Deploy inference workloads with Kubernetes and Run:ai: Practice Questions — Workload Management (NVIDIA-Certified Professional: AI Operations)

Practice Questions: Deploy Inference Workloads with Kubernetes and Run:AI

These multiple-choice questions are designed to help candidates prepare for the Deploy inference workloads with Kubernetes and Run:AI section of the NVIDIA-Certified Professional: AI Operations exam. Each question includes four options, the correct answer, and a brief explanation.

1. Which Kubernetes resource is primarily used to manage and scale inference workloads in a cluster?

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

   Correct Answer: B. Deployment

   Explanation: A Deployment manages stateless applications like inference workloads by providing declarative updates and scaling capabilities, making it ideal for managing inference services.

2. In Run:AI, what is the primary benefit of using virtual clusters when deploying inference workloads?

   • A. Isolating workloads by project or team
   • B. Increasing GPU clock speeds
   • C. Automatically converting models to ONNX format
   • D. Managing Kubernetes namespaces

   Correct Answer: A. Isolating workloads by project or team

   Explanation: Run:AI virtual clusters allow resource partitioning and isolation, enabling teams to share physical GPU resources securely and efficiently.

3. Which Kubernetes object would you use to expose an inference workload externally to clients?

   • A. PersistentVolumeClaim
   • B. Ingress
   • C. StatefulSet
   • D. Secret

   Correct Answer: B. Ingress

   Explanation: An Ingress resource manages external access to services in a Kubernetes cluster, typically HTTP/HTTPS, making it suitable for exposing inference APIs.

4. What is the main function of Run:AI’s scheduler when deploying inference workloads on Kubernetes?

   • A. Automatically retraining AI models
   • B. Optimizing GPU resource allocation and job prioritization
   • C. Managing Kubernetes cluster upgrades
   • D. Monitoring network traffic

   Correct Answer: B. Optimizing GPU resource allocation and job prioritization

   Explanation: Run:AI’s scheduler intelligently allocates GPU resources and prioritizes workloads to maximize utilization and meet SLAs for inference jobs.

5. Which command-line tool is commonly used to deploy and manage Kubernetes inference workloads?

   • A. docker-compose
   • B. kubectl
   • C. helm
   • D. nvidia-smi

   Correct Answer: B. kubectl

   Explanation: kubectl is the primary CLI tool for interacting with Kubernetes clusters, including deploying and managing inference workloads.

6. When deploying an inference workload with Run:AI on Kubernetes, which of the following is essential to ensure GPU resources are properly requested?

   • A. Setting resources.requests.nvidia.com/gpu in the pod spec
   • B. Using a ConfigMap to define GPU limits
   • C. Installing NVIDIA drivers inside each container
   • D. Specifying CPU limits only

   Correct Answer: A. Setting resources.requests.nvidia.com/gpu in the pod spec

   Explanation: Kubernetes requires explicit GPU resource requests in the pod specification to schedule workloads on GPU nodes; Run:AI leverages this for GPU allocation.

7. Which Run:AI feature helps to automatically scale inference workloads based on demand?

   • A. Run:AI AutoML
   • B. Run:AI Elasticity
   • C. Run:AI Virtual Clusters
   • D. Run:AI Model Registry

   Correct Answer: B. Run:AI Elasticity

   Explanation: Run:AI Elasticity enables dynamic scaling of workloads, adjusting resource allocation to meet changing inference demand efficiently.

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