AI0-001 AI Infrastructure and Technologies Practice Question
A team is deploying a model on Kubernetes using Kubeflow. They want to automatically scale the number of inference pods based on request latency. Which Kubernetes-native feature should they configure?
⚠ Common exam trap
The distinction between pod-level scaling (HPA) and node-level scaling (Cluster Autoscaler) is important. The trap is that candidates may confuse Cluster Autoscaler with pod autoscaling, or assume VPA can handle latency-based scaling when it only adjusts resource limits.
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
✓
Horizontal Pod Autoscaler (HPA) with custom metrics
The Horizontal Pod Autoscaler (HPA) with custom metrics is the correct choice because it allows scaling based on application-level metrics like request latency, not just CPU or memory. By configuring HPA to use a custom metric (e.g., from Prometheus or a metrics adapter), the team can automatically adjust the number of inference pods to maintain target latency thresholds, which is essential for responsive inference serving.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Horizontal Pod Autoscaler (HPA) with custom metrics
Why this is correct
HPA scales pod replicas from metrics, and custom metrics let it target request latency rather than CPU. This satisfies the latency-based scaling constraint, since the default resource metrics cannot express latency and would not react to slow inference responses.
- ✗
Kubeflow Pipelines component
Why it's wrong here
Kubeflow Pipelines components orchestrate ML workflow steps such as training and evaluation; they do not scale running inference pods. It is tempting because Kubeflow is already in use, but pipeline components execute DAG tasks, not Kubernetes workload autoscaling driven by latency metrics.
- ✗
Cluster Autoscaler
Why it's wrong here
Cluster Autoscaler adjusts node count when pods cannot be scheduled, not pod replicas from latency metrics. It is tempting because it scales capacity, but it reacts to pending pods and node pressure, not request latency, so it would not add inference pods in response to latency thresholds.
- ✗
Vertical Pod Autoscaler (VPA)
Why it's wrong here
VPA adjusts CPU and memory requests for existing pods; it does not change replica counts. Latency-driven scaling requires the Horizontal Pod Autoscaler, which adds or removes pods based on metrics. VPA suits right-sizing resource requests for workloads with stable replica counts.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.