- A
Use a smaller chunk size for all documents
Why wrong: Not always beneficial; can lead to loss of context.
- B
Remove stop words from documents before embedding
Why wrong: Minor impact compared to other improvements.
- C
Increase the topK parameter significantly
Why wrong: Often introduces noise.
- D
Use a more accurate embedding model
Better embeddings improve similarity search.
- E
Enrich chunk metadata and apply strict filters during retrieval
Metadata filtering reduces irrelevant results.
Question 892 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 TWO actions can improve the retrieval accuracy of a RAG system? (Select two.)
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 more accurate embedding model
Option D is correct because a more accurate embedding model (e.g., OCI Generative AI's embedding models or Cohere embeddings available in OCI) produces higher-quality vector representations that capture semantic meaning more precisely, directly improving retrieval relevance in a RAG pipeline.
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 smaller chunk size for all documents
Why it's wrong here
Not always beneficial; can lead to loss of context.
- ✗
Remove stop words from documents before embedding
Why it's wrong here
Minor impact compared to other improvements.
- ✗
Increase the topK parameter significantly
Why it's wrong here
Often introduces noise.
- ✓
Use a more accurate embedding model
Why this is correct
Better embeddings improve similarity search.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enrich chunk metadata and apply strict filters during retrieval
Why this is correct
Metadata filtering reduces irrelevant results.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that simply increasing the number of retrieved documents (topK) or naively preprocessing text (e.g., removing stop words) will improve accuracy, when in fact these actions can harm retrieval quality.
Detailed technical explanation
How to think about this question
Embedding models like those based on transformer architectures (e.g., BERT, E5) are trained on large corpora and produce dense vectors that encode contextual meaning; a more accurate model reduces the cosine distance between semantically similar texts. Enriching chunk metadata (e.g., document source, date, category) and applying strict filters during retrieval allows the system to pre-filter irrelevant chunks before vector similarity search, effectively narrowing the search space and boosting precision without altering embeddings.
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 more accurate embedding model — Option D is correct because a more accurate embedding model (e.g., OCI Generative AI's embedding models or Cohere embeddings available in OCI) produces higher-quality vector representations that capture semantic meaning more precisely, directly improving retrieval relevance in a RAG pipeline.
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
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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