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NVIDIA AI Operations Sample Questions (Q50-Q55):

NEW QUESTION # 50
After successfully creating MIG instances on your NVIDIA A100 GPU, you observe that applications assigned to these instances are not fully utilizing the allocated resources. You suspect that CPU affinity is not properly configured. What steps should you take to ensure optimal CPU affinity for these MIG instances?

A. Manually assign CPU cores to each MIG instance using the 'taskset' command or similar tools, ensuring that each instance has exclusive access to its assigned cores. Also use numactl.
B. Set CPU affinity to the same core for all MIG instances.
C. Rely solely on the operating system's default scheduler to handle CPU affinity.
D. Disable CPU affinity altogether to allow processes to migrate freely across all cores.
E. Increase the priority of all processes running within the MIG instances using the snice' command.

Answer: A

Explanation:
CPU affinity binds processes to specific CPU cores, reducing context switching and improving performance. Manually assigning CPU cores to each MIG instance, ensuring exclusivity, is crucial for optimal resource utilization. 'tasksets and 'numactl' are commonly used tools for this purpose. Options A, C, D, and E would likely lead to performance degradation or resource contention.

NEW QUESTION # 51
You are managing a fleet of edge devices using Fleet Command and need to implement a mechanism for A/B testing of different AI models. What is the BEST approach to achieve this using Fleet Command?

A. Use Fleet Command's device targeting feature with labels to deploy different model versions to subsets of devices and configure traffic routing accordingly.
B. Create separate Fleet Command organizations for each model version and manually direct traffic to each organization.
C. Manually deploy each model version to all devices and rely on the application to randomly select which model to use.
D. Schedule different model deployments at different times to perform time-based A/B testing.
E. Develop a custom script to dynamically switch between model versions on each device.

Answer: A

Explanation:
Device targeting with labels allows for controlled deployment of different model versions to specific device subsets, enabling effective AIB testing. Separate organizations (A) are overly complex. Manual deployment (C) lacks control. Custom scripts (D) add complexity. Time-based testing (E) is not true AIB testing.

NEW QUESTION # 52
You are managing a large Slurm cluster used for AI research. You notice that some users are submitting jobs that request excessive amounts of memory, even though their applications don't actually need it, leading to inefficient resource utilization. What steps can you take to address this issue and encourage users to request more appropriate memory resources?

A. Educate users on how to accurately estimate their memory requirements and provide tools for monitoring memory usage.
B. Configure Slurm's accounting system to track memory usage and provide reports to users, highlighting inefficient memory requests.
C. Adjust the scheduler's priority algorithm to penalize jobs that request excessive memory.
D. All of the above
E. Implement strict memory limits per job and automatically kill jobs that exceed those limits.

Answer: D

Explanation:
A multi-faceted approach is needed. Enforcing limits (A) prevents waste, education (B) helps users improve requests, accounting (C) provides feedback, and adjusting priority (D) discourages wasteful requests. A, B, C, D combined provide the best approach.

NEW QUESTION # 53
You're trying to build a Docker image for a PyTorch application that requires CUDA 11.6. Your Dockerfile includes the following line: 'FROM nvidia/cuda:ll .6-base-ubuntu20.04'. However, during the build process, you encounter errors related to missing CUDA libraries.
What could be the problem and what are possible solutions?

A. You are using the wrong base image. Use 'nvidia/cuda:ll .6-devel-ubuntu20.04' instead, as it includes development libraries.
B. The 'nvidia/cuda:ll .6-base-ubuntu20.04' image only provides the base CUDA environment. You need to install additional CUDA libraries like 'libcudnn8' explicitly using 'apt-get' in your Dockerfile.
C. The host machine's NVIDIA driver version is incompatible with CUDA 11.6. Update the host driver to a compatible version.
D. The Docker daemon does not have enough resources to build the image. Increase the Docker daemon's memory and CPU limits.
E. You are not setting the CUDA HOME environment variable inside the Dockerfile. Set 'ENV CUDA HOME=/usr/local/cuda'

Answer: A,B

Explanation:
The '-base image provides a minimal CUDA environment, often lacking necessary libraries. The '-dever image includes development libraries, and explicit installation of specific libraries (like cuDNN) might be required. CUDA_HOME might also be necessary to explicitly set, but the more likely issues are the missing libraries and the image selection.

NEW QUESTION # 54
You're implementing a preemption policy in your Slurm cluster to allow higher-priority jobs to interrupt lower-priority jobs. Which Slurm configuration parameters are MOST relevant to configure preemption? (Select TWO)

A. PreemptMode
B. SchedulerRootFilter
C. PreemptType
D. AccountingStorageType
E. FastSchedule

Answer: A,C

Explanation:
'PreemptMode' defines when preemption is triggered (e.g., 'OFF', 'CANCEL', 'REQUEUE'). 'preemptType' determines which jobs are eligible for preemption (e.g., 'priority', 'qos').

NEW QUESTION # 55
......

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Chris Ward Chris Ward

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