1Z0-1127-25 OCI Generative AI Service Practice Question
An organization requires low-latency inference for a custom fine-tuned model that will be used in a real-time application. They also need guaranteed availability and isolation from other tenants. Which infrastructure option should they choose?
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 with model units for the fine-tuned model
Dedicated AI Clusters provide isolated, low-latency inference for custom models. Shared infrastructure is multi-tenant and may have higher latency. On-demand tokens and serving endpoints without a cluster do not offer dedicated resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Provision a Dedicated AI Cluster with model units for the fine-tuned model
Why this is correct
A Dedicated AI Cluster provides isolated compute resources, ensuring low latency and dedicated capacity for custom models.
- ✗
Create an endpoint on OCI API Gateway pointing to shared inference
Why it's wrong here
Shared inference lacks dedicated resources; API Gateway does not change the underlying infrastructure.
- ✗
Use the OCI Generative AI Playground to host the model
Why it's wrong here
The Playground is for testing, not production hosting; it does not provide dedicated infrastructure.
- ✗
On-demand token-based inference on shared infrastructure
Why it's wrong here
Shared infrastructure is multi-tenant and does not provide dedicated resources or guaranteed low latency.
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