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NVIDIA NCP-AIO Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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.

NVIDIA AI Operations Sample Questions (Q15-Q20):

NEW QUESTION # 15
You are deploying a cloud VMI container on AWS using the NVIDIA GPU Cloud (NGC) AMI. You need to ensure that the container has access to a specific S3 bucket containing the training dat a. Which of the following is the MOST secure and recommended method to grant this access?

A. Storing AWS credentials in environment variables within the Dockerfile used to build the container image.
B. Using AWS Secrets Manager to store the credentials and retrieving them via the AWS CLI within the container.
C. Hardcoding AWS credentials directly within the container's application code.
D. Creating a dedicated IAM user and distributing its credentials via AWS Systems Manager Parameter Store. Retrieve credentials during container startup.
E. Using an IAM role assigned to the EC2 instance running the VMI container.

Answer: E

Explanation:
Using an IAM role assigned to the EC2 instance is the most secure method. It avoids storing credentials within the container itself, relying instead on the AWS infrastructure's built-in security mechanisms. Option D is viable but more complex than simply using an IAM Role.

NEW QUESTION # 16
You need to configure network settings for your Fleet Command deployment. You want to ensure that edge devices can only communicate with the Fleet Command server over a specific port and protocol for security reasons. Which of the following configurations is the MOST appropriate?

A. Rely on the default network settings provided by the operating system.
B. Configure a VPN for all communication, even local communication.
C. Open all ports on the edge devices and the Fleet Command server to allow unrestricted communication.
D. Configure a firewall on the edge devices and the Fleet Command server to allow communication only on the designated port and protocol (e.g., HTTPS on port 443),
E. Disable all network access on the edge devices except for SSH.

Answer: D

Explanation:
A firewall provides the necessary security by restricting communication to only the required port and protocol. Opening all ports (A) is insecure. Disabling network access (C) prevents functionality. Relying on defaults (D) is insufficient. VPN is not needed for local communication and overcomplicated. (E)

NEW QUESTION # 17
You are using BCM to manage a Kubernetes cluster with multiple GPU nodes. You need to enable GPU monitoring using Prometheus and the NVIDIA DCGM exporter. Outline the steps required to accomplish this. Choose the correct sequence:

A. 0 1. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 2. Install Prometheus in your Kubernetes cluster. 3. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.
B. 0 1. Install Prometheus in your Kubernetes cluster. 2. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 3. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 4. Verify GPU metrics are available in Prometheus.
C. 0 1. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 2. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 3. Install Prometheus in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.
D. 0 1. Deploy the NVIDIA DCGM exporter as a Deployment in your Kubernetes cluster. 2. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 3. Install Prometheus in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.
E. 0 1. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 2. Configure the NVIDIA DCGM exporter endpoints. 3. Install Prometheus in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.

Answer: B

Explanation:
Prometheus must be installed first to enable metric collection. The DCGM exporter is then deployed as a DaemonSet (to ensure it runs on every node) and configured, enabling Prometheus to scrape the GPU metrics. Finally, the metrics availability is verified.

NEW QUESTION # 18
When deploying a DOCA application using the command, which of the following parameters are mandatory for specifying the DOCA core application's entry point?

A. '-class The DOCAApplication class in the DOCA executable
B. -num-cores Specifies the number of CPU cores to be allocated to the DOCA application.
C. '-log-level ' : Sets the verbosity level for logging messages from the DOCA application.
D. '-pci-address Defines the PCl address of the BlueField DPLJ where the application will run.
E. -entry-point Specifies the name of the function within the DOCA application that should be executed as the entry point.

Answer: A,E

Explanation:
The zentry-point' parameter is required to specify the function that will be the entry point of the DOCA core application. '--class' parameter is mandatory.

NEW QUESTION # 19
Consider a scenario where you have the following deployment manifest for Fleet Command. What is the primary function of 'resources' section within the manifest?

A. Defines the computational resources (e.g., CPU, GPU, memory) required by the deployment.
B. Specifies dependencies on external libraries or frameworks.
C. Determines which devices receive the deployment based on label match
D. Specifies the location of the AI model to be deployed.
E. Configures networking settings for the deployment.

Answer: A

Explanation:
The 'resources section in a deployment manifest is primarily used to define the computational resource requirements of the deployment, ensuring that the application has access to the necessary CPU, GPU, and memory to function correctly.

NEW QUESTION # 20
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