1Z0-1127-25 OCI Generative AI Service Practice Question
An enterprise needs to deploy a custom fine-tuned model for real-time inference with strict latency requirements. They also need to manage costs by paying only for usage. Which three steps are required to achieve this? (Select THREE)
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
✓
Create an endpoint using InferenceClient to point to the dedicated cluster
To deploy a custom model for dedicated low-latency inference, you need a Dedicated AI Cluster, host the model on it, and create an endpoint to access it. Using shared infrastructure does not guarantee low latency. Deletion of cluster is not needed. On-demand tokens are for pay-as-you-go, not dedicated clusters.
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 on-demand token-based inference on shared infrastructure
Why it's wrong here
Shared infrastructure may have higher latency and is not dedicated.
- ✓
Create an endpoint using InferenceClient to point to the dedicated cluster
Why this is correct
An endpoint enables API calls to the model hosted on the cluster.
- ✗
Delete the fine-tuning job after deployment to save costs
Why it's wrong here
Deleting the job does not affect inference costs; the cluster cost remains.
- ✓
Provision a Dedicated AI Cluster with sufficient model units
Why this is correct
A dedicated cluster provides isolated resources for low-latency inference.
- ✓
Host the fine-tuned model on the Dedicated AI Cluster
Why this is correct
Deploying the model to the cluster makes it available for inference.
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