1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company is deploying a generative AI model for a real-time inference API. To ensure high availability and cost efficiency under variable load, which two configurations should they implement? (Choose two.)
⚠ Common exam trap
Oracle often tests the misconception that a single, powerful GPU instance is sufficient for high availability, but the exam expects you to recognize that redundancy through multiple replicas and autoscaling are required for both availability and cost efficiency under variable load.
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 the number of model deployment replicas to at least 2
Deploying at least two replicas ensures high availability by eliminating a single point of failure; if one replica fails, the other can still serve inference requests. This is a standard best practice for production workloads on OCI, where model deployment replicas are distributed across fault domains to maintain service continuity.
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 a single replica with a larger GPU to handle all traffic
Why it's wrong here
A single replica is a single point of failure and cannot handle high availability.
- ✗
Deploy the model in a single availability domain to simplify management
Why it's wrong here
Deploying in a single AD increases risk of outage if that AD fails.
- ✗
Disable connection draining on the load balancer
Why it's wrong here
Disabling connection draining may cause requests to fail during scale-in.
- ✓
Set the number of model deployment replicas to at least 2
Why this is correct
Multiple replicas provide redundancy and high availability.
- ✓
Enable autoscaling based on average CPU utilization
Why this is correct
Autoscaling adjusts replicas based on load, balancing cost and performance.
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