Quick Answer
The answer is caching frequent queries and their embeddings, as this directly reduces the number of embedding API calls, which is the primary cost driver in a RAG pipeline. By storing the vector representations of common user questions, the application can retrieve them from a local cache instead of repeatedly invoking OCI Generative AI’s embedding model, thereby minimizing per-query expenses while maintaining response quality. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this scenario tests your understanding of practical cost optimization for RAG applications, often appearing as a trap where candidates might mistakenly choose to reduce model size or lower temperature settings—neither of which cuts embedding costs. A key memory tip is to think of the cache as a “reuse, not redo” strategy: every cached embedding is one less API call billed.
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.
A developer wants to deploy a RAG application using OCI Generative AI for both embedding and text generation while minimizing costs. Which strategy is most effective?
Use a larger generation model
Why wrong: Larger generation models increase cost per generation.
Cache frequent queries and their embeddings
Caching reduces redundant embedding API calls, lowering costs.
Reduce chunk size to decrease embedding calls
Why wrong: Smaller chunks may increase the number of chunks and thus embedding calls.
Use a larger embedding model for better accuracy
Why wrong: Larger models cost more per API call.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Cache frequent queries and their embeddings
Caching embeddings for frequent queries eliminates repeated embedding API calls, directly reducing cost.
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.
Use a larger generation model
Why it's wrong here
Larger generation models increase cost per generation.
Cache frequent queries and their embeddings
Why this is correct
Caching reduces redundant embedding API calls, lowering costs.
Related concept
Static NAT maps one inside address to one outside address.
Reduce chunk size to decrease embedding calls
Why it's wrong here
Smaller chunks may increase the number of chunks and thus embedding calls.
Use a larger embedding model for better accuracy
Why it's wrong here
Larger models cost more per API call.
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.
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.
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Practise 1Z0-1127 questions linked to Fundamentals of Large Language Models.
Practise 1Z0-1127 questions linked to Using OCI Generative AI Service.
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: Cache frequent queries and their embeddings — Caching embeddings for frequent queries eliminates repeated embedding API calls, directly reducing cost.
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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