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[Hardware] Actual NCP-AIO Test Pdf | NCP-AIO New Dumps Sheet

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【Hardware】 Actual NCP-AIO Test Pdf | NCP-AIO New Dumps Sheet

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NVIDIA AI Operations Sample Questions (Q16-Q21):NEW QUESTION # 16
You are deploying a DOCA application that needs to interact with the host operating system for certain tasks. What are the potential challenges and solutions for achieving this interaction securely and efficiently?
  • A. Challenges: Difficulty in debugging and troubleshooting issues across the host-DPU boundary. Solutions: Using comprehensive logging and tracing mechanisms, implementing remote debugging tools, and establishing clear communication channels between the host and DPU components.
  • B. Challenges: Kernel module compatibility issues and potential conflicts with host drivers. Solutions: Using standard Linux APIs whenever possible, avoiding direct kernel module modifications, and testing thoroughly for compatibility.
  • C. Solutions: Direct Memory Access on non secured memory for performance
  • D. Challenges: Resource contention between the host and DPU applications. Solutions: Using proper resource allocation and prioritization mechanisms, such as cgroups and QOS policies, to prevent resource starvation.
  • E. Challenges: Limited direct access to host resources, security concerns, and potential performance overhead. Solutions: Using DOCA Comm Channel for control message exchange, utilizing shared memory for data transfer, and employing secure APIs for host interaction.
Answer: A,B,D,E
Explanation:
Interacting with the host OS poses several challenges, including limited access, security concerns, and potential conflicts. The solutions involve using secure communication channels, standard APIs, comprehensive debugging mechanisms, and resource allocation policies. Direct Memory access on non-secured memory is not a solution for secure and efficient communication.

NEW QUESTION # 17
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 # 18
A user reports that they are unable to submit jobs to a specific partition. You've verified that the partition exists and is enabled. What are the possible reasons for this?
  • A. The partition's state is set to INACTIVE.
  • B. All of the above
  • C. The 'MaxNodes' parameter for the partition is set to 0.
  • D. The user has exceeded their QOS limit.
  • E. The user's account is not associated with the partition.
Answer: B
Explanation:
All the options are reasons for the user to be unable to submit jobs to a specific partition. All must be checked to solve the root problem.

NEW QUESTION # 19
Your Kubernetes cluster hosts several AI workloads with varying GPU requirements. Some workloads require high compute performance, while others are memory-bound. You want to optimize GPU resource allocation by bin-packing workloads with complementary resource profiles onto the same nodes. How would you approach this?
  • A. Configure resource quotas to limit the overall GPU usage in the cluster.
  • B. Use node affinity rules to schedule all workloads onto the same set of nodes, regardless of their resource profiles.
  • C. Employ a custom Kubernetes scheduler that considers GPU compute and memory usage when placing pods.
  • D. Implement a vertical pod autoscaler (VPA) to dynamically adjust the GPU resource requests of pods based on their actual usage.
  • E. Manually assign workloads to specific nodes based on their resource requirements.
Answer: C
Explanation:
The correct answer is B. A custom Kubernetes scheduler allows you to implement sophisticated bin-packing algorithms that consider both GPU compute and memory usage to optimize resource allocation. This enables you to pack workloads with complementary resource profiles onto the same nodes, maximizing GPU utilization. Option A ignores resource profiles. Option C is not scalable. Option D limits overall usage but doesn't optimize placement. Option E focuses on individual pod resource adjustment, not bin-packing.

NEW QUESTION # 20
Which of the following Slurm configuration options are typically modified within the "slurm.conf' file? (Select TWO)
  • A. MailType
  • B. SchedulerParameters
  • C. JobSubmitPlugins
  • D. sacctmgr
  • E. squeue
Answer: B,C
Explanation:
'JobSubmitPluginS configures plugins for job submission, and 'SchedulerParameterS configures the behavior of the scheduler. These are typically defined cluster-wide in 'slurm.conf. 'squeue' and 'sacctmgr' are commands, not configuration options. 'MailType' can be configured but depends on the email plugin being setup.

NEW QUESTION # 21
......
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