Administer Kubernetes environments: Practice Questions — Administration (NVIDIA-Certified Professional: AI Operations)

Practice Questions: Administer Kubernetes Environments for NVIDIA-Certified Professional: AI Operations

This set of multiple-choice questions is designed to help candidates prepare for the Administer Kubernetes Environments section of the NVIDIA-Certified Professional: AI Operations exam. Each question focuses on key tasks and concepts relevant to managing Kubernetes clusters in AI infrastructure contexts.

1. Which Kubernetes resource is primarily used to manage GPU resource allocation for AI workloads?

   • A. ConfigMap
   • B. PersistentVolumeClaim
   • C. Device Plugin
   • D. StatefulSet

   Correct answer: C

   Explanation: The Device Plugin framework in Kubernetes enables the scheduling and management of specialized hardware like GPUs by advertising available devices to the kubelet and allowing pods to request GPU resources.

2. What is the recommended method to isolate multiple AI workloads on a single NVIDIA GPU within Kubernetes?

   • A. Use Multi-Instance GPU (MIG) profiles
   • B. Deploy separate namespaces
   • C. Configure pod affinity rules
   • D. Use PersistentVolumeClaims

   Correct answer: A

   Explanation: MIG allows a single physical GPU to be partitioned into multiple isolated instances, enabling multiple AI workloads to run concurrently with guaranteed resource isolation.

3. Which Kubernetes object would you configure to ensure AI workloads automatically restart on failure?

   • A. Pod
   • B. Deployment
   • C. Service
   • D. ConfigMap

   Correct answer: B

   Explanation: A Deployment manages replica sets and ensures pods are restarted automatically if they fail, providing high availability for AI workloads.

4. How can you monitor GPU utilization metrics within a Kubernetes cluster running AI workloads?

   • A. Use Prometheus with NVIDIA DCGM exporter
   • B. Enable Kubernetes Horizontal Pod Autoscaler
   • C. Configure Kubernetes Network Policies
   • D. Use Kubernetes Secrets

   Correct answer: A

   Explanation: The NVIDIA Data Center GPU Manager (DCGM) exporter integrates with Prometheus to collect GPU metrics, enabling monitoring of GPU utilization in Kubernetes environments.

5. Which command is used to apply a YAML configuration file to create or update Kubernetes resources for AI workloads?

   • A. kubectl get
   • B. kubectl apply
   • C. kubectl describe
   • D. kubectl delete

   Correct answer: B

   Explanation: kubectl apply creates or updates Kubernetes resources declaratively from a YAML configuration file, which is the standard method for managing AI workload deployments.

6. What is the purpose of a Kubernetes Namespace when administering AI workloads?

   • A. To isolate resources and manage access control
   • B. To allocate GPU resources
   • C. To schedule pods on specific nodes
   • D. To store container images

   Correct answer: A

   Explanation: Namespaces provide a mechanism to isolate resources and apply role-based access control (RBAC) policies, which helps manage multi-tenant AI workloads securely.

7. Which Kubernetes feature allows AI workloads to scale automatically based on GPU utilization?

   • A. StatefulSets
   • B. Horizontal Pod Autoscaler (HPA)
   • C. DaemonSets
   • D. ReplicaSets

   Correct answer: B

   Explanation: The Horizontal Pod Autoscaler can be configured with custom metrics such as GPU utilization to dynamically scale AI workload pods in response to demand.

8. When configuring Multi-Instance GPU (MIG) in Kubernetes, which NVIDIA component must be installed on the cluster nodes?

   • A. NVIDIA Container Toolkit
   • B. NVIDIA Device Plugin for Kubernetes
   • C. NVIDIA CUDA Toolkit
   • D. NVIDIA Triton Inference Server

   Correct answer: B

   Explanation: The NVIDIA Device Plugin for Kubernetes supports MIG and is required to advertise MIG partitions as schedulable resources to Kubernetes, enabling proper GPU resource management.

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