AI-200 Containerized AI Workloads Practice Question
You are running a GPU-intensive AI workload in AKS. You want to ensure the pods are scheduled only on nodes that have GPUs. How do you enforce this?
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
✓
Use a nodeSelector for 'sku=gpu'.
Using nodeSelectors or nodeAffinity is the standard Kubernetes way to restrict pod placement to specific node types.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a DaemonSet.
Why it's wrong here
DaemonSets run on all nodes.
- ✓
Use a nodeSelector for 'sku=gpu'.
Why this is correct
nodeSelectors ensure the pod only lands on labeled GPU nodes.
- ✗
Define a Resource Request for RAM.
Why it's wrong here
RAM requests do not filter for GPU capability.
- ✗
Add a Kubernetes Taint to the GPU nodes.
Why it's wrong here
Taints repel pods; you still need tolerations and selectors.
About these practice questions
This AI-200 question is part of Courseiva's 503-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-200 practice question is part of Courseiva's free Microsoft 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 AI-200 exam.