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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Optimization | 20% | - System health and reliability maintenance - Performance monitoring and analysis - Fault diagnosis and resolution - Throughput and latency optimization |
| Topic 2: Management | 28% | - GPU cluster and compute node management - Software and container lifecycle management - Resource allocation and scheduling - Access control and security policies |
| Topic 3: Installation and Deployment | 32% | - AI cluster setup and configuration - NVIDIA software stack deployment - Driver and firmware installation - Container orchestration and resource management |
| Topic 4: Workload Management | 20% | - AI workload deployment and scaling - Data pipeline and storage integration - Job scheduling and queue management - Framework and runtime configuration |
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NEW QUESTION # 62
What must be done before installing new versions of DOCA drivers on a BlueField DPU?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Before installing new versions of DOCA drivers on NVIDIA BlueField DPUs, it is required touninstall any previous versionsof DOCA drivers to prevent conflicts and ensure a clean upgrade. This ensures that the new installation is not affected by leftover files or configurations from earlier versions. Re-flashing firmware or disabling network interfaces is not always required before every driver installation. Rebooting the host system might be recommended after installation but is not a prerequisite before installing drivers.
NEW QUESTION # 63
You are using NVIDIA MPS (Multi-Process Service) to allow multiple CUDA applications to share a single GPU. One of the applications is consistently crashing. How can you isolate the faulty application using MPS?
Answer: A,B
Explanation:
The most direct approach is to disable MPS and run each application independently to pinpoint the source of the crashes. Examining the system logs for error messages linked to specific PIDs helps identify the failing process. Monitoring GPU utilization (B) might provide hints, but it doesn't directly isolate the faulty application. Reducing threads (D) might mask the issue, but it doesn't solve it. Restarting the server (E) is a temporary solution and doesn't address the root cause.
NEW QUESTION # 64
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: B,C,D
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 # 65
An administrator is troubleshooting a bottleneck in a deep learning run time and needs consistent data feed rates to GPUs.
Which storage metric should be used?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When troubleshooting performance bottlenecks related to feeding data consistently to GPUs during deep learning workloads, the key storage metric to consider is sequential read speed. Deep learning training typically involves streaming large datasets sequentially from storage to GPUs. The sequential read speed measures how fast data can be read in a continuous stream, directly impacting the ability to keep GPUs fed without stalls.
* Disk I/O operations per second (IOPS) measures random read/write operations and is less relevant for large sequential data streams in AI workloads.
* Disk free space indicates available storage capacity but does not impact data feed rate.
* Disk utilization in performance manager shows overall usage but does not specify the speed or consistency of data feed.
Therefore, focusing on sequential read speed (option C) is critical for ensuring consistent, high- throughput data feeding to GPUs, minimizing bottlenecks in deep learning runtime environments.
This is consistent with NVIDIA AI Operations best practices for system performance optimization and troubleshooting storage-related issues in AI infrastructure.
NEW QUESTION # 66
You are deploying a containerized application from NGC that relies on the NVIDIA Data Loading Library (DALI) for efficient data preprocessing. You want to ensure that DALI can access the GPU within the container. What steps are necessary to configure DALI correctly?
Answer: B,D,E
Explanation:
The NVIDIA Container Toolkit enables GPU access. 'CUDA_VISIBLE DEVICES' controls GPU visibility. specifies the GPU device within the DALI pipeline. A is incorrect; drivers are provided by the host. D defeats the purpose of using DALI for GPU-accelerated data preprocessing.
NEW QUESTION # 67
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