1Z0-1127-25 OCI Generative AI Service Practice Question
A financial services company must deploy a fine-tuned model for transaction categorization. The model must be isolated from other tenants and provide predictable low-latency inference. The compliance team also requires that training data never leaves the OCI tenancy. Which THREE steps should the team take? (Choose 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
✓
Fine-tune the model using T-Few technique within OCI
A dedicated AI cluster provides isolation and predictable low latency. T-Few fine-tuning runs entirely within OCI, keeping data in tenancy. Model units are required for dedicated cluster provisioning. Shared infrastructure would compromise isolation. On-demand inference does not guarantee low latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fine-tune the model using T-Few technique within OCI
Why this is correct
T-Few fine-tuning runs inside OCI, ensuring data does not leave the tenancy.
- ✗
Use OCI GenAI on-demand inference for the fine-tuned model
Why it's wrong here
On-demand inference uses shared infrastructure, violating isolation and latency requirements.
- ✓
Ensure the model is deployed on the dedicated cluster after fine-tuning
Why this is correct
Deploying the fine-tuned model on the dedicated cluster meets isolation and latency goals.
- ✓
Provision a dedicated AI cluster with model units
Why this is correct
Dedicated cluster ensures isolation and low-latency inference.
- ✗
Host the model on a shared AI cluster to reduce cost
Why it's wrong here
Shared cluster does not provide isolation and may have variable latency.
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