AIF-C01 Fundamentals of Generative AI Practice 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

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?

⚠ Common exam trap

AWS often tests the misconception that simply increasing chunk size or using a larger embedding model will improve retrieval, when the real bottleneck is the chunking strategy's ability to preserve semantic coherence.

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

Semantic chunking groups text based on meaning rather than fixed token counts, preserving the natural boundaries of concepts and paragraphs. This ensures that relevant details from long documents remain intact within a single chunk, improving retrieval accuracy for the chatbot.

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.
✗
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.

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AIF-C01 Fundamentals of Generative AI Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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