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Fundamentals of Generative AI →mediumMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to change the chunking strategy to SEMANTIC. This is because the current FIXED_SIZE chunking splits documents into arbitrary, equal-length segments, which often breaks apart coherent paragraphs and severs contextual relationships, causing the chatbot to miss relevant details during retrieval. SEMANTIC chunking, by contrast, groups text based on meaning and natural topic boundaries, preserving the logical flow of information and significantly improving the relevance of retrieved chunks for the knowledge base. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of how chunking strategy directly impacts retrieval accuracy in Amazon Bedrock knowledge bases—a common trap is assuming larger fixed chunks always help, when in fact semantic grouping is superior for long, narrative documents. Remember the memory tip: “Fixed splits text, Semantic keeps context next.”

AIF-C01 Fundamentals of Generative AI Practice Question

This AIF-C01 practice question tests your understanding of fundamentals of generative ai. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.

Exhibit

AWS CloudFormation template snippet:
Resources:
  BedrockKnowledgeBase:
    Type: AWS::Bedrock::KnowledgeBase
    Properties:
      Name: support-kb
      RoleArn: arn:aws:iam::123456789012:role/BedrockKnowledgeBaseRole
      KnowledgeBaseConfiguration:
        Type: VECTOR
        VectorKnowledgeBaseConfiguration:
          EmbeddingModelArn: arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v1
      StorageConfiguration:
        Type: OPENSEARCH_SERVERLESS
        OpensearchServerlessConfiguration:
          CollectionArn: arn:aws:aoss:us-east-1:123456789012:collection/abc123
          FieldMapping:
            MetadataField: metadata
            TextField: text
  DataSource:
    Type: AWS::Bedrock::DataSource
    Properties:
      KnowledgeBaseId: !Ref BedrockKnowledgeBase
      Name: s3-source
      DataSourceConfiguration:
        Type: S3
        S3Configuration:
          BucketArn: arn:aws:s3:::my-docs-bucket
      VectorIngestionConfiguration:
        ChunkingConfiguration:
          ChunkingStrategy: FIXED_SIZE

Refer to the exhibit. A company sets up a knowledge base for a customer support chatbot using Amazon Bedrock. Users report that the chatbot misses relevant details from long documents. Which change to the data source configuration would most likely improve retrieval?

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.

Question 1mediummultiple choice
Full question →

Exhibit

AWS CloudFormation template snippet:
Resources:
  BedrockKnowledgeBase:
    Type: AWS::Bedrock::KnowledgeBase
    Properties:
      Name: support-kb
      RoleArn: arn:aws:iam::123456789012:role/BedrockKnowledgeBaseRole
      KnowledgeBaseConfiguration:
        Type: VECTOR
        VectorKnowledgeBaseConfiguration:
          EmbeddingModelArn: arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v1
      StorageConfiguration:
        Type: OPENSEARCH_SERVERLESS
        OpensearchServerlessConfiguration:
          CollectionArn: arn:aws:aoss:us-east-1:123456789012:collection/abc123
          FieldMapping:
            MetadataField: metadata
            TextField: text
  DataSource:
    Type: AWS::Bedrock::DataSource
    Properties:
      KnowledgeBaseId: !Ref BedrockKnowledgeBase
      Name: s3-source
      DataSourceConfiguration:
        Type: S3
        S3Configuration:
          BucketArn: arn:aws:s3:::my-docs-bucket
      VectorIngestionConfiguration:
        ChunkingConfiguration:
          ChunkingStrategy: FIXED_SIZE

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

✓

Change chunking strategy to SEMANTIC

The chunking strategy is set to FIXED_SIZE, which may split documents into chunks that are too small or lose context. Switching to SEMANTIC chunking improves retrieval by grouping paragraphs with similar meaning.

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.

  • ✗

    Increase the chunk size in FIXED_SIZE chunking

    Why it's wrong here

    Increasing fixed chunk size may still break semantic units; semantic chunking is better for relevance.

  • ✓

    Change chunking strategy to SEMANTIC

    Why this is correct

    Semantic chunking groups related content, preserving context and improving retrieval accuracy.

    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.

  • ✗

    Add more documents to the S3 bucket

    Why it's wrong here

    Adding more documents does not fix the chunking issue.

  • ✗

    Change the embedding model to a larger one

    Why it's wrong here

    Embedding model quality may help but the primary issue is chunking strategy.

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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

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.

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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: Change chunking strategy to SEMANTIC — The chunking strategy is set to FIXED_SIZE, which may split documents into chunks that are too small or lose context. Switching to SEMANTIC chunking improves retrieval by grouping paragraphs with similar meaning.

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.

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Last reviewed: Jun 23, 2026

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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.