- A
Threshold-based search
Why wrong: Threshold search returns all chunks above a similarity threshold, which does not enforce diversity.
- B
Maximal Marginal Relevance (MMR)
MMR balances relevance and diversity by iteratively selecting documents that are dissimilar to already chosen ones.
- C
Random sampling of the top-k results
Why wrong: Random sampling does not guarantee diversity; it may still select similar chunks.
- D
Similarity search with a high k value
Why wrong: High k returns many results but they may all be very similar, lacking diversity.
Question 948 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.
In a LangChain RAG pipeline using Oracle AI Vector Search, the developer wants to retrieve chunks that are both relevant and diverse to cover multiple aspects of a query. Which retrieval method should they configure 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
✓
Maximal Marginal Relevance (MMR)
Maximal Marginal Relevance (MMR) is the correct retrieval method because it explicitly balances relevance to the query with diversity among the retrieved chunks. In a LangChain RAG pipeline using Oracle AI Vector Search, MMR re-ranks the initial similarity results to minimize redundancy, ensuring the final set covers multiple aspects of the query rather than returning near-duplicate chunks.
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.
- ✗
Threshold-based search
Why it's wrong here
Threshold search returns all chunks above a similarity threshold, which does not enforce diversity.
- ✓
Maximal Marginal Relevance (MMR)
Why this is correct
MMR balances relevance and diversity by iteratively selecting documents that are dissimilar to already chosen ones.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Random sampling of the top-k results
Why it's wrong here
Random sampling does not guarantee diversity; it may still select similar chunks.
- ✗
Similarity search with a high k value
Why it's wrong here
High k returns many results but they may all be very similar, lacking diversity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that simply increasing k or using a threshold will naturally yield diverse results, but candidates fail to recognize that without an explicit diversity mechanism like MMR, similarity-based retrievers inherently favor redundancy over coverage.
Trap categories for this question
Similar concept trap
Threshold search returns all chunks above a similarity threshold, which does not enforce diversity.
Detailed technical explanation
How to think about this question
MMR works by iteratively selecting chunks that maximize a weighted combination of similarity to the query and dissimilarity to already-selected chunks, using a lambda parameter (often 0.5) to control the trade-off. In Oracle AI Vector Search, this is implemented via the `vector_distance` function combined with a custom re-ranking step, as LangChain's MMR retriever performs an initial ANN search (e.g., using cosine similarity) and then applies the MMR algorithm in-memory. A real-world scenario is a legal document retrieval system where a query like 'breach of contract damages' should return chunks covering different types of damages (compensatory, consequential, punitive) rather than multiple paragraphs on the same type.
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 security administrator must allow nursing staff to reach a patient records server while blocking access from the guest Wi-Fi VLAN. After applying an extended ACL, traffic is still blocked from nursing workstations. The ACL was applied outbound instead of inbound on the wrong interface. Questions like this test ACL direction and placement rules.
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: Maximal Marginal Relevance (MMR) — Maximal Marginal Relevance (MMR) is the correct retrieval method because it explicitly balances relevance to the query with diversity among the retrieved chunks. In a LangChain RAG pipeline using Oracle AI Vector Search, MMR re-ranks the initial similarity results to minimize redundancy, ensuring the final set covers multiple aspects of the query rather than returning near-duplicate chunks.
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.