1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
Which THREE of the following are best practices when deploying a generative AI model on OCI?
⚠ Common exam trap
Oracle often tests the misconception that disabling logging is a valid cost-saving measure, but in reality, logging is essential for operational visibility and compliance, and costs can be managed through sampling or retention policies rather than outright disabling.
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
✓
Set up autoscaling for the endpoint.
Autoscaling ensures that the generative AI endpoint can dynamically adjust compute resources based on real-time inference traffic, maintaining low latency and high availability while optimizing cost. On OCI, autoscaling policies can be configured for dedicated AI clusters to scale the number of model serving replicas in response to metrics like CPU utilization or request queue depth.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store API keys in the model endpoint configuration.
Why it's wrong here
API keys should be stored securely in OCI Vault, not in configuration.
- ✓
Set up autoscaling for the endpoint.
Why this is correct
Autoscaling handles variable load efficiently.
- ✗
Disable logging to save costs.
Why it's wrong here
Logging is essential for troubleshooting and auditing.
- ✓
Use a dedicated AI cluster for production endpoints.
Why this is correct
Dedicated clusters provide isolated and consistent performance.
- ✓
Enable content filtering on the endpoint.
Why this is correct
Content filtering helps prevent harmful outputs.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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