- A
The prompt template used to ask the LLM
A well-structured prompt helps the LLM use the context properly.
- B
The chunk size used during document processing
Chunk size determines how much context is in each retrieved piece.
- C
The temperature parameter of the LLM
Why wrong: Temperature affects randomness, not the core retrieval quality.
- D
The number of GPUs allocated to the LLM
Why wrong: GPU count affects speed but not response quality directly.
- E
The choice of embedding model
Different models produce different vector spaces, impacting retrieval relevance.
Question 481 of 991
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.
Which THREE factors directly influence the quality of responses in a RAG system? (Choose three.)
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
✓
The prompt template used to ask the LLM
Option A is correct because the prompt template directly controls how the LLM interprets the retrieved context and formulates its response. A well-structured prompt with clear instructions, context formatting, and output constraints significantly improves response relevance and accuracy, while a poorly designed prompt can lead to hallucinations or off-topic 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.
- ✓
The prompt template used to ask the LLM
Why this is correct
A well-structured prompt helps the LLM use the context properly.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
The chunk size used during document processing
Why this is correct
Chunk size determines how much context is in each retrieved piece.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The temperature parameter of the LLM
Why it's wrong here
Temperature affects randomness, not the core retrieval quality.
- ✗
The number of GPUs allocated to the LLM
Why it's wrong here
GPU count affects speed but not response quality directly.
- ✓
The choice of embedding model
Why this is correct
Different models produce different vector spaces, impacting retrieval relevance.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle certification exams often test the distinction between factors that directly influence response quality (prompt, chunk size, embedding model) versus factors that affect performance or output style (temperature, GPU count), leading candidates to mistakenly select temperature or hardware options.
Detailed technical explanation
How to think about this question
In a RAG pipeline, the chunk size (Option B) determines the granularity of retrieved information—too small chunks may miss context, while too large chunks can dilute relevance. The embedding model (Option E) maps text to vector space; a model fine-tuned for domain-specific data (e.g., using contrastive learning) yields better retrieval precision, directly affecting the quality of context fed to the LLM. The prompt template (Option A) acts as the final orchestrator, often incorporating retrieved chunks with system instructions and few-shot examples to guide the LLM's generation.
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
Got this wrong? Here's your next step.
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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: The prompt template used to ask the LLM — Option A is correct because the prompt template directly controls how the LLM interprets the retrieved context and formulates its response. A well-structured prompt with clear instructions, context formatting, and output constraints significantly improves response relevance and accuracy, while a poorly designed prompt can lead to hallucinations or off-topic answers.
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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