- A
Increase the similarity score threshold
Why wrong: Threshold filtering only removes low-similarity documents; it does not diversify the selected set.
- B
Increase chunk_overlap in the text splitter
Why wrong: chunk_overlap affects chunk boundaries, not retrieval diversity; it can increase redundancy if set too high.
- C
Use MMR (maximum marginal relevance) as the search type
MMR explicitly penalizes similarity to already selected documents, promoting a more diverse result set.
- D
Reduce chunk_size in the text splitter
Why wrong: Smaller chunks may reduce overlap but do not directly address duplicate documents at retrieval time.
Question 6 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 observes that their LangChain RAG pipeline sometimes returns duplicate or near-duplicate documents in the retrieved set, reducing the diversity of information provided to the LLM. Which retrieval parameter should they adjust to improve diversity?
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 MMR (maximum marginal relevance) as the search type
MMR (Maximum Marginal Relevance) is a retrieval algorithm that balances relevance with diversity by re-ranking documents based on their similarity to the query and their dissimilarity to already-selected documents. By adjusting the lambda parameter, you control the trade-off between relevance and diversity, directly addressing the problem of duplicate or near-duplicate documents in the retrieved set.
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 similarity score threshold
Why it's wrong here
Threshold filtering only removes low-similarity documents; it does not diversify the selected set.
- ✗
Increase chunk_overlap in the text splitter
Why it's wrong here
chunk_overlap affects chunk boundaries, not retrieval diversity; it can increase redundancy if set too high.
- ✓
Use MMR (maximum marginal relevance) as the search type
Why this is correct
MMR explicitly penalizes similarity to already selected documents, promoting a more diverse result set.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce chunk_size in the text splitter
Why it's wrong here
Smaller chunks may reduce overlap but do not directly address duplicate documents at retrieval time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that adjusting chunk size or overlap can solve retrieval diversity issues, when in fact those parameters affect text splitting, not the retrieval algorithm's ability to select diverse results.
Trap categories for this question
Similar concept trap
Threshold filtering only removes low-similarity documents; it does not diversify the selected set.
Detailed technical explanation
How to think about this question
MMR works by iteratively selecting documents: at each step, it scores candidate documents using a linear combination of their similarity to the query (relevance) and their maximum similarity to any already-selected document (diversity penalty). The lambda parameter (often called 'fetch_k' or 'lambda_mult' in LangChain) controls this balance; a lower lambda emphasizes diversity, while a higher lambda emphasizes relevance. In practice, tuning lambda between 0.5 and 0.7 often yields a good trade-off for RAG pipelines.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
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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: Use MMR (maximum marginal relevance) as the search type — MMR (Maximum Marginal Relevance) is a retrieval algorithm that balances relevance with diversity by re-ranking documents based on their similarity to the query and their dissimilarity to already-selected documents. By adjusting the lambda parameter, you control the trade-off between relevance and diversity, directly addressing the problem of duplicate or near-duplicate documents in the retrieved set.
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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