Quick Answer
The correct answer is to use a reranker model. This is the best approach because a reranker improves RAG retrieval precision by taking the initial set of chunks returned by vector search and re-ordering them based on deep semantic relevance to the query, using cross-encoding to evaluate each query-chunk pair as a whole rather than relying on simple vector similarity. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this concept tests your understanding of how to refine retrieval when mixing RAG with other data sources, where irrelevant chunks are common. A frequent trap is assuming that increasing the number of retrieved chunks or adjusting vector similarity thresholds alone will solve the problem, but only a reranker filters effectively by scoring relevance at a finer granularity. Memory tip: think of a reranker as a “second-pass judge” that cross-examines each chunk against the query, unlike the first-pass vector search which only looks for approximate neighbors.
This 1Z0-1127 practice question tests your understanding of building llm applications with rag and vector search. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An application mixes RAG with other data sources. The vector search returns too many irrelevant chunks. What is the best approach to filter them?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Use a reranker model
A reranker scores retrieved chunks by relevance, filtering out irrelevant ones.
Use exact search instead of ANN
Why wrong: Exact search does not inherently filter irrelevant results.
Reduce the number of retrieved chunks
Why wrong: Reducing the number may cut relevant chunks as well.
Increase chunk size
Why wrong: Larger chunks may include more irrelevant information.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a reranker model
A reranker model (Option A) is the best approach because it takes the initial set of retrieved chunks and re-orders them based on semantic relevance to the query, effectively filtering out irrelevant chunks. Unlike simple vector similarity, a reranker uses cross-encoding to evaluate the query-chunk pair as a whole, which significantly improves precision when mixing RAG with other data sources.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use a reranker model
Why this is correct
A reranker scores retrieved chunks by relevance, filtering out irrelevant ones.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Use exact search instead of ANN
Why it's wrong here
Exact search does not inherently filter irrelevant results.
Reduce the number of retrieved chunks
Why it's wrong here
Reducing the number may cut relevant chunks as well.
Increase chunk size
Why it's wrong here
Larger chunks may include more irrelevant information.
Common exam traps
Oracle often tests the misconception that reducing the number of retrieved chunks (Option C) is a valid filter, but the trap is that this only limits output size without improving relevance—reranking is the correct technique to reorder and discard irrelevant results.
Detailed technical explanation
Rerankers, such as Cohere Rerank or BERT-based cross-encoders, compute a relevance score for each query-chunk pair by processing them together through a transformer, which captures nuanced interactions like negation or synonymy that bi-encoder embeddings miss. In practice, a common pipeline retrieves top 20-100 chunks via ANN, then reranks to keep the top 3-5, drastically improving answer quality in enterprise RAG systems that combine vector search with keyword or graph-based retrieval.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Building LLM Applications with RAG and Vector Search — study guide chapter
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FAQ
Building LLM Applications with RAG and Vector Search — This question tests Building LLM Applications with RAG and Vector Search — Read the scenario before looking for a memorised answer..
The correct answer is: Use a reranker model — A reranker model (Option A) is the best approach because it takes the initial set of retrieved chunks and re-orders them based on semantic relevance to the query, effectively filtering out irrelevant chunks. Unlike simple vector similarity, a reranker uses cross-encoding to evaluate the query-chunk pair as a whole, which significantly improves precision when mixing RAG with other data sources.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company has deployed a RAG application using OCI Generative AI service with a vector store in OCI OpenSearch. Users report that answers are often incomplete or irrelevant. The application uses a single prompt template with a fixed chunk size of 1000 tokens. Which action is most likely to improve answer quality?
mediumWhy C: Implementing a re-ranking step after vector search helps filter and prioritize the most relevant chunks, improving answer quality. Larger chunks may dilute context, smaller models reduce accuracy, and disabling vector search defeats RAG purpose.
Last reviewed: Jun 30, 2026
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