The answer is to use GPU utilization as the scaling metric instead of CPU utilization. This is correct because a large language model that heavily utilizes the GPU creates a GPU-bound workload, where the GPU is the primary bottleneck for inference throughput. Autoscaling based on average CPU utilization is irrelevant for such models, as the CPU remains underutilized while the GPU is saturated, causing delayed scale-out and high latency during peak demand. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of matching autoscaling metrics to actual resource bottlenecks in OCI Model Deployment, a common trap being the default reliance on CPU metrics for GPU-intensive tasks. A helpful memory tip is “match the metric to the muscle”—if the model flexes the GPU, scale by GPU utilization, not CPU.
1Z0-1127 Deploying and Managing Generative AI on OCI Practice Question
This 1Z0-1127 practice question tests your understanding of deploying and managing generative ai on oci. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the cooldown period between scale-out events
Why it's wrong here
Increasing cooldown delays scaling, making the problem worse.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
OCI Model Deployment autoscaling uses a target metric (e.g., average CPU utilization) to trigger scale-out events. For GPU-intensive LLMs, CPU utilization stays low even under heavy GPU load, so the autoscaler remains idle. By switching to GPU utilization, the autoscaler can react to the actual resource pressure. Additionally, OCI supports custom metrics via the Monitoring service, allowing you to define GPU utilization as a scaling metric for more precise control.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deploying and Managing Generative AI on OCI — This question tests Deploying and Managing Generative AI on OCI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use GPU utilization as the scaling metric instead of CPU utilization — Option C is correct because 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.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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