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| Section | Weight | Objectives |
|---|---|---|
| Management | 28% | - Resource allocation and scheduling - Access control and security policies - GPU cluster and compute node management - Software and container lifecycle management |
| Workload Management | 20% | - AI workload deployment and scaling - Framework and runtime configuration - Data pipeline and storage integration - Job scheduling and queue management |
| Troubleshooting and Optimization | 20% | - Throughput and latency optimization - Fault diagnosis and resolution - System health and reliability maintenance - Performance monitoring and analysis |
| Installation and Deployment | 32% | - Driver and firmware installation - Container orchestration and resource management - AI cluster setup and configuration - NVIDIA software stack deployment |
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NEW QUESTION # 43
You are managing a high availability (HA) cluster that hosts mission-critical applications. One of the nodes in the cluster has failed, but the application remains available to users.
What mechanism is responsible for ensuring that the workload continues to run without interruption?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In an HA cluster, thefailover mechanismis responsible for detecting node failures and automatically transferring workloads to a standby or redundant node to maintain service availability. This process ensures mission-critical applications continue running without interruption. Load balancing helps distribute traffic but does not handle node failures. Manual intervention is not ideal for HA, and data replication ensures data integrity but does not itself manage workload continuity.
NEW QUESTION # 44
A system administrator needs to collect the information below:
GPU behavior monitoring
GPU configuration management
GPU policy oversight
GPU health and diagnostics
GPU accounting and process statistics
NVSwitch configuration and monitoring
What single tool should be used?
Answer: C
Explanation:
The NVIDIA Data Center GPU Manager (DCGM) is the comprehensive management tool that provides all the requested functionalities: monitoring GPU behavior, managing configurations, enforcing policies, health diagnostics, process accounting, and NVSwitch monitoring. DCGM is designed for large-scale GPU management in data centers and AI clusters, providing detailed telemetry and control over NVIDIA GPUs and NVSwitches.
NEW QUESTION # 45
You have noticed that users can access all GPUs on a node even when they request only one GPU in their job script using --gres=gpu:1. This is causing resource contention and inefficient GPU usage.
What configuration change would you make to restrict users' access to only their allocated GPUs?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To restrict users' access strictly to the GPUs allocated to their jobs, Slurm usescgroups (control groups)for resource isolation. Enabling device cgroup enforcement by settingConstrainDevices=yesincgroup.
confenforces device access restrictions, ensuring jobs cannot access GPUs beyond those assigned.
* Increasing memory allocation or setting job priorities does not restrict device access.
* Modifying job scripts to request additional CPU cores does not limit GPU access.
Hence, enablingcgroup enforcement with ConstrainDevices=yesis the correct method to prevent users from accessing unallocated GPUs.
NEW QUESTION # 46
A DGX H100 system in a cluster is showing performance issues when running jobs.
Which command should be run to generate system logs related to the health report?
Answer: A
Explanation:
For troubleshooting and performance optimization on NVIDIA DGX systems such as DGX H100, the NVIDIA System Management (nvsm) tool is used to gather system health and diagnostic data. The command nvsm dump health is the correct command to generate and export detailed system logs related to the health report of the DGX system.
NEW QUESTION # 47
You have a Run.ai cluster integrated with NVIDIA's Cluster Manager (ACM). A data scientist reports that their job is being preempted frequently, even though they have a high-priority quot a. What are the MOST likely reasons for this preemption, assuming ACM is configured correctly?
Answer: C,D,E
Explanation:
Preemption in Run.ai with ACM is typically triggered by: Another job with a higher guaranteed quota and higher priority needing the resources (ACM prioritizes based on quota and priority). The node being drained (Kubernetes initiates preemption to safely evacuate pods before maintenance). A higher priority job needing resources and preemption is enabled. Exceeding memory limits usually results in an 00M error, not preemption. An outdated CUDA driver could cause errors, but not typically preemption. Note that OOM can occur on containers when the available memory is exhausted, which can cause them to be killed
NEW QUESTION # 48
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