- A
top_k
Why wrong: top_k controls the number of results, not their diversity.
- B
similarity_search
Why wrong: Similarity search returns top-k most similar results, which can be redundant.
- C
Maximum Marginal Relevance (MMR)
MMR balances relevance and diversity, reducing redundancy among retrieved chunks.
- D
chunk_overlap
Why wrong: Chunk overlap affects chunking, not retrieval diversity.
Question 451 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. 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 developer wants to ensure that a LangChain RetrievalQA chain returns diverse results, avoiding near-duplicate passages in the retrieved context. Which retrieval parameter should be configured on the retriever?
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
✓
Maximum Marginal Relevance (MMR)
Option C is correct because Maximum Marginal Relevance (MMR) is specifically designed to reduce redundancy by balancing relevance with diversity. When configured on a retriever, MMR selects documents that are both relevant to the query and dissimilar to each other, thus avoiding near-duplicate passages in the retrieved context.
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.
- ✗
top_k
Why it's wrong here
top_k controls the number of results, not their diversity.
- ✗
similarity_search
Why it's wrong here
Similarity search returns top-k most similar results, which can be redundant.
- ✓
Maximum Marginal Relevance (MMR)
Why this is correct
MMR balances relevance and diversity, reducing redundancy among retrieved chunks.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
chunk_overlap
Why it's wrong here
Chunk overlap affects chunking, not retrieval diversity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between retrieval parameters that control quantity (top_k) versus those that control quality/diversity (MMR), and candidates mistakenly assume that increasing top_k alone will provide diverse results.
Trap categories for this question
Similar concept trap
Similarity search returns top-k most similar results, which can be redundant.
Detailed technical explanation
How to think about this question
MMR works by first selecting the document most similar to the query, then iteratively adding documents that maximize a combined score of relevance to the query and dissimilarity to already selected documents, controlled by a lambda parameter. In LangChain, MMR is implemented in vector store retrievers (e.g., FAISS, Chroma) via the `fetch_k` and `lambda_mult` parameters, where `fetch_k` determines the initial candidate pool and `lambda_mult` balances relevance vs. diversity. A real-world scenario is a Q&A system over a large corpus of news articles where MMR prevents the same event from being described in multiple similar passages.
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?
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: Maximum Marginal Relevance (MMR) — Option C is correct because Maximum Marginal Relevance (MMR) is specifically designed to reduce redundancy by balancing relevance with diversity. When configured on a retriever, MMR selects documents that are both relevant to the query and dissimilar to each other, thus avoiding near-duplicate passages in the retrieved context.
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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