- A
Implement Retrieval-Augmented Generation (RAG) using a knowledge base on Amazon Bedrock and a system prompt demanding factual responses.
RAG retrieves relevant documents to ground the answer, and system prompts can enforce constraints, reducing hallucinations.
- B
Fine-tune the model on a large dataset of medical transcripts and deploy with default parameters.
Why wrong: Fine-tuning alone does not guarantee factual accuracy; the model may still hallucinate on unseen topics.
- C
Use reinforcement learning from human feedback (RLHF) on the deployed model.
Why wrong: RLHF is not natively supported in Amazon Bedrock; it requires SageMaker and is complex to operationalize.
- D
Set the maxTokens to a low value to force shorter, more focused answers.
Why wrong: Limiting output length does not improve factual accuracy; the model may still generate incorrect information within the limit.
Quick Answer
The correct answer is to implement Retrieval-Augmented Generation (RAG) using a knowledge base on Amazon Bedrock combined with a system prompt demanding factual responses. This combination directly addresses the need to minimize hallucinations in a Bedrock healthcare chatbot by grounding the model’s output in verified, retrieved documents rather than relying solely on its parametric memory, while the system prompt enforces strict accuracy constraints for patient triage. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of how RAG and prompt engineering work together to improve factual reliability in regulated domains like healthcare—a common trap is assuming fine-tuning alone solves hallucinations, but it cannot guarantee grounding against incomplete data. Remember the memory tip: “RAG retrieves, prompt constrains” to recall that retrieval grounds the answer while the system prompt sets the safety guardrails.
AIF-C01 Applications of Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of applications of foundation models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A healthcare company is deploying a conversational AI using a foundation model on Amazon Bedrock for patient triage. The application must minimize hallucinations and ensure factual accuracy. Which combination of techniques should the team implement?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Implement Retrieval-Augmented Generation (RAG) using a knowledge base on Amazon Bedrock and a system prompt demanding factual responses.
Option C is correct because RAG grounds responses in retrieved documents, and system prompts can enforce safety and accuracy constraints. Option A is wrong because fine-tuning alone may still lead to hallucinations if the training data is incomplete. Option B is wrong because RLHF is complex to implement on Bedrock and doesn't directly ground responses. Option D is wrong because reducing max tokens does not improve accuracy.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement Retrieval-Augmented Generation (RAG) using a knowledge base on Amazon Bedrock and a system prompt demanding factual responses.
Why this is correct
RAG retrieves relevant documents to ground the answer, and system prompts can enforce constraints, reducing hallucinations.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Fine-tune the model on a large dataset of medical transcripts and deploy with default parameters.
Why it's wrong here
Fine-tuning alone does not guarantee factual accuracy; the model may still hallucinate on unseen topics.
- ✗
Use reinforcement learning from human feedback (RLHF) on the deployed model.
Why it's wrong here
RLHF is not natively supported in Amazon Bedrock; it requires SageMaker and is complex to operationalize.
- ✗
Set the maxTokens to a low value to force shorter, more focused answers.
Why it's wrong here
Limiting output length does not improve factual accuracy; the model may still generate incorrect information within the limit.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Command / output trap
Limiting output length does not improve factual accuracy; the model may still generate incorrect information within the limit.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
- →
Applications of Foundation Models — study guide chapter
Learn the concepts, then practise the questions
- →
Applications of Foundation Models practice questions
Targeted practice on this topic area only
- →
All AIF-C01 questions
500 questions across all exam domains
- →
AWS Certified AI Practitioner AIF-C01 study guide
Full concept coverage aligned to exam objectives
- →
AIF-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AIF-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Applications of Foundation Models practice questions
Practise AIF-C01 questions linked to Applications of Foundation Models.
Fundamentals of AI and ML practice questions
Practise AIF-C01 questions linked to Fundamentals of AI and ML.
Fundamentals of Generative AI practice questions
Practise AIF-C01 questions linked to Fundamentals of Generative AI.
Guidelines for Responsible AI practice questions
Practise AIF-C01 questions linked to Guidelines for Responsible AI.
Security, Compliance and Governance for AI Solutions practice questions
Practise AIF-C01 questions linked to Security, Compliance and Governance for AI Solutions.
AIF-C01 fundamentals practice questions
Practise AIF-C01 questions linked to AIF-C01 fundamentals.
AIF-C01 scenario practice questions
Practise AIF-C01 questions linked to AIF-C01 scenario.
AIF-C01 troubleshooting practice questions
Practise AIF-C01 questions linked to AIF-C01 troubleshooting.
Practice this exam
Start a free AIF-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AIF-C01 question test?
Applications of Foundation Models — This question tests Applications of Foundation Models — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Implement Retrieval-Augmented Generation (RAG) using a knowledge base on Amazon Bedrock and a system prompt demanding factual responses. — Option C is correct because RAG grounds responses in retrieved documents, and system prompts can enforce safety and accuracy constraints. Option A is wrong because fine-tuning alone may still lead to hallucinations if the training data is incomplete. Option B is wrong because RLHF is complex to implement on Bedrock and doesn't directly ground responses. Option D is wrong because reducing max tokens does not improve accuracy.
What should I do if I get this AIF-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 23, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.