Building LLM Applications with RAG and Vector Search →mediumMultiple ChoiceObjective-mapped

1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

A company's RAG application ingests news articles that are updated frequently. The vector store in OCI OpenSearch contains embeddings of the articles. The team notices that outdated information is still retrieved even after updating the source documents. What is the most effective way to ensure the vector store reflects the latest content?

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 the OCI OpenSearch document update API to replace embeddings for changed documents

Using the OCI OpenSearch document update API to replace embeddings for changed documents is efficient and targeted, ensuring immediate consistency.

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 TTL for vector indices
Why it's wrong here
TTL controls document expiration, not synchronization with source updates.
✗
Rely on the LLM to ignore outdated information
Why it's wrong here
LLMs cannot automatically determine factuality without retrieval.
✗
Re-index the entire vector store daily
Why it's wrong here
Daily re-indexing is resource-intensive and may not be timely enough for frequent updates.
✓
Use the OCI OpenSearch document update API to replace embeddings for changed documents
Why this is correct
Targeted updates minimize cost and ensure real-time accuracy.

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1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Why each option matters

Option-by-option breakdown

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