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| Section | Weight | Objectives |
|---|---|---|
| Workload Management | 20% | - Framework and runtime configuration - AI workload deployment and scaling - Job scheduling and queue management - Data pipeline and storage integration |
| Management | 28% | - Resource allocation and scheduling - Access control and security policies - Software and container lifecycle management - GPU cluster and compute node management |
| Troubleshooting and Optimization | 20% | - System health and reliability maintenance - Fault diagnosis and resolution - Throughput and latency optimization - Performance monitoring and analysis |
| Installation and Deployment | 32% | - AI cluster setup and configuration - Driver and firmware installation - NVIDIA software stack deployment - Container orchestration and resource management |
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NEW QUESTION # 36
You are designing a data center that must support both interactive AI development and large-scale batch training jobs. You want to maximize GPU utilization while ensuring that interactive users have a responsive experience. Which of the following strategies is MOST effective?
Answer: C
Explanation:
NVIDIA MPS allows multiple processes to share a GPU concurrently, which maximizes utilization. QOS ensures that interactive workloads receive priority, maintaining a responsive experience. Dedicated GPUs for interactive users wastes resources when they are idle. Scheduling batch jobs for off-peak hours is limiting and inefficient. Oversubscribing without QOS can severely impact interactive performance. Running all workloads on a single server creates a single point of failure and limits scalability.
NEW QUESTION # 37
You have multiple users sharing a server with a single NVIDIAA100 GPU. Two users, Alice and Bob, want to run deep learning experiments concurrently. Alice's job requires 20GB of GPU memory and 30% of compute, while Bob's job needs IOGB of GPU memory and 20% of compute. How can you use MIG to optimally configure the GPU to accommodate both users' requirements?
Answer: B
Explanation:
This question challenges understanding of MIG instance sizes. Options A and B are not correct because they allocate insufficient memory to Alice. Option C is not correct because it does not provide dedicated resources for Bob. Option E means that Alice's job is resource intensive. The correct answer is D because it ensures that both Alice and Bob get at least the memory they need and some compute resource allocation. 4g.20gb and 2g.10gb instances ensure allocation of resources required for both users independently.
NEW QUESTION # 38
A system administrator of a high-performance computing (HPC) cluster that uses an InfiniBand fabric for high-speed interconnects between nodes received reports from researchers that they are experiencing unusually slow data transfer rates between two specific compute nodes. The system administrator needs to ensure the path between these two nodes is optimal.
What command should be used?
Answer: B
Explanation:
To verify the optimal communication path and diagnose issues between two nodes in an InfiniBand fabric, the ibtracert command is used. It traces the route that InfiniBand packets take through the fabric, identifying each hop and any potential bottlenecks or faulty links along the path.
NEW QUESTION # 39
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: D
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 # 40
Which two (2) ways does the pre-configured GPU Operator in NVIDIA Enterprise Catalog differ from the GPU Operator in the public NGC catalog? (Choose two.)
Answer: B,C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The pre-configured GPU Operator in the NVIDIA Enterprise Catalog differs from the public NGC catalog GPU Operator primarily by its configuration to use aprebuilt vGPU driver imageand being configured to use theNVIDIA License System (NLS). These adaptations allow better support for enterprise environments where vGPU functionality and license management are critical.
Other options such as automatic installation of the Datacenter driver or additional installation of Network Operator are not specific differences highlighted between the two operators.
NEW QUESTION # 41
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