1Z0-1127-25 OCI Generative AI Service Practice Question
A data scientist has fine-tuned a Cohere Command R model using the T-Few technique. They now need to deploy this custom model for real-time inference with low latency. What is the recommended deployment option in OCI Generative AI?
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
✓
Provision a dedicated AI cluster and host the fine-tuned model on it
Dedicated AI clusters provide isolated, low-latency inference for custom fine-tuned models. Shared infrastructure is multi-tenant and may have variable latency; on-demand inference does not support custom models directly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the OCI Generative AI Playground to test the model
Why it's wrong here
The Playground is for interactive testing of base models, not for deploying custom fine-tuned models for production inference.
- ✓
Provision a dedicated AI cluster and host the fine-tuned model on it
Why this is correct
Dedicated AI clusters allow you to deploy your own fine-tuned models with low latency and dedicated compute resources, suitable for production real-time inference.
- ✗
Use the shared infrastructure endpoint with an API call
Why it's wrong here
Shared infrastructure serves only base models; fine-tuned models require a dedicated cluster.
- ✗
Create an InferenceClient pointing to the fine-tuned model directly without a cluster
Why it's wrong here
The InferenceClient needs an endpoint that points to either a base model or a dedicated cluster; custom models must be hosted on a dedicated cluster.
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