Quick Answer
The answer is OCI OpenSearch. This service is the correct choice because it natively supports hybrid search, allowing you to combine BM25 keyword search with k-NN vector search within a single query, which directly improves retrieval accuracy in a RAG pipeline by leveraging both lexical and semantic relevance. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of which OCI service bridges traditional and AI-driven search; a common trap is confusing OCI OpenSearch with Autonomous Database’s AI Vector Search, which lacks native keyword search capabilities. Remember the memory tip: “OpenSearch opens both doors”—keyword and vector—while other services only open one.
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.
An organization wants to combine keyword search and vector search to improve retrieval accuracy in their RAG pipeline. Which OCI service provides built-in hybrid search capabilities?
OCI Search with AI
Why wrong: This is a search service but not primarily for hybrid search.
OCI OpenSearch
OpenSearch integrates BM25 and vector search.
Autonomous Database with AI Vector Search
Why wrong: Lacks robust keyword search capabilities.
OCI Logging
Why wrong: Unrelated.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
OCI OpenSearch
OCI OpenSearch supports both BM25 keyword search and k-NN vector search in a single query, enabling hybrid search. Autonomous Database with AI Vector Search focuses on vector search but lacks native keyword search. OCI Search is a different service. OCI Logging is for logs.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
OCI Search with AI
Why it's wrong here
This is a search service but not primarily for hybrid search.
OCI OpenSearch
Why this is correct
OpenSearch integrates BM25 and vector search.
Related concept
Static NAT maps one inside address to one outside address.
Autonomous Database with AI Vector Search
Why it's wrong here
Lacks robust keyword search capabilities.
OCI Logging
Why it's wrong here
Unrelated.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Keyword trap
Lacks robust keyword search capabilities.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related 1Z0-1127 NAT questions on configuration and troubleshooting.
Building LLM Applications with RAG and Vector Search — study guide chapter
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Building LLM Applications with RAG and Vector Search practice questions
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Related practice questions
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Practise 1Z0-1127 questions linked to Fundamentals of Large Language Models.
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Practise 1Z0-1127 questions linked to Building LLM Applications with RAG and Vector Search.
Practise 1Z0-1127 questions linked to Deploying and Managing Generative AI on OCI.
Practise 1Z0-1127 questions linked to 1Z0-1127 fundamentals.
Practise 1Z0-1127 questions linked to 1Z0-1127 scenario.
Practise 1Z0-1127 questions linked to 1Z0-1127 troubleshooting.
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FAQ
Building LLM Applications with RAG and Vector Search — This question tests Building LLM Applications with RAG and Vector Search — Static NAT maps one inside address to one outside address..
The correct answer is: OCI OpenSearch — OCI OpenSearch supports both BM25 keyword search and k-NN vector search in a single query, enabling hybrid search. Autonomous Database with AI Vector Search focuses on vector search but lacks native keyword search. OCI Search is a different service. OCI Logging is for logs.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related 1Z0-1127 NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 23, 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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