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

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

A developer wants to implement a simple RAG pipeline using OCI Language's text generation and embedding models. Which OCI SDK method is used to generate embeddings for a text chunk?

⚠ Common exam trap

A common mix-up: candidates confuse OCI SDK method names with those from other cloud providers (e.g., OpenAI's `create_embedding` or generic `encode_text`), leading them to select a plausible-sounding but incorrect option.

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

✓

embed_text

The correct OCI SDK method for generating embeddings for a text chunk is `embed_text`. This method is part of the OCI Language service's `AIServiceLanguageClient` and directly returns vector representations of input text, which are essential for RAG pipelines to enable semantic search and retrieval.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
embed_text
Why this is correct
`embed_text` is the correct method to call for generating embeddings from text.
✗
generate_embeddings
Why it's wrong here
This method name is not used in OCI Language SDK.
✗
encode_text
Why it's wrong here
`encode_text` is not a method in OCI Language; it's used in other libraries.
✗
create_embedding
Why it's wrong here
This is not a recognized method in OCI Language SDK.

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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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