MLA-C02 practice question: RAG architecture and retrieval tuning (1 of 2)
ML Model and Foundation Model (FM) Development A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.
A public library runs a retrieval-augmented chatbot over its policy manual and catalog help pages, using an Amazon Bedrock knowledge base with an Amazon OpenSearch Serverless vector store. Patrons often ask about exact codes, such as a policy number like POL-2290, and the assistant often answers with a general policy instead.
The team wants retrieval to match exact identifiers as well as meaning, using only the knowledge base it already has, without adding a new search service.
Which change should the team make?
Choose one answer.
AA. Increase the number of retrieved chunks from 5 to 50 so that the exact code is more likely to appear somewhere in the results.
BB. Replace the embedding model with a larger one, since larger embedding models always match exact codes more precisely.
CC. Shorten the chatbot prompt so the model focuses only on the policy text and ignores the catalog help pages.
DD. Change the retrieval search type to hybrid, which combines semantic vector matching with keyword matching for the query.
Show the answer and explanation
Answer: D
A. Wrong: More chunks increase the amount of text sent to the model and do not guarantee that the exact code ranks high. The stem asks for matching of identifiers.
B. Wrong: Embedding models capture meaning, and a bigger model does not guarantee that exact strings match. The keyword side of hybrid search is what handles identifiers.
C. Wrong: The retrieval step is where the wrong passage is missed, and a shorter prompt does not fix that. The stem calls for changing how passages are found.
D. Right: Correct. Hybrid search combines semantic vector search with a search of the raw text, so exact codes such as POL-2290 can match even when meaning-based search misses them. It is supported for OpenSearch Serverless vector stores that have a filterable text field and is set in the retrieval configuration. Learn more: Configure and customize queries and response generation (opens in a new tab)
Explanation
Hybrid search combines semantic similarity with text matching, which helps when a query contains exact identifiers, and the knowledge base's OpenSearch Serverless store supports it. More chunks, larger embedding models or prompt edits do not change how exact codes are matched during retrieval.
Want more on RAG architecture and retrieval tuning? The 5 full-length practice sets have 9 more RAG architecture and retrieval tuning questions, 325 in all, none of them in the free exam. One payment of $14.99, no subscription. See the practice sets →