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AIP-C01 practice question: GenAI solution design, proofs of concept and the Well-Architected Generative AI Lens (1 of 2)

Foundation Model Integration, Data Management, and Compliance A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A regional bank's compliance team is building an assistant that answers staff questions about 4,000 internal policy documents.

The policies change every week, and each answer must cite the clause it came from. The team also needs every answer to follow one fixed memo layout that the legal reviewers have approved.

A policy change must be reflected in answers within one business day, and the team does not want to retrain a model every week. The team has no staff to run or tune a vector database.

Which architecture meets these requirements with the LEAST retraining effort?

Choose one answer.

  1. A. Fine-tune a foundation model every week on the policy documents, with the memo layout included in the training examples, and point the assistant at the newest model version.
  2. B. Place the full text of all 4,000 documents into every prompt, with a fixed system prompt that describes the memo layout, and send each request to a single foundation model.
  3. C. Create an Amazon Bedrock Managed Knowledge Base over the policy bucket, run an incremental sync after each weekly update, and keep the memo layout in a Prompt Management template.
  4. D. Fine-tune a model once on the memo layout, then rely on the model's built-in knowledge to answer questions about the bank's current policies without any retrieval step.
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Answer: C

  • A. Wrong: Weekly fine-tuning means a training job on every policy change, which is the retraining the team wants to avoid. Fine-tuning also does not give reliable clause-level citations, because the model does not store the source passage it learned from.
  • B. Wrong: This keeps the facts current because the prompt always holds the latest text. However, the documents far exceed a typical context window and would make every request expensive and slow, so it misses the cost and latency needs.
  • C. Right: Retrieval reads the latest policy text after each sync, so no model is retrained, and the knowledge base returns source passages for citations. Bedrock manages the vector store, so no database staff are needed, and the prompt template enforces the approved memo format. Learn more: Build a managed knowledge base (opens in a new tab)
  • D. Wrong: A foundation model has no knowledge of the bank's internal policies, so its answers would be unsupported and could not cite clauses. Changes to policies would also be missed entirely until the model was retrained.

Explanation

Policies change weekly and answers need citations, so retrieval over the documents with incremental sync fits better than retraining. A prompt template handles the fixed memo layout, which is a format need rather than a knowledge need. Full-text prompts are too large and costly, and a model-only approach has no access to the bank's policies.

Retrieval augmented generation with a knowledge base Sync: documents from S3 and other connectors are split into chunks, turned into embeddings, and stored in a vector store. Query: a user question retrieves the best matching chunks from the vector store, and the model writes an answer from them with citations. Sync Documents S3, connectors Chunk + embed embeddings Vector store managed or own Query User question from the app Retrieve best chunks Model answers with citations
A managed knowledge base over the policy bucket, synced incrementally after each weekly update, keeps answers current.

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