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NVIDIA AI Operations Sample Questions (Q34-Q39):

NEW QUESTION # 34
You have deployed the NVIDIA Device Plugin for Kubernetes on your BCM-managed cluster. After a kernel update on one of the worker nodes, the device plugin fails to discover the GPUs. The error messages indicate a mismatch between the driver version expected by the device plugin and the actual driver version installed on the node. What is the MOST reliable way to resolve this issue without disrupting other workloads?

A. Remove the NVIDIA Device Plugin and replace it with the 'nvidia-driver-installer' helm chart
B. Uninstall and reinstall the NVIDIA Container Toolkit on the affected worker node to automatically update the driver version.
C. Use a DaemonSet to manage the NVIDIA driver installation on all worker nodes, ensuring a consistent driver version across the cluster and compatibility with the device plugin.
D. Manually downgrade the NVIDIA driver on the affected worker node to match the version expected by the device plugin.
E. Update the NVIDIA Device Plugin deployment manifest to specify the driver version installed on the node.

Answer: C

Explanation:
Using a DaemonSet to manage the NVIDIA driver installation is the MOST reliable and scalable solution. It ensures that all worker nodes have the correct driver version and simplifies driver updates. Manually downgrading or updating individual nodes (A, B) is not sustainable. Reinstalling the toolkit (D) might not update the driver. Simply removing and replacing the plugin (E) doesn't address driver mismatch and would likely use a similar deployment method that would lead to the same error.

NEW QUESTION # 35
A new researcher needs access to GPU resources but should not have permission to modify cluster settings or manage other users.
What role should you assign them in Run:ai?

A. Research Manager
B. Department Administrator
C. Application Administrator
D. L1 Researcher

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Run:ai, roles are assigned based on levels of permissions. TheL1 Researcherrole is designed for users who need access to GPU resources for running jobs and experiments but should not have administrative rights over cluster settings or other users. This role ensures researchers can use resources without affecting cluster configurations or user management. Other roles like Department Administrator, Application Administrator, or Research Manager have broader privileges, including managing users and settings, which are not appropriate for the new researcher's requirements.

NEW QUESTION # 36
A data scientist has provided you with a Jupyter Notebook running inside an NGC container. This notebook relies on a large dataset stored in an object storage service (e.g., AWS S3, Google Cloud Storage). What's the most efficient and secure way to provide the notebook access to this data without embedding credentials directly into the notebook or container image?

A. Use environment variables to pass the object storage credentials to the container.
B. Leverage Identity and Access Management (IAM) roles or Service Accounts associated with the Kubernetes cluster to grant the container access to the object storage.
C. Create a custom Docker image that includes the object storage SDK and hardcodes the credentials.
D. Utilize Kubernetes Secrets to store the object storage credentials and mount them as files into the container.
E. Mount the object storage as a network drive on the host system and then mount this drive into the container.

Answer: B,D

Explanation:
C and E are the most secure and efficient. Kubernetes Secrets allow for secure storage and management of sensitive data, which can be mounted into the container as files. Leveraging IAM roles or Service Accounts allows the container to inherit permissions from the Kubernetes cluster, eliminating the need for explicit credentials. Option B is less secure as environment variables can be easily exposed. Option A can introduce performance bottlenecks. Option D is highly discouraged due to security risks and lack of flexibility.

NEW QUESTION # 37
When deploying a VMI container that utilizes CUDA, what is the primary purpose of the NVIDIA Container Toolkit?

A. To automatically install the correct NVIDIA drivers on the host system.
B. To automatically scale the number of VMI containers based on workload.
C. To provide CUDA libraries and drivers inside the container, enabling GPU acceleration.
D. To manage and orchestrate Docker containers across multiple hosts.
E. To monitor the GPU utilization of the container in real-time.

Answer: C

Explanation:
The NVIDIA Container Toolkit allows you to build and run GPU-accelerated containers by providing the necessary CUDA libraries and drivers inside the container, ensuring that the application can leverage the GPU.

NEW QUESTION # 38
You have a Kubernetes cluster running on BCM and are using the NVIDIA device plugin. Some pods require a specific CUDA version that is different from the default CUDA version installed on the nodes. Which of the following is the MOST appropriate strategy to handle this requirement?

A. Create dedicated nodes for each CUDA Version, and define nodeSelectors
B. Use NVIDIA's container image that includes the desired CUDA version, ensuring that the container has the correct CUDA libraries.
C. Mount the CUDA toolkit from the host into the container using a hostPath volume.
D. Use init containers to install the required CUDA version on the nodes before the main container starts.
E. Install multiple CUDA versions directly on each node and configure each pod to use the desired version via environment variables.

Answer: B

Explanation:
Using NVIDIA's container images that include the desired CUDA version is the recommended and most reliable approach. It avoids polluting the host system with multiple CUDA installations and ensures consistency. Installing multiple CUDA versions directly on nodes (A) can lead to conflicts. Mount CUDA from host via volumes bypasses the entire idea of running BCM. Create dedicated nodes is only viable for limited use case. Init containers may have issues cleaning up. The images provide the dependencies required for GPU workloads.

NEW QUESTION # 39
......

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NCP-AIO Testdump | NCP-AIO Customizable Exam Mode

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NVIDIA AI Operations Sample Questions (Q34-Q39):

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