NCP-AIO Practice Questions: Workload Management Domain

Published: November 15, 2025 | 20 min read

Test your NCP-AIO knowledge with 10 practice questions from the Workload Management domain. Includes detailed explanations and answers.

NCP-AIO Practice Questions

Master the Workload Management Domain

Test your knowledge in the Workload Management domain with these 10 practice questions. Each question is designed to help you prepare for the NCP-AIO certification exam with detailed explanations to reinforce your learning.

Question 1

An AI research team is experiencing long job queue times on their Kubernetes cluster. What is the most effective strategy to reduce these times without adding more hardware?

A) Increase the priority of all jobs

B) Implement a job preemption policy

C) Use GPU sharing among jobs

D) Reduce the resource requests for each job

Show Answer & Explanation

Correct Answer: B

Explanation: Implementing a job preemption policy allows higher priority jobs to interrupt lower priority ones, reducing queue times for critical tasks. Increasing the priority of all jobs nullifies the effect of prioritization. GPU sharing can lead to contention and isn't directly supported by Kubernetes. Reducing resource requests may help, but can also degrade performance if not done carefully.

Question 2

Which Kubernetes resource would you use to automate the execution of a sequence of AI data processing tasks?

A) DaemonSet

B) Job

C) Pipeline

D) Workflow

Show Answer & Explanation

Correct Answer: D

Explanation: A Workflow, typically implemented with tools like Argo Workflows, automates the execution of a sequence of tasks, ideal for AI data processing pipelines. DaemonSet runs pods on all nodes, Job manages batch tasks, and Pipeline is not a native Kubernetes resource.

Question 3

A user reports that their AI training job on a Kubernetes cluster is running slower than expected. The cluster uses NVIDIA GPUs and the NVIDIA device plugin. What is the first step you should take to diagnose the issue?

A) Check the node's GPU utilization using NVIDIA's DCGM (Data Center GPU Manager).

B) Increase the priority of the user's job in the Kubernetes scheduler.

C) Re-deploy the job with more CPU resources allocated.

D) Restart the Kubernetes cluster to clear any potential resource locks.

Show Answer & Explanation

Correct Answer: A

Explanation: Checking the node's GPU utilization with DCGM is the most appropriate first step to diagnose performance issues, as it provides insights into whether the GPUs are being fully utilized or if there are bottlenecks.

Question 4

When deploying AI workloads on Kubernetes using NVIDIA's GPU Operator, what is the primary benefit of using MIG (Multi-Instance GPU) with A100 GPUs?

A) It allows for the dynamic scaling of GPU resources across different nodes.

B) It enables the sharing of a single GPU across multiple pods, optimizing resource utilization.

C) It provides automatic failover capabilities for GPU workloads.

D) It reduces the overall power consumption of the GPU cluster.

Show Answer & Explanation

Correct Answer: B

Explanation: MIG (Multi-Instance GPU) allows a single A100 GPU to be partitioned into multiple instances, each capable of running separate workloads. This enables sharing of a single GPU across multiple pods, thus optimizing resource utilization. Option A is incorrect as MIG does not facilitate dynamic scaling across nodes. Option C is incorrect because MIG does not provide failover capabilities. Option D is incorrect as MIG is not primarily focused on reducing power consumption.

Question 5

Which method is most effective for automating the deployment of AI workloads in a hybrid cloud environment using NVIDIA technologies?

A) Manually configure each environment to match workload requirements.

B) Use NVIDIA NGC Catalog to deploy pre-configured containers.

C) Implement a CI/CD pipeline that integrates with Kubernetes.

D) Deploy all workloads on-premises to avoid cloud complexities.

Show Answer & Explanation

Correct Answer: C

Explanation: Implementing a CI/CD pipeline that integrates with Kubernetes allows for automated deployment and continuous integration of AI workloads across hybrid cloud environments, ensuring consistent and efficient deployment processes. While NVIDIA NGC Catalog provides pre-configured containers, it does not automate the deployment process. Manual configuration and on-premises deployment do not leverage cloud benefits and automation.

Question 6

While deploying a containerized AI application, you notice that the container fails to start due to insufficient GPU resources. Which Kubernetes feature can help you troubleshoot and resolve this issue?

A) Pod Logs

B) Event Monitoring

C) Resource Quotas

D) Pod Affinity

Show Answer & Explanation

Correct Answer: B

Explanation: Event Monitoring in Kubernetes provides detailed information about events that occur within the cluster, including resource scheduling issues. This can help identify why a pod fails to start due to insufficient GPU resources. Pod Logs provide application-specific logs, Resource Quotas limit resource usage, and Pod Affinity influences pod scheduling based on node labels.

Question 7

Which tool is most suitable for monitoring GPU utilization and performance metrics in a Kubernetes cluster running AI workloads?

A) Prometheus with the NVIDIA DCGM Exporter.

B) Kubernetes Dashboard with built-in metrics.

C) Grafana with default Kubernetes metrics.

D) Standard Linux tools like top and nvidia-smi.

Show Answer & Explanation

Correct Answer: A

Explanation: Prometheus with the NVIDIA DCGM Exporter provides detailed GPU metrics and integrates well with Kubernetes. Option B lacks GPU-specific metrics. Option C requires additional setup for GPU metrics. Option D is not suitable for cluster-wide monitoring.

Question 8

In a Kubernetes cluster managing AI workloads, which strategy is most effective for ensuring that critical AI jobs are not preempted due to resource constraints?

A) Use Kubernetes taints and tolerations to prioritize critical jobs.

B) Assign higher CPU and memory requests for critical jobs.

C) Implement a resource quota system to limit resources for non-critical jobs.

D) Configure a dedicated node pool for critical jobs.

Show Answer & Explanation

Correct Answer: D

Explanation: Configuring a dedicated node pool for critical jobs ensures that these jobs have reserved resources and are not preempted by other workloads. While taints and tolerations (A) can help schedule jobs on specific nodes, they do not guarantee resource availability. Higher requests (B) do not prevent preemption if resources are not available. Resource quotas (C) help manage overall resource usage but do not prioritize specific jobs.

Question 9

When deploying a multi-node Kubernetes cluster for AI workloads on an NVIDIA platform, which method ensures optimal GPU resource allocation?

A) Use Kubernetes default scheduler with no modifications.

B) Implement NVIDIA's GPU Operator to manage GPU resources across nodes.

C) Manually assign GPUs to specific nodes using node affinity.

D) Utilize Kubernetes taints and tolerations to dedicate nodes for GPU workloads.

Show Answer & Explanation

Correct Answer: B

Explanation: The NVIDIA GPU Operator automates the management of GPU resources within Kubernetes, ensuring optimal allocation and utilization across nodes. Using the default scheduler without modifications (A) may not efficiently handle GPU resources. Manually assigning GPUs (C) is not scalable and can lead to inefficient resource use. Taints and tolerations (D) help isolate workloads but do not manage GPU allocation.

Question 10

How can Kubernetes be configured to prioritize AI inference workloads over other types of workloads in a mixed-use cluster?

A) Use pod anti-affinity to separate AI workloads from others.

B) Implement priority classes to assign higher priority to AI inference workloads.

C) Deploy AI workloads on dedicated nodes without any other workloads.

D) Increase the resource requests for AI inference workloads.

Show Answer & Explanation

Correct Answer: B

Explanation: Implementing priority classes allows Kubernetes to prioritize AI inference workloads by assigning them higher priority, ensuring they are scheduled and executed before lower-priority workloads. Pod anti-affinity and dedicated nodes can help, but they do not inherently prioritize workloads. Increasing resource requests does not guarantee scheduling priority.

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About NCP-AIO Certification

The NCP-AIO certification validates your expertise in workload management and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.

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