The correct adjustment is to increase the number of retrieved chunks. In a Retrieval-Augmented Generation (RAG) pipeline, the model’s answer quality is directly tied to the breadth of context it receives; when too few chunks are retrieved, the generated response may lack critical details, resulting in incomplete answers. By increasing the retrieved chunk count, the system provides the generative model with a richer, more comprehensive set of supporting passages, enabling it to synthesize a complete and accurate response. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this concept tests your understanding of RAG configuration trade-offs—specifically how chunk retrieval parameters affect answer completeness. A common trap is confusing chunk size with chunk count; remember that increasing chunk size alone can dilute relevance, while increasing the number of retrieved chunks directly expands context without sacrificing precision. Memory tip: “More chunks, more context—complete answers are the result.”
1Z0-1127 Practice Question: Building LLM Applications with RAG and Vector Search
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 uses RAG to answer customer queries, but answers are often incomplete because the retrieved chunks do not contain full context. Which adjustment should the developer make?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of retrieved chunks
Increasing the number of retrieved chunks gives the model more contextual information, leading to more complete answers.
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.
✗
Use a different embedding model
Why it's wrong here
The embedding model does not affect the amount of context retrieved.
✗
Increase chunk overlap
Why it's wrong here
While helpful, increasing overlap alone may not provide enough context if the total retrieved tokens are limited.
✓
Increase the number of retrieved chunks
Why this is correct
Retrieving more chunks provides more context to the generation model.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease chunk size
Why it's wrong here
Smaller chunks reduce context per chunk, worsening the problem.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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
Got this wrong? Here's your next step.
Identify which 1Z0-1127 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Increase the number of retrieved chunks — Increasing the number of retrieved chunks gives the model more contextual information, leading to more complete answers.
What should I do if I get this 1Z0-1127 question wrong?
Identify which 1Z0-1127 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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