Describe AI cluster orchestration and job scheduling: Practice Questions — AI Operations (NVIDIA-Certified Associate: AI Infrastructure and Operations)

Practice Questions: AI Cluster Orchestration and Job Scheduling

This set of multiple-choice questions is designed to help candidates prepare for the NVIDIA-Certified Associate: AI Infrastructure and Operations exam, focusing specifically on AI cluster orchestration and job scheduling.

1. Which of the following best describes the primary purpose of AI cluster orchestration?

   • A. Managing the lifecycle of AI models in production environments
   • B. Automating deployment, scaling, and management of containerized AI workloads across multiple nodes
   • C. Monitoring GPU temperature and power consumption
   • D. Virtualizing AI infrastructure for multi-tenant use

   Correct answer: B

   Explanation: AI cluster orchestration automates deployment, scaling, and management of containerized AI workloads across multiple nodes, ensuring efficient resource utilization and workload distribution.

2. In job scheduling for AI workloads, what is the main advantage of using a priority-based scheduling algorithm?

   • A. It guarantees equal resource allocation to all jobs
   • B. It allows critical or time-sensitive jobs to be executed before less important ones
   • C. It reduces GPU power consumption
   • D. It eliminates the need for cluster orchestration tools

   Correct answer: B

   Explanation: Priority-based scheduling ensures that critical or time-sensitive AI jobs are executed ahead of lower-priority tasks, improving overall system responsiveness and meeting SLAs.

3. Which Kubernetes component is primarily responsible for scheduling AI jobs onto appropriate nodes in a cluster?

   • A. kubelet
   • B. kube-proxy
   • C. kube-scheduler
   • D. etcd

   Correct answer: C

   Explanation: The kube-scheduler is the Kubernetes component that assigns AI jobs (pods) to nodes based on resource availability and scheduling policies.

4. When orchestrating AI workloads, what is a key consideration when scheduling jobs that require GPU resources?

   • A. Ensuring jobs are scheduled only on CPU nodes
   • B. Allocating GPUs exclusively to a single job to avoid contention
   • C. Ignoring GPU memory requirements
   • D. Scheduling jobs without regard to GPU availability

   Correct answer: B

   Explanation: GPU resources are typically allocated exclusively to individual jobs to prevent contention and ensure optimal performance for AI workloads.

5. Which of the following is NOT a common feature of AI job schedulers in cluster environments?

   • A. Job queue management
   • B. Resource-aware scheduling
   • C. Automated model training optimization
   • D. Job prioritization and preemption

   Correct answer: C

   Explanation: Automated model training optimization is usually handled by AI frameworks or training platforms, not by the job scheduler itself, which focuses on resource allocation and job management.

6. In the context of AI cluster orchestration, what is the benefit of using namespaces?

   • A. They enable physical separation of GPUs
   • B. They provide logical isolation of resources and workloads within the same cluster
   • C. They increase GPU clock speeds
   • D. They automatically schedule jobs to the fastest nodes

   Correct answer: B

   Explanation: Namespaces provide logical isolation within a cluster, allowing multiple teams or projects to share the same physical infrastructure without interfering with each other.

7. Which scheduling approach helps to improve cluster utilization by allowing smaller AI jobs to fill resource gaps left by larger jobs?

   • A. First-Come, First-Served scheduling
   • B. Bin packing scheduling
   • C. Round-robin scheduling
   • D. Random scheduling

   Correct answer: B

   Explanation: Bin packing scheduling attempts to pack jobs tightly into nodes to maximize resource utilization, filling gaps with smaller jobs efficiently.

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