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NVIDIA AI Operations Sample Questions (Q10-Q15):

NEW QUESTION # 10
Consider a scenario where you're trying to run a Docker container that uses the NVIDIA MPS (Multi-Process Service). However, you keep encountering errors indicating that MPS is not properly initialized within the container. What steps should you take to troubleshoot this issue?

A. Verify that MPS is enabled on the host system before launching the Docker container. This typically involves running 'nvidia-smi -i O -gom 1 ' and 'nvidia-cuda- mps-control -d'.
B. Check if any other processes on the host system are already using the GPU exclusively, preventing MPS from initializing correctly.
C. Ensure that the NVIDIA drivers on the host system are compatible with MPS. MPS requires specific driver versions.
D. Make sure that the Docker container has the necessary permissions to access the NVIDIA devices. This may involve setting the correct user and group IDs.
E. Confirm that the CUDA version within the container is compatible with the NVIDIA drivers on the host and supports MPS.

Answer: A,B,C,D,E

Explanation:
All the options play a crucial role in ensuring that MPS functions correctly within a Docker container. MPS requires specific drivers and enabled on the host. Docker container permission should be set to access the NVIDIA devices. Other processes can hinder initialization, and CUDA version compatibility is essential.

NEW QUESTION # 11
Consider the following data center scenario: You need to deploy a large-scale distributed training job using PyTorch across 16 GPU servers. Each server has 8 NVIDIAA100 GPUs. The training dataset is 1 TB and stored on a network file system (NFS). You observe significant performance bottlenecks during data loading. What are the MOST effective strategies to mitigate this bottleneck? (Select TWO)

A. Implement data parallelism using larger mini-batches.
B. Use a faster network protocol (e.g., NVMe-oF) for accessing the NFS storage.
C. Increase the number of NFS servers and stripe the data across them.
D. Reduce the batch size used for training.
E. Move the entire dataset to local SSDs on each GPU server.

Answer: C,E

Explanation:
The bottleneck is data loading. Increasing the number of NFS servers and striping the data improves the overall read throughput from the network storage. Moving the data to local SSDs eliminates the network bottleneck entirely. Reducing the batch size or using data parallelism only addresses the compute aspect of the training, not the data loading bottleneck. While a faster network protocol helps, moving data local is even more effective. The NFS server configuration is key to improvement.

NEW QUESTION # 12
You have a Slurm cluster configured with multiple partitions, and you want to restrict a specific user group to only submit jobs to a particular partition. How can you achieve this using Slurm's Quality of Service (QOS) and Access Control features?

A. Configure PAM (Pluggable Authentication Modules) to restrict user access based on group membership.
B. Modify the partition configuration to include the user group in the 'AllowGroupS parameter.
C. Create a QOS that allows access only to the desired partition and assign that QOS to the user group using 'sacctmgr' .
D. Edit the user's .bashrc file to include #SBATCH --partition=.
E. Use the 'scontror command to set the default partition for the user group.

Answer: C

Explanation:
Creating a QOS that restricts access to the desired partition and associating that QOS with the user group is the most direct and controlled method. The 'sacctmgr' tool is used to manage QOS and user/group associations.

NEW QUESTION # 13
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. Enable gang scheduling for the job.
B. Set the job's 'restartPolicy' to 'Always'.
C. Use Run.ai's 'suspend' and 'resume' commands manually.
D. Configure a lower priority for the job.
E. Set a higher quota for the team.

Answer: A

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 # 14
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. Utilize Kubernetes Secrets to store the object storage credentials and mount them as files into the container.
B. Use environment variables to pass the object storage credentials to the container.
C. Mount the object storage as a network drive on the host system and then mount this drive into the container.
D. Create a custom Docker image that includes the object storage SDK and hardcodes the credentials.
E. Leverage Identity and Access Management (IAM) roles or Service Accounts associated with the Kubernetes cluster to grant the container access to the object storage.

Answer: A,E

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 # 15
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