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NVIDIA AI Operations Sample Questions (Q60-Q65):

NEW QUESTION # 60
A Slurm user needs to display real-time information about the running processes and resource usage of a Slurm job.
Which command should be used?

A. scontrol show job <jobid>
B. sinfo -j <jobid>
C. sstat -j <job(.step)>
D. smap -j <jobid>

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The Slurm commandsstatis designed to provide real-time statistics about running jobs, including process-level details and resource usage such as CPU, memory, and GPU utilization. Usingsstat -j <jobid>orsstat -j <jobid.
step>allows monitoring of active job resource consumption.
* smapis not a standard Slurm command.
* scontrol show jobgives job configuration and status but not real-time resource usage.
* sinfodisplays node and partition information, not job-specific resource stats.
Therefore,sstatis the correct command for real-time job process and resource monitoring.

NEW QUESTION # 61
You are deploying a cloud VMI container with Kubernetes. Your application requires a specific NVIDIA driver version. How do you ensure the correct driver version is used within the container, especially when the host node might have a different driver version?

A. The NVIDIA driver version used on the host node is automatically inherited by the container; no specific configuration is needed.
B. Utilize the NVIDIA Device Plugin for Kubernetes and configure it to inject the correct driver libraries into the container during runtime.
C. Configure the Kubernetes node's operating system to always use the desired NVIDIA driver version globally.
D. Specify the desired driver version in the Kubernetes Deployment manifest using the 'nvidia.com/gpu.driver.version' resource limit.
E. Bake the required NVIDIA driver version directly into the container image during the Docker build process.

Answer: B,E

Explanation:
Using the NVIDIA Device Plugin allows for dynamic injection of driver libraries. Baking the driver into the container also works, but it results in a larger image and less flexibility. Option A is not a valid resource limit. Overriding the host OS driver is not practical for multi-tenant environments. Relying on host inheritance is risky as driver versions can vary.

NEW QUESTION # 62
You are tasked with deploying a TensorFlow container from NGC on a Kubernetes cluster. The container requires specific NVIDIA drivers and libraries. Which of the following steps are essential to ensure successful deployment and GPU utilization?

A. Create a Kubernetes DaemonSet to automatically deploy and manage the NVIDIA device plugin on all nodes.
B. Deploy the container without specifying any resource limits or requests to allow it to utilize all available GPUs.
C. Bypass the NVIDIA Container Toolkit and directly use Docker to deploy the container.
D. Ensure the NVIDIA Container Toolkit is installed and configured on all worker nodes.
E. Verify that the NVIDIA drivers on the host machines match the versions required by the container.

Answer: A,D,E

Explanation:
A, C, and D are correct. The NVIDIA Container Toolkit enables GPU access within containers. Matching driver versions are crucial for compatibility. The device plugin exposes GPU resources to Kubernetes. B is incorrect because resource limits are important for scheduling and stability. E is incorrect; the NVIDIA Container Toolkit is the recommended method for GPU access within containers.

NEW QUESTION # 63
You are an administrator managing a large-scale Kubernetes-based GPU cluster using Run:AI.
To automate repetitive administrative tasks and efficiently manage resources across multiple nodes, which of the following is essential when using the Run:AI Administrator CLI for environments where automation or scripting is required?

A. Use the CLI to manually allocate specific GPUs to individual jobs for better resource management.
B. Install the CLI on Windows machines to take advantage of its scripting capabilities.
C. Use the runai-adm command to directly update Kubernetes nodes without requiring kubectl.
D. Ensure that the Kubernetes configuration file is set up with cluster administrative rights before using the CLI.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When automating tasks with the Run:AI Administrator CLI, it is essential to ensure that theKubernetes configuration file (kubeconfig)is correctly set up with cluster administrative rights. This enables the CLI to interact programmatically with the Kubernetes API for managing nodes, resources, and workloads efficiently.
Without proper administrative permissions in the kubeconfig, automated operations will fail due to insufficient rights.
Manual GPU allocation is typically handled by scheduling policies rather than CLI manual assignments. The CLI does not replacekubectlcommands entirely, and installation on Windows is not a critical requirement.
The Run:AI Administrator CLI requires a Kubernetes configuration file with cluster-administrative rights in order to perform automation or scripting tasks across the cluster. Without those rights, the CLI cannot manage nodes or resources programmatically.

NEW QUESTION # 64
You have deployed a VMI container with Triton Inference Server on a cloud provider that supports MIG (Multi-lnstance GPU). You have a single A100 GPU and you want to partition it into two MIG instances to serve two different models concurrently, each requiring half of the GPU's resources. What steps are necessary to achieve this?

A. No special configuration is needed; Triton automatically detects and utilizes MIG instances.
B. Partition the AIOO GPU into two MIG instances using the 'nvidia-smi' command-line tool, then configure Triton to use each MIG instance separately by specifying the corresponding UUIDs in the model configuration files.
C. MIG is not a supported feature in Triton
D. Configure the cloud provider's instance settings to automatically partition the GPU into MIG instances.
E. Bake different drivers in Triton Container to target different MIG instances

Answer: B

Explanation:
To utilize MIG with Triton, you need to first partition the GPU into MIG instances using 'nvidia-smi' , and then configure Triton to use each MIG instance separately. This involves specifying the correct UUIDs for each MIG instance in the model configuration files, allowing Triton to isolate and utilize each partition effectively.

NEW QUESTION # 65
......

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