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NVIDIA - Authoritative Exam NCP-AIO Preparation

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100% Pass Quiz 2025 NCP-AIO: NVIDIA AI Operations Fantastic Exam Preparation

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NVIDIA NCP-AIO Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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.

Topic 3

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 4

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.

NVIDIA AI Operations Sample Questions (Q65-Q70):

NEW QUESTION # 65
When configuring node auto-scaling within BCM for an AI cluster, which of the following metrics would be the most effective indicators for triggering scale-up events, ensuring efficient resource utilization for AI workloads?

A. CPU utilization across the cluster.
B. Memory utilization across the cluster.
C. Disk I/O across the cluster.
D. GPU utilization across the cluster.
E. Network I/O across the cluster.

Answer: D

Explanation:
For AI workloads, GPU utilization is the primary driver. While CPU, memory, network, and disk I/O are relevant, GPU bottleneck has most impact on model training and inference performance. Autoscaling should primarily react to GPU demand to provide optimal performance.

NEW QUESTION # 66
A data scientist submits a Run.ai job requesting 4 GPUs. However, due to resource constraints, only 2 GPUs are immediately available. You want the job to automatically start running as soon as the remaining 2 GPUs become available, without manual intervention. How do you configure Run.ai to achieve this?

A. Use Run.ai's 'suspend' and 'resume' commands manually.
B. Set a higher quota for the team.
C. Configure a lower priority for the job.
D. Set the job's 'restartPolicy' to 'Always'.
E. Enable gang scheduling for the job.

Answer: E

Explanation:
Gang scheduling ensures that all requested resources (in this case, all 4 GPUs) are allocated before the job starts. The job will remain in a pending state until all resources are available, and then it will automatically start. 'restartPolicy only applies if a job fails after it has already started. Lower priority would make it less likely to start. Manually suspending and resuming requires intervention. A quota impacts how much you can submit overall, not the allocation of the complete resources requested by a single job.

NEW QUESTION # 67
Consider the following scenario: You have a DOCA application running on a BlueField-2 DPU that performs deep packet inspection (DPI) using the DOCA DPI service. The application needs to identify specific patterns within the network traffic. Which of the following methods can be used to define the patterns for DPI?

A. Using YAML files to define the patterns and their associated actions.
B. Using predefined signature databases provided by NVIDIA.
C. Using eBPF programs to define custom pattern matching logic.
D. Using custom C code to define matching logic.
E. Using regular expressions (RegEx) to define the patterns.

Answer: A,E

Explanation:
The doca DPI service patterns can be defined through regular expression and YAML files. Predefined signature database may exist , but that is not the primary method of definition, eBPF and Custom C are not the mechanism supported directly via DPI service.

NEW QUESTION # 68
You need to deploy a containerized AI application from NGC using a CI/CD pipeline. The pipeline should automatically build, test, and deploy the container image to a Kubernetes cluster whenever changes are pushed to the code repository. Which of the following CI/CD tools and practices are most suitable for this scenario?

A. Use Jenkins with the Docker plugin to build the image and 'kubectl apply' to deploy it.
B. Employ CircleCl with a custom script to build the image and directly push it to the container registry.
C. Integrate NVIDIA GPU Cloud (NGC) CLI into the CIICD pipeline to fetch and deploy pre-built containers.
D. Utilize GitLab CI/CD with kaniko to build the image and Helm to manage deployments.
E. Use AWS CodePipeline with AWS CodeBuild to build the image and AWS Elastic Kubernetes Service (EKS) to deploy the application.

Answer: C,D,E

Explanation:
B, D and E are the correct. GitLab CI/CD with kaniko and Helm provides a robust and scalable solution for building and deploying container images. NGC CLI allows fetching and deploying pre-built containers, simplifying the process. AWS CodePipeline, CodeBuild, and EKS offer a complete CI/CD solution within the AWS ecosystem. Option A is a valid but less modern approach. Option C lacks a structured deployment process.

NEW QUESTION # 69
Which command line utility can be used to verify the proper functioning of GPUDirect RDMA between two GPUs on different nodes?

A. Ispci'
B. nvidia-smi'
C. rocminfo'
D. 'ibv devinfo'
E. 'cuda-memcheck'

Answer: D

Explanation:
'ibv_devinfo' is a command-line utility (part of the InfiniBand Verbs library) that provides information about RDMA devices and their capabilities. This includes verifying that RDMA is enabled and configured correctly, which is essential for GPUDirect RDMA. 'nvidia-smi' monitors GPU status. 'rocminfo' is for AMD GPUs. 'cuda-memcheck' is for CUDA memory errors. 'Ispci' lists PCI devices, but it doesn't specifically verify RDMA functionality.

NEW QUESTION # 70
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Matthew Robinson Matthew Robinson

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