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Foundation Model Integration Data And CompliancehardMultiple ChoiceObjective-mapped

AIP-C01 Practice Question: Foundation Model Integration Data And Compliance

When migrating an on-premises RAG system to Amazon Bedrock, how should the team handle existing vector embeddings?

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

Re-embed the documents using a model supported by Amazon Bedrock.

Existing embeddings may not be compatible with Bedrock's supported models; they must be re-embedded using a supported model like Titan Embeddings to maintain consistent vector space.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Map the old embeddings to new dimensions using a linear function.

    Why it's wrong here

    Linear mapping does not preserve semantic meaning.

  • Re-embed the documents using a model supported by Amazon Bedrock.

    Why this is correct

    Consistency in embedding models is required for accurate similarity search.

  • Use the Bedrock Import Vector API.

    Why it's wrong here

    There is no such API; re-indexing is required.

  • Import existing vectors directly into the OpenSearch index.

    Why it's wrong here

    Vectors must be compatible with the embedding model used for queries.

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JA

Written and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed August 2026 · checked against the official Amazon Web Services exam blueprint

This AIP-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 AIP-C01 exam.