- A
Horizontal Pod Autoscaler (HPA) with custom metrics
HPA scales the number of pods based on metrics like latency, which is what the team needs.
- B
Kubeflow Pipelines component
Why wrong: Kubeflow Pipelines orchestrates ML workflows, not autoscaling.
- C
Cluster Autoscaler
Why wrong: Cluster Autoscaler adds or removes nodes, not pods.
- D
Vertical Pod Autoscaler (VPA)
Why wrong: VPA adjusts resource requests/limits, not the number of pods.
Question 217 of 1,000
AI0-001 AI Infrastructure and Technologies Practice Question
This AI0-001 practice question tests your understanding of ai infrastructure and technologies. 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.
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?
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.
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.
- ✓
Horizontal Pod Autoscaler (HPA) with custom metrics
Why this is correct
HPA scales the number of pods based on metrics like latency, which is what the team needs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Kubeflow Pipelines component
Why it's wrong here
Kubeflow Pipelines orchestrates ML workflows, not autoscaling.
- ✗
Cluster Autoscaler
Why it's wrong here
Cluster Autoscaler adds or removes nodes, not pods.
- ✗
Vertical Pod Autoscaler (VPA)
Why it's wrong here
VPA adjusts resource requests/limits, not the number of pods.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between pod-level scaling (HPA) and node-level scaling (Cluster Autoscaler), and the trap here is that candidates may confuse Cluster Autoscaler with pod autoscaling, or assume VPA can handle latency-based scaling when it only adjusts resource limits.
Detailed technical explanation
How to think about this question
Under the hood, HPA with custom metrics works by querying a metrics API (e.g., custom.metrics.k8s.io) that aggregates data from sources like Prometheus. The HPA controller calculates the desired replica count using the formula: desiredReplicas = currentReplicas × (currentMetricValue / targetMetricValue). In a real-world scenario, if latency spikes due to a traffic burst, HPA can rapidly scale out pods, but it must be tuned with a stabilization window to avoid thrashing from transient latency fluctuations.
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 AI0-001 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.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Infrastructure and Technologies — This question tests AI Infrastructure and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI0-001 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.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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