1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A data scientist is using the OCI Generative AI SDK to create embeddings for a large corpus of legal documents. They want to perform semantic search. Which endpoint should they use?
⚠ Common exam trap
Oracle often tests the distinction between embedding endpoints and generation/classification endpoints, trapping candidates who confuse the purpose of semantic search (which requires embeddings) with text generation or classification tasks.
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
✓
/v1/embed
The /v1/embed endpoint is specifically designed to generate vector embeddings from input text, which are numerical representations that capture semantic meaning. For semantic search over a large corpus of legal documents, embeddings must be created to enable similarity comparisons, making this the correct choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
/v1/classify
Why it's wrong here
The /v1/classify endpoint is for text classification, not for generating vector embeddings.
- ✓
/v1/embed
Why this is correct
The /v1/embed endpoint returns embeddings that can be stored in a vector database and used for semantic search.
- ✗
/v1/generate
Why it's wrong here
The /v1/generate endpoint is for text generation tasks like summarization or completion, not embeddings.
- ✗
/v1/chat
Why it's wrong here
The /v1/chat endpoint is designed for conversational interactions, not for generating embeddings.
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