1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A data scientist needs to generate vector embeddings for a large corpus of text documents to use in a semantic search application. Which OCI service is best suited for this task?
⚠ Common exam trap
Oracle often tests the misconception that OCI Language can generate embeddings because it handles text, but OCI Language lacks an embedding API, while OCI Generative AI is the only service that provides this capability for semantic search.
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
✓
OCI Generative AI
OCI Generative AI is the correct choice because it provides a managed service for generating vector embeddings from text using large language models (LLMs) like Cohere. This service is specifically designed for tasks such as semantic search, where embeddings capture the meaning of text to enable similarity comparisons. OCI Vision, Speech, and Language focus on other modalities (images, audio, and NLP tasks like sentiment analysis) and do not offer embedding generation for semantic search.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
OCI Vision
Why it's wrong here
OCI Vision is for image analysis, not text embeddings.
- ✗
OCI Speech
Why it's wrong here
OCI Speech is for audio transcription, not text embeddings.
- ✓
OCI Generative AI
Why this is correct
OCI Generative AI offers embedding models (e.g., Cohere embed) specifically for text.
- ✗
OCI Language
Why it's wrong here
OCI Language provides NLP capabilities but not embedding generation as a primary service.
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