to ensure the output is safe and appropriate. Which TWO actions should they take? (Choose 2.)
⚠ Common exam trap
Candidates often confuse mechanisms that control randomness (temperature=0) or network security (private endpoints) with content safety. While these settings affect output diversity and data confidentiality, they do not filter harmful or inappropriate content. Native guardrails or human review are required for content safety.
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
✓
Enable content filtering with guardrails
Option A is correct because Amazon Bedrock Guardrails provide configurable content filters (for hate, violence, sexual, insults, misconduct, and prompt-attack categories) that detect and block unsafe or inappropriate model output, directly addressing the goal of safe marketing copy. Option E is correct because implementing human review of all generated content adds a human-in-the-loop control that catches context-dependent or brand-inappropriate material that automated filters may miss, ensuring appropriateness before publication. Option B is incorrect because temperature controls randomness/creativity of sampling, not safety; setting it to 0 only makes output more deterministic and does nothing to filter harmful content. Option C is incorrect because fine-tuning with unsafe examples would teach the model unsafe patterns, worsening rather than improving safety. Option D is incorrect because a private endpoint (VPC endpoint/PrivateLink) only secures network connectivity to Bedrock and does not evaluate or filter the safety of generated text.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable content filtering with guardrails
Why this is correct
Guardrails apply configurable content filters that detect and block harmful, hateful, or inappropriate categories in both prompts and responses, plus deny-topics and word filters. This directly enforces output safety at inference time, satisfying the requirement without changing the underlying model.
- ✗
Set temperature to 0 for deterministic output
Why it's wrong here
Temperature controls sampling randomness, not content safety; setting it to zero yields deterministic but potentially still harmful text. It is tempting because low temperature is used for reproducible, factual outputs, but filtering requires Guardrails for Amazon Bedrock, which blocks unsafe content independently of sampling parameters.
- ✗
Use model fine-tuning with unsafe examples
Why it's wrong here
Fine-tuning with unsafe examples teaches the model to reproduce harmful, biased or inappropriate language, directly undermining the safety goal. Fine-tuning adapts tone, style or domain vocabulary using curated, vetted datasets. It would be the right choice for brand voice alignment, not for content moderation.
- ✗
Use a private endpoint for Bedrock
Why it's wrong here
A private endpoint secures network connectivity between the VPC and Bedrock, protecting data in transit from interception. It does not inspect, filter or moderate generated text, so unsafe marketing copy still reaches users. Private endpoints suit regulated workloads needing traffic isolation, not content safety.
- ✓
Implement human review of all generated content
Why this is correct
Human review adds a person-in-the-loop checkpoint before generated marketing copy is published, catching context-dependent issues that automated filters miss, such as misleading claims or brand-inappropriate tone. This satisfies the safety requirement through oversight rather than relying solely on model-side controls.
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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.