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PDE Practice Question: A machine learning engineer at a FinTech company
You are a machine learning engineer at a FinTech company. Your team has developed a credit risk model using XGBoost and deployed it on Vertex AI Prediction using a custom container. The model is used for real-time credit decisions, and the endpoint is configured with a single machine type (n1-standard-4) and min_replica_count = 2, max_replica_count = 10. Recently, the team observed that during a promotional campaign, the endpoint's prediction latency increased from 200ms to over 2 seconds, and some requests resulted in 503 errors. You check the Cloud Monitoring metrics and see that CPU utilization reached 100% on the existing replicas, but the number of replicas never scaled beyond the initial 2. The deployment uses a custom container that runs a TensorFlow Serving-like model server. The container image is stored in Artifact Registry. The Vertex AI endpoint is configured with a traffic split of 100% to this model version. What is the most likely cause of the scaling failure, and what step should you take to resolve it?
⚠ Common exam trap
Candidates often assume autoscaling is automatic based on CPU utilization alone, but Vertex AI requires explicit metric exposure from custom containers; otherwise, the autoscaler remains inactive.
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
✓
Ensure the custom container exposes the correct metrics for CPU utilization so that Vertex AI autoscaling can trigger.
Vertex AI's autoscaling relies on the custom container exposing standard metrics (e.g., CPU utilization via the /metrics endpoint in a Prometheus format or through the Vertex AI custom metric adapter). If the container does not expose these metrics, the autoscaler cannot detect high CPU usage and will not trigger scaling beyond the initial replicas, leading to latency spikes and 503 errors under load.
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 min_replica_count to 5 to handle the baseline load.
Why it's wrong here
This would increase cost but not fix the scaling failure; the system should scale dynamically.
- ✗
Change the endpoint configuration to use gRPC instead of HTTP to reduce latency.
Why it's wrong here
Latency is a symptom, not the root cause; scaling is the issue.
- ✓
Ensure the custom container exposes the correct metrics for CPU utilization so that Vertex AI autoscaling can trigger.
Why this is correct
Autoscaling relies on metrics; if the container doesn't expose them, scaling won't happen.
- ✗
Set the max_replica_count to a higher value like 20.
Why it's wrong here
The issue is that replicas didn't scale at all, not the max limit.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.