SAP-C03 practice question: AI agents and RAG (AgentCore, Knowledge Bases) (1 of 2)
Architecture Design and Implementation Built for the Cloud A free question from our original SAP-C03 practice exam. Try it, then open the answer to see why each option is right or wrong.
A news publisher keeps a four-million-article archive as text files in Amazon S3. About 1,500 new articles arrive each day, and reporters need answers to research questions that cite the exact articles used.
Each article carries a licensing country. A reader may see only the articles licensed for their country, and new articles must be searchable within 30 minutes of publication. The publisher wants to avoid building and tuning its own embedding and indexing pipeline.
Which solution meets these requirements?
Choose one answer.
AA. Create a Bedrock knowledge base with the S3 bucket as its data source, store each article's licensing country as metadata, and run ingestion on a schedule. Pass a metadata filter for each reader's country when retrieving passages, and return citations.
BB. Fine-tune a foundation model on the archive every month in Amazon SageMaker AI, include the licensing country in the training text, and answer questions from the fine-tuned model without retrieval.
CC. Build a Lambda function that splits each new article into passages, creates embeddings with a Bedrock embedding model and stores them in a self-managed vector index, then sends the top passages to a Bedrock model with each question.
DD. Put the full text of every article that a reader may see into the prompt of each question, and send the request to a Bedrock model with the largest context window it offers.
Show the answer and explanation
Answer: A
A. Right: Correct. The knowledge base handles chunking, embedding and vector storage for the archive, and scheduled ingestion keeps new articles current. Metadata filters enforce the licensing rule at retrieval time, and the answer includes citations to the articles used.
B. Wrong: A monthly retrain cannot make articles searchable within 30 minutes, and a model does not return a citation to the source article. Fine-tuning also adds training work the publisher does not want to own.
C. Wrong: This design can meet the citation and freshness needs, but the publisher would build and tune the embedding and indexing pipeline itself. That is the work the publisher wants to avoid.
D. Wrong: Passing the whole archive in each prompt is far more costly than retrieving only the relevant passages, and it can exceed the model's context window. It also leaves no per-article citation trail for the reporters.
Explanation
A managed knowledge base covers ingestion, vector storage and citations, and metadata filters enforce the licensing rule at query time. Fine-tuning, a custom pipeline and whole-archive prompts each fail the freshness, effort or cost requirements.
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