1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A startup needs to deploy a large language model for a customer support chatbot that requires low latency and cost efficiency. They are evaluating OCI Generative AI models. Which model type is most appropriate?
⚠ Common exam trap
Oracle often tests the distinction between base models and instruct models, trapping candidates who assume a base model can be used directly for task-specific applications without fine-tuning or instruction alignment.
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
✓
Instruct model (e.g., cohere.command)
The startup requires low latency and cost efficiency for a customer support chatbot. Instruct models like cohere.command are specifically fine-tuned to follow conversational instructions and generate concise, task-oriented responses, making them ideal for interactive chatbot applications. They balance performance and cost better than base models, which lack instruction-following capability, and embedding models, which are designed for semantic search rather than text generation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Embedding model (e.g., cohere.embed)
Why it's wrong here
Embedding models convert text to vectors, not for generating responses.
- ✓
Instruct model (e.g., cohere.command)
Why this is correct
Instruct models are fine-tuned to follow instructions, making them ideal for chatbots.
- ✗
Image generation model
Why it's wrong here
Image generation models are for creating images, not text-based customer support.
- ✗
Base model (e.g., cohere.base)
Why it's wrong here
Base models are not optimized for instruction-following; they produce open-ended completions.
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