- A
Increase the 'chunk_size' in the text splitter
Why wrong: chunk_size affects document granularity, not retrieval diversity.
- B
Set the 'k' parameter to a very high number
Why wrong: Increasing k only retrieves more documents, but without diversity control they may be redundant.
- C
Use a different embedding model
Why wrong: A different embedding model changes the representation but does not directly enforce diversity in retrieval.
- D
Set the 'fetch_k' parameter to a value larger than 'k'
fetch_k retrieves a larger initial set from which MMR can choose a diverse subset.
- E
Enable MMR (maximum marginal relevance) in the retriever
MMR selects documents that are both relevant to the query and diverse from each other.
Question 530 of 991
1Z0-1127 LangChain and AI Application Development Practice Question
This 1Z0-1127 practice question tests your understanding of langchain and ai application development. 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.
A developer is using LangChain's RetrievalQA chain with a vector store. They want to improve the diversity of retrieved documents to avoid redundant information. Which TWO parameters or methods should they adjust?
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
✓
Set the 'fetch_k' parameter to a value larger than 'k'
Option D is correct because setting 'fetch_k' to a value larger than 'k' allows the retriever to first fetch a larger pool of candidate documents (fetch_k) and then apply a diversity-promoting algorithm like MMR to select the final 'k' documents. This reduces redundancy by ensuring the returned set is not dominated by similar documents from the same region of the vector space.
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' in the text splitter
Why it's wrong here
chunk_size affects document granularity, not retrieval diversity.
- ✗
Set the 'k' parameter to a very high number
Why it's wrong here
Increasing k only retrieves more documents, but without diversity control they may be redundant.
- ✗
Use a different embedding model
Why it's wrong here
A different embedding model changes the representation but does not directly enforce diversity in retrieval.
- ✓
Set the 'fetch_k' parameter to a value larger than 'k'
Why this is correct
fetch_k retrieves a larger initial set from which MMR can choose a diverse subset.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable MMR (maximum marginal relevance) in the retriever
Why this is correct
MMR selects documents that are both relevant to the query and diverse from each other.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that simply increasing the number of retrieved documents (k) improves diversity, when in fact without MMR or a similar algorithm, more documents often means more redundancy.
Detailed technical explanation
How to think about this question
Maximum Marginal Relevance (MMR) works by iteratively selecting documents that are both relevant to the query and dissimilar to already-selected documents, balancing relevance and diversity via a lambda parameter. The 'fetch_k' parameter is used in conjunction with MMR to first retrieve a large candidate set (e.g., fetch_k=50) and then apply MMR to select the final 'k' (e.g., k=5), ensuring the final set covers multiple topics or perspectives. In real-world RAG systems, this is critical when the vector store contains many near-duplicate passages from similar sources, such as multiple FAQ entries about the same error code.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
LangChain and AI Application Development — study guide chapter
Learn the concepts, then practise the questions
- →
LangChain and AI Application Development practice questions
Targeted practice on this topic area only
- →
All 1Z0-1127 questions
991 questions across all exam domains
- →
Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 study guide
Full concept coverage aligned to exam objectives
- →
1Z0-1127 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related 1Z0-1127 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Prompt Engineering practice questions
Practise 1Z0-1127 questions linked to Prompt Engineering.
OCI Generative AI Service practice questions
Practise 1Z0-1127 questions linked to OCI Generative AI Service.
LLM Fundamentals practice questions
Practise 1Z0-1127 questions linked to LLM Fundamentals.
LangChain and AI Application Development practice questions
Practise 1Z0-1127 questions linked to LangChain and AI Application Development.
Fundamentals of Large Language Models practice questions
Practise 1Z0-1127 questions linked to Fundamentals of Large Language Models.
Using OCI Generative AI Service practice questions
Practise 1Z0-1127 questions linked to Using OCI Generative AI Service.
Building LLM Applications with RAG and Vector Search practice questions
Practise 1Z0-1127 questions linked to Building LLM Applications with RAG and Vector Search.
Deploying and Managing Generative AI on OCI practice questions
Practise 1Z0-1127 questions linked to Deploying and Managing Generative AI on OCI.
1Z0-1127 fundamentals practice questions
Practise 1Z0-1127 questions linked to 1Z0-1127 fundamentals.
1Z0-1127 scenario practice questions
Practise 1Z0-1127 questions linked to 1Z0-1127 scenario.
1Z0-1127 troubleshooting practice questions
Practise 1Z0-1127 questions linked to 1Z0-1127 troubleshooting.
Practice this exam
Start a free 1Z0-1127 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
LangChain and AI Application Development — This question tests LangChain and AI Application Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set the 'fetch_k' parameter to a value larger than 'k' — Option D is correct because setting 'fetch_k' to a value larger than 'k' allows the retriever to first fetch a larger pool of candidate documents (fetch_k) and then apply a diversity-promoting algorithm like MMR to select the final 'k' documents. This reduces redundancy by ensuring the returned set is not dominated by similar documents from the same region of the vector space.
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 →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.