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100% Pass 2025 NVIDIA The Best NCP-AIO: Latest NVIDIA AI Operations Test Notes

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NVIDIA NCP-AIO Exam Syllabus Topics:

Topic
Details

Topic 1

Troubleshooting and Optimization: NVIThis section of the exam measures the skills of AI infrastructure engineers and focuses on diagnosing and resolving technical issues that arise in advanced AI systems. Topics include troubleshooting Docker, the Fabric Manager service for NVIDIA NVlink and NVSwitch systems, Base Command Manager, and Magnum IO components. Candidates must also demonstrate the ability to identify and solve storage performance issues, ensuring optimized performance across AI workloads.

Topic 2

Workload Management: This section of the exam measures the skills of AI infrastructure engineers and focuses on managing workloads effectively in AI environments. It evaluates the ability to administer Kubernetes clusters, maintain workload efficiency, and apply system management tools to troubleshoot operational issues. Emphasis is placed on ensuring that workloads run smoothly across different environments in alignment with NVIDIA technologies.

Topic 3

Administration: This section of the exam measures the skills of system administrators and covers essential tasks in managing AI workloads within data centers. Candidates are expected to understand fleet command, Slurm cluster management, and overall data center architecture specific to AI environments. It also includes knowledge of Base Command Manager (BCM), cluster provisioning, Run.ai administration, and configuration of Multi-Instance GPU (MIG) for both AI and high-performance computing applications.

Topic 4

Installation and Deployment: This section of the exam measures the skills of system administrators and addresses core practices for installing and deploying infrastructure. Candidates are tested on installing and configuring Base Command Manager, initializing Kubernetes on NVIDIA hosts, and deploying containers from NVIDIA NGC as well as cloud VMI containers. The section also covers understanding storage requirements in AI data centers and deploying DOCA services on DPU Arm processors, ensuring robust setup of AI-driven environments.

>> Latest NCP-AIO Test Notes <<

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NVIDIA AI Operations Sample Questions (Q30-Q35):

NEW QUESTION # 30
Which command-line tool is primarily used for creating and managing MIG instances on NVIDIA GPUs?

A. nvcc
B. nvidia-smi
C. nvidia-container-cli
D. nvprof
E. nvidia-cuda-smi

Answer: B

Explanation:
'nvidia-smi' is the primary command-line tool for managing and monitoring NVIDIA GPUs, including creating and managing MIG instances. The other options have different purposes related to CUDA development and containerization.

NEW QUESTION # 31
Which of the following correctly identifies the key components of a Kubernetes cluster and their roles?

A. The control plane consists of the kube-apiserver, etcd, kube-scheduler, and kube-controller-manager, while worker nodes run kubelet and kube-proxy.
B. The control plane is responsible for running all application containers, while worker nodes manage network traffic through etcd.
C. Worker nodes manage the kube-apiserver and etcd, while the control plane handles all container runtimes.
D. The control plane includes the kubelet and kube-proxy, and worker nodes are responsible for running etcd and the scheduler.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Kubernetes architecture, thecontrol planeis composed of several core components including thekube- apiserver,etcd(the cluster's key-value store),kube-scheduler, andkube-controller-manager. These manage the overall cluster state, scheduling, and orchestration of workloads. Theworker nodesare responsible for running the actual containers and include thekubelet(agent that communicates with the control plane) and kube-proxy(handles network routing for services). Other options incorrectly assign these components or roles.

NEW QUESTION # 32
A system administrator is troubleshooting a Docker container that crashes unexpectedly due to a segmentation fault. They want to generate and analyze core dumps to identify the root cause of the crash.
Why would generating core dumps be a critical step in troubleshooting this issue?

A. Core dumps capture the memory state of the process at the time of the crash.
B. Core dumps provide real-time logs that can be used to monitor ongoing application performance.
C. Core dumps prevent future crashes by stopping any further execution of the faulty process.
D. Core dumps restore the process to its previous state, often fixing the error-causing crash.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Core dumps capture thememory state of a process at the time of its crash, providing a snapshot useful for post- mortem debugging. Analyzing core dumps helps identify the cause of segmentation faults or other critical errors by revealing what the process was doing at failure, including stack traces, variable states, and memory content.

NEW QUESTION # 33
You are setting up a Kubernetes cluster on NVIDIA DGX systems using BCM, and you need to initialize the control-plane nodes.
What is the most important step to take before initializing these nodes?

A. Disable swap on all control-plane nodes before initializing them.
B. Set up a load balancer before initializing any control-plane node.
C. Configure each control-plane node with its own external IP address.
D. Ensure that Docker is installed and running on all control-plane nodes.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Disablingswapon all control-plane nodes is a critical prerequisite before initializing Kubernetes control-plane nodes. Kubernetes requires swap to be disabled to maintain performance and stability. Failure to disable swap can cause kubeadm initialization to fail or lead to unpredictable cluster behavior.

NEW QUESTION # 34
You are deploying AI applications at the edge and want to ensure they continue running even if one of the servers at an edge location fails.
How can you configure NVIDIA Fleet Command to achieve this?

A. Use Secure NFS support for data redundancy.
B. Configure Fleet Command's multi-instance GPU (MIG) to handle failover.
C. Enable high availability for edge clusters.
D. Set up over-the-air updates to automatically restart failed applications.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To ensure continued operation of AI applications at the edge despite server failures, NVIDIA Fleet Command allows administrators toenable high availability (HA) for edge clusters. This HA configuration ensures redundancy and failover capabilities, so applications remain operational when an edge server goes down.
Over-the-air updates handle software patching but do not inherently provide failover. MIG manages GPU resource partitioning, not failover. Secure NFS supports storage redundancy but is not the primary solution for application failover.

NEW QUESTION # 35
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