1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
An organization is deploying a generative AI model that requires GPU acceleration for inference. They are using OCI Data Science Model Deployment. The model is expected to handle variable traffic, with occasional spikes. Which scaling option should they configure to ensure cost-efficiency and responsiveness?
⚠ Common exam trap
Oracle often tests the misconception that serverless endpoints (Option A) are always the best for variable traffic, but candidates must recognize that OCI Generative AI on-demand API is a pre-built model service, not a custom model deployment, and thus does not support custom GPU scaling policies.
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
✓
Configure autoscaling with a minimum of 1 and maximum of 10 GPU instances
Autoscaling with a minimum of 1 and maximum of 10 GPU instances allows the deployment to dynamically adjust capacity in response to variable traffic and spikes, ensuring cost-efficiency by scaling down during low demand and responsiveness by scaling up during peaks. OCI Data Science Model Deployment supports autoscaling policies that can be configured with GPU shapes, making it the optimal choice for a generative AI model requiring GPU acceleration.
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 OCI Generative AI on-demand API with a serverless endpoint
Why it's wrong here
On-demand API uses pre-built models; not for custom models deployed via Data Science.
- ✗
Use CPU-only instances and rely on batching
Why it's wrong here
CPU inference is too slow for large models.
- ✓
Configure autoscaling with a minimum of 1 and maximum of 10 GPU instances
Why this is correct
Autoscaling matches capacity to load.
- ✗
Deploy with a fixed number of 1 GPU instance
Why it's wrong here
Cannot handle spikes without overprovisioning.
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