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
Larger fixed-size chunks dilute the embedding with unrelated text, so the retriever's similarity score for a specific detail drops and it is missed. Fixed-size chunking suits uniform, short passages where boundary loss is tolerable, not long documents needing precise detail retrieval.
✓
Change chunking strategy to SEMANTIC
Why this is correct
Semantic chunking splits documents at natural meaning boundaries rather than fixed token counts, preserving coherent context within each chunk. This improves retrieval accuracy for long documents, since relevant details are less likely to be split across chunks and missed during embedding search.
✗
Add more documents to the S3 bucket
Why it's wrong here
Adding more documents increases corpus size without improving chunking or embedding quality, so long-document details remain poorly retrieved. It is tempting because a larger knowledge base appears richer, but the actual fix is adjusting chunk size or overlap so relevant passages are indexed and matched.
✗
Change the embedding model to a larger one
Why it's wrong here
Embedding models map text to vectors; swapping to a larger one does not alter how source documents are split, so details spanning chunk boundaries stay unretrieved. Larger embeddings suit semantic similarity tuning, not the chunking configuration the stem asks about.

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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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