- A
Add a reranking step after initial retrieval
Why wrong: Rerankers add computational overhead.
- B
Use a smaller chunk size during document ingestion
Smaller chunks mean faster embedding and retrieval.
- C
Increase the topK parameter to retrieve more context
Why wrong: More retrieved documents increase processing time.
- D
Switch to a larger embedding model for better accuracy
Why wrong: Larger models are slower.
Question 78 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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 development team notices that their RAG application returns responses slowly when processing large PDF documents (100+ pages). They need to improve response time without significantly reducing retrieval quality. Which action is most effective?
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
✓
Use a smaller chunk size during document ingestion
Using a smaller chunk size during document ingestion reduces the amount of text per chunk, which decreases the latency of embedding generation and vector search. For large PDFs, smaller chunks also minimize the risk of retrieving overly large context windows that slow down the LLM's response generation, while still preserving retrieval quality if overlap is properly configured.
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.
- ✗
Add a reranking step after initial retrieval
Why it's wrong here
Rerankers add computational overhead.
- ✓
Use a smaller chunk size during document ingestion
Why this is correct
Smaller chunks mean faster embedding and retrieval.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the topK parameter to retrieve more context
Why it's wrong here
More retrieved documents increase processing time.
- ✗
Switch to a larger embedding model for better accuracy
Why it's wrong here
Larger models are slower.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle GenAI documentation emphasizes that while adding reranking or increasing topK may improve retrieval quality, they increase latency. The most direct way to reduce response time for large PDFs is to use a smaller chunk size, which reduces the amount of text per chunk and speeds up both embedding and vector search operations.
Detailed technical explanation
How to think about this question
Smaller chunk sizes (e.g., 256 tokens instead of 1024) reduce the per-vector storage and search space in the vector index, leading to faster approximate nearest neighbor (ANN) searches. In practice, chunk overlap (e.g., 10-20%) is critical to avoid losing context at boundaries, and the optimal chunk size depends on the embedding model's token limit and the expected query granularity.
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: Use a smaller chunk size during document ingestion — Using a smaller chunk size during document ingestion reduces the amount of text per chunk, which decreases the latency of embedding generation and vector search. For large PDFs, smaller chunks also minimize the risk of retrieving overly large context windows that slow down the LLM's response generation, while still preserving retrieval quality if overlap is properly configured.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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