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2025 NVIDIA NCP-AIO: NVIDIA AI Operations Useful Braindumps

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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.

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NVIDIA AI Operations Sample Questions (Q18-Q23):

NEW QUESTION # 18
Consider the following Dockerfile snippet for a VMI container deployment:

A. Copy the requirements.txt file and install python packages.
B. Run the command 'python app.py'
C. Copy all files from the current directory to /app
D. Install CUDA toolkit version 11.0
E. Set the working directory to /app

Answer: A,B,C,E

Explanation:
The snippet performs the following actions: Sets the working directory using WORKDIR, Copies files using COPY, and Runs a Python script using CMD. It installs the python requirments using requirements.txt file as well.

NEW QUESTION # 19
A Docker container running a CUDA application terminates unexpectedly with an 'out of memory' error, despite the host machine having sufficient RAM. What are the potential causes and how would you diagnose them?

A. The container's memory limit is too low. Increase the container's memory limit using the -memory' flag with 'docker rum.
B. The GPU is running out of memory. Monitor GPU memory usage using 'nvidia-smr within the container to identify memory leaks or excessive memory allocation.
C. The Docker image is corrupted. Rebuild the Docker image from scratch.
D. The application is experiencing a memory leak within the container. Use memory profiling tools within the container to identify and fix the memory leak.
E. The host machine is experiencing swap thrashing. Monitor swap usage on the host machine and consider increasing RAM or optimizing the application's memory usage.

Answer: A,B,D

Explanation:
The error could stem from a constrained container memory limit, GPU memory exhaustion, or a memory leak within the application itself. Each of these requires different diagnostic approaches. Container memory limits can be checked and adjusted. GPU memory usage can be monitored. Application memory leaks need specific profiling tools inside the container.

NEW QUESTION # 20
You are tasked with deploying a DOCA service on an NVIDIA BlueField DPU in an air-gapped data center environment. The DPU has the required BlueField OS version (3.9.0 or higher) installed, and you have access to the necessary container image from NVIDIA's NGC catalog. However, you need to ensure that the deployment process is successful without an internet connection.
Which of the following steps should you take to deploy the DOCA service on the DPU?

A. Pull the container image from NGC using Docker and modify the YAML file before deployment.
B. Use the host system's Docker engine to pull the container image and deploy it on the DPU via SSH.
C. Install Docker on the DPU, pull the container directly from NGC, and run it using 'docker run' with appropriate environment variables.
D. Manually download the container image and YAML file beforehand, transfer them to the DPU, and deploy using Kubernetes with standalone Kubelet.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In an air-gapped environment where the DPU has no internet connectivity, direct pulling of container images from NVIDIA's NGC catalog is not possible. The recommended approach is tomanually download the required container image and YAML deployment filesfrom a connected system, then transfer these files to the DPU. Deployment is then performed using Kubernetes with a standalone Kubelet on the DPU, which can deploy the preloaded container image offline. This ensures the deployment proceeds successfully without internet access.

NEW QUESTION # 21
You have configured MIG instances on an NVIDIA GPU. After a system reboot, the MIG configuration is lost, and all instances are gone. What is the MOST likely cause of this issue and how can you resolve it?

A. MIG instances are automatically deleted after each reboot for security reasons.
B. The system's power supply is insufficient. Use power supply with more wattage.
C. The MIG configuration was not saved persistently. Use 'nvidia-smi mig -Igip' to save the configuration to the persistence database after creation, then reboot.
D. The NVIDIA driver is outdated. Update the driver to the latest version.
E. The system BIOS does not support MIG. Update the BIOS to the latest version.

Answer: C

Explanation:
MIG configurations are not persistent by default. The 'nvidia-smi mig -Igip' command can be used to load and save instance placement to persistence DB (Igip) and thus the instances are retained across reboots. If the configuration is not saved, it will be lost after a reboot. The other options are less likely causes of this specific issue.

NEW QUESTION # 22
What are the functionalities of 'SlurmDBD'?

A. A command-line interface for submitting jobs to Slurm.
B. A high performance database for storing accounting information.
C. A daemon used to manage Slurm's job queue.
D. A web-based interface for managing Slurm clusters.
E. A tool for monitoring the health of Slurm nodes.

Answer: B

Explanation:
SlurmDBD (Slurm DataBase Daemon) is a high-performance database used to store accounting information, job history, and resource usage data for Slurm clusters. It allows administrators to track and analyze cluster usage patterns and generate reports.

NEW QUESTION # 23
......

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