1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
You deployed a generative AI model on OCI Model Deployment with autoscaling configured based on average CPU utilization. The model is a large language model that heavily utilizes the GPU. During peak hours, the scaling is too slow to keep up with demand, resulting in high latency for users. You want to improve the responsiveness of autoscaling. Which change should you make?
⚠ Common exam trap
Many exam-takers assume CPU utilization is always the correct scaling metric for any workload, overlooking that GPU-bound models require a metric that reflects the actual bottleneck.
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
✓
Use GPU utilization as the scaling metric instead of CPU utilization
The model heavily utilizes GPU, not CPU. Autoscaling based on CPU utilization is irrelevant for GPU-bound workloads, leading to delayed scale-out. Using GPU utilization as the scaling metric directly reflects the actual resource bottleneck, enabling faster and more accurate scaling decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the target CPU utilization threshold for scale-out
Why it's wrong here
CPU utilization is not a good indicator for GPU workload; this change may not help.
- ✗
Increase the maximum number of replicas in the autoscaling configuration
Why it's wrong here
Increasing max replicas does not speed up scaling; it only allows more replicas eventually.
- ✓
Use GPU utilization as the scaling metric instead of CPU utilization
Why this is correct
GPU utilization directly correlates with inference load, enabling more responsive scaling.
- ✗
Increase the cooldown period between scale-out events
Why it's wrong here
Increasing cooldown delays scaling, making the problem worse.
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