- A
Increase the chunk size to 1024 tokens to include more context.
Why wrong: Larger chunks can introduce noise and reduce relevance.
- B
Add a 20% token overlap between consecutive chunks.
Overlap ensures that context spanning chunk boundaries is preserved.
- C
Reduce the chunk size to 256 tokens to increase precision.
Why wrong: Smaller chunks may lose necessary context, reducing recall.
- D
Switch to a sentence-based chunking strategy with no overlap.
Why wrong: Sentence-based may help but without overlap still risks missing cross-sentence context.
Question 229 of 991
Boost RAG Recall with Token Overlap
This 1Z0-1127 practice question tests your understanding of building llm applications with rag and vector search. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
A small business is building an internal Q&A bot using OCI Generative AI with RAG. They have indexed their product manuals into OCI OpenSearch using a precomputed embedding model. When they test queries, the bot often returns answers that are only partially relevant, and sometimes it cannot find answers for questions that are clearly present in the manuals. The developers suspect the chunking strategy is suboptimal. Currently, they use a fixed chunk size of 512 tokens with no overlap. What should they do to improve retrieval relevance?
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
✓
Add a 20% token overlap between consecutive chunks.
Adding a 20% token overlap between chunks ensures that semantically related content spanning chunk boundaries is not lost, which directly addresses the problem of missing answers for questions present in the manuals. Overlap preserves context continuity, improving retrieval recall and relevance without sacrificing the precision that a fixed-size chunk provides.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the chunk size to 1024 tokens to include more context.
Why it's wrong here
Larger chunks can introduce noise and reduce relevance.
- ✓
Add a 20% token overlap between consecutive chunks.
Why this is correct
Overlap ensures that context spanning chunk boundaries is preserved.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the chunk size to 256 tokens to increase precision.
Why it's wrong here
Smaller chunks may lose necessary context, reducing recall.
- ✗
Switch to a sentence-based chunking strategy with no overlap.
Why it's wrong here
Sentence-based may help but without overlap still risks missing cross-sentence context.
Common exam traps
Common exam trap: answer the scenario, not the keyword
OCI often tests the misconception that larger chunks always improve context, when in fact overlap is the key to preserving boundary-spanning information without sacrificing precision.
Detailed technical explanation
How to think about this question
In vector search with RAG, chunking strategies directly impact embedding quality and retrieval performance. Overlap (e.g., 20% of tokens) ensures that the same semantic unit appears in multiple chunks, increasing the probability that a query embedding will match at least one chunk containing the full context. This is especially critical for technical manuals where a single procedure or concept may span multiple paragraphs or code blocks.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
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..
What is the correct answer to this question?
The correct answer is: Add a 20% token overlap between consecutive chunks. — Adding a 20% token overlap between chunks ensures that semantically related content spanning chunk boundaries is not lost, which directly addresses the problem of missing answers for questions present in the manuals. Overlap preserves context continuity, improving retrieval recall and relevance without sacrificing the precision that a fixed-size chunk provides.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This 1Z0-1127 practice question is part of Courseiva's free Oracle 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 1Z0-1127 exam.
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