- A
Switch to a larger foundation model like Claude 3 Opus
Why wrong: A larger model may improve quality but does not fix the underlying retrieval issues; it's a band-aid and more expensive.
- B
The model's temperature is set too high; reduce it to 0.1
Why wrong: High temperature increases randomness but the symptoms point to inaccurate context, not randomness.
- C
The maximum tokens limit is too low; increase it to 4096
Why wrong: While a low limit could cut off context, the logs indicate retrieved documents are passed; length is not the primary issue.
- D
The chunking strategy for documents is too coarse or inappropriate; refine chunking and use semantic search in Kendra
Proper chunking ensures each chunk contains coherent information relevant to potential queries; Kendra's semantic search improves relevance.
Quick Answer
The correct answer is that the chunking strategy for documents is too coarse or inappropriate, and the solution is to refine chunking and use semantic search in Kendra. This is because improving retrieval relevance with chunking and semantic search directly addresses how document segments are created and matched against user queries; if chunks are too large or poorly structured, they may contain irrelevant filler that dilutes the exact answer the model needs, while semantic search ensures the retrieved chunks align contextually rather than relying on simple keyword overlap. On the AWS Certified AI Practitioner AIF-C01 exam, this scenario tests your understanding of the retrieval-augmented generation pipeline, where chunking quality and search method are common failure points—a frequent trap is assuming the model or temperature settings are at fault. Remember the mnemonic “Chunk and Search, Don’t Blame the Model” to recall that retrieval quality, not model tuning, is the root cause.
AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company deployed a chatbot using Amazon Lex integrated with a Lambda function that invokes Claude on Amazon Bedrock. The Lambda function retrieves relevant documents from an Amazon Kendra index to use as context. Users report that the chatbot's responses are often irrelevant or incorrect despite the Kendra index containing accurate information. The logs show that the Lambda function is correctly passing retrieved documents to the model. What is the most likely cause and solution?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
The chunking strategy for documents is too coarse or inappropriate; refine chunking and use semantic search in Kendra
The issue likely stems from the chunking and retrieval strategy. If the retrieved document chunks do not contain the exact answer or are poorly segmented, the model may not have the necessary context. Improving chunking to be more semantic and ensuring retrieval uses a relevant similarity metric (e.g., using Kendra's relevance tuning) would help. Increasing temperature or reducing tokens would degrade quality. Switching model may not address the root cause.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a larger foundation model like Claude 3 Opus
Why it's wrong here
A larger model may improve quality but does not fix the underlying retrieval issues; it's a band-aid and more expensive.
- ✗
The model's temperature is set too high; reduce it to 0.1
Why it's wrong here
High temperature increases randomness but the symptoms point to inaccurate context, not randomness.
- ✗
The maximum tokens limit is too low; increase it to 4096
Why it's wrong here
While a low limit could cut off context, the logs indicate retrieved documents are passed; length is not the primary issue.
- ✓
The chunking strategy for documents is too coarse or inappropriate; refine chunking and use semantic search in Kendra
Why this is correct
Proper chunking ensures each chunk contains coherent information relevant to potential queries; Kendra's semantic search improves relevance.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
- →
Fundamentals of Generative AI — study guide chapter
Learn the concepts, then practise the questions
- →
Fundamentals of Generative AI 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?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The chunking strategy for documents is too coarse or inappropriate; refine chunking and use semantic search in Kendra — The issue likely stems from the chunking and retrieval strategy. If the retrieved document chunks do not contain the exact answer or are poorly segmented, the model may not have the necessary context. Improving chunking to be more semantic and ensuring retrieval uses a relevant similarity metric (e.g., using Kendra's relevance tuning) would help. Increasing temperature or reducing tokens would degrade quality. Switching model may not address the root cause.
What should I do if I get this AIF-C01 question wrong?
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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 →
Keep practising
More AIF-C01 practice questions
- A company is using Amazon Bedrock to generate code snippets. They want to ensure the generated code is secure. Which TWO…
- A healthcare company is using Amazon Bedrock to summarize patient notes. The compliance team requires that no patient da…
- A company is using Amazon Bedrock to generate marketing copy. They want to evaluate the quality of the generated text. W…
- An organization wants to detect anomalies in real-time streaming data from IoT devices. The data includes sensor reading…
- A company is deploying a machine learning model for real-time fraud detection. The model must make predictions with late…
- A company is using Amazon Bedrock to generate marketing content. They want to evaluate the quality of the generated text…
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.