PMLE Monitoring ML Solutions Practice Question
A financial services company deploys a model on Vertex AI Endpoints with GPU acceleration. They notice that the p99 latency for predictions has increased from 200ms to 1.2s over the past week. CPU utilisation is low, but GPU utilisation is high. Which action should they take to reduce latency?
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
✓
Increase the number of replicas in the endpoint deployment.
High GPU utilisation with low CPU utilisation suggests the model is compute-bound on GPU. Scaling out by adding more replicas distributes the load, reducing queuing and latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the CPU machine type for the endpoint.
Why it's wrong here
CPU is not the bottleneck; GPU is the constraint.
- ✗
Switch to a more powerful GPU type (e.g., from T4 to A100).
Why it's wrong here
While a more powerful GPU could help, scaling out replicas is more cost-effective and immediate.
- ✓
Increase the number of replicas in the endpoint deployment.
Why this is correct
More replicas spread the prediction load, reducing per-request latency.
- ✗
Reduce the sampling rate for monitoring to free up resources.
Why it's wrong here
Monitoring sampling has minimal impact on latency.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.