1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
Which two actions are required when deploying a custom fine-tuned model using the OCI Generative AI service? (Choose two.)
⚠ Common exam trap
Watch out — candidates often confuse the OCI Data Science Model Catalog (used for ML model lifecycle management) with the Generative AI service's own model registration, leading them to incorrectly select Option B.
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
✓
Upload model artifacts to OCI Object Storage
Deploying a custom fine-tuned model in OCI Generative AI requires the model artifacts (e.g., weights, configuration files) to be stored in OCI Object Storage. The service pulls the artifacts from a designated bucket during deployment. Option E is correct because a dedicated AI cluster must be created to host the model, providing the necessary compute resources for inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure an API Gateway for the model endpoint
Why it's wrong here
API Gateway is optional and not required for deployment.
- ✗
Register the model in the OCI Data Science Model Catalog
Why it's wrong here
Registration in the Model Catalog is not mandatory for Generative AI deployment.
- ✗
Set up a load balancer for the deployment
Why it's wrong here
Load balancing is built into the Dedicated AI Cluster; no separate setup needed.
- ✓
Upload model artifacts to OCI Object Storage
Why this is correct
Model artifacts must be stored in Object Storage before deployment.
- ✓
Create a dedicated AI cluster
Why this is correct
A dedicated AI cluster is required to host the custom model.
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