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NVIDIA AI Infrastructure Sample Questions (Q14-Q19):

NEW QUESTION # 14
In a large-scale InfiniBand fabric, you need to implement a mechanism to prioritize traffic for a specific application that requires low latency and high bandwidth. You want to leverage Quality of Service (QOS) to achieve this. Which of the following steps are essential to properly configure QOS in this scenario? (Select THREE)

A. Configure Weighted Fair Queueing (WFQ) or Strict Priority Queueing on the egress ports of the InfiniBand switches to prioritize the application's traffic class.
B. Map the application's traffic to a specific traffic class with appropriate priority settings within the InfiniBand switches.
C. Mark the application's traffic with appropriate DiffServ Code Point (DSCP) values.
D. Disable Adaptive Routing (AR) to ensure that the application's traffic always takes the shortest path.
E. Configure VLAN tagging on the application's traffic to isolate it from other traffic.

Answer: A,B,C

Explanation:
Effective QOS requires traffic classification (DSCP marking), mapping to appropriate traffic classes with priority settings, and configuring queueing mechanisms (WFQ/Strict Priority Queueing) on egress ports to enforce the priority. VLAN tagging is useful for network segmentation but not directly for QOS. Disabling AR might reduce path diversity, but could also lead to congestion if the shortest path is already heavily utilized.

NEW QUESTION # 15
You encounter an error during MIG instance creation using 'nvidia-smi' stating 'Insufficient GPU resources'. Which of the following could be the cause? (Select all that apply)

A. The GPIJ is in a bad state and needs to be reset.
B. The NVIDIA driver version is outdated and does not support the requested MIG configuration.
C. The GPU is already fully utilized by other MIG instances or processes.
D. There is no error; MIG always creates instances regardless of resources.
E. The requested MIG configuration exceeds the GPU's available resources (e.g., compute or memory).

Answer: B,C,E

Explanation:
The 'Insufficient GPIJ resources' error indicates that the requested MIG instance creation cannot be fulfilled due to limitations in available resources (A) such as compute or memory. Outdated drivers (B) may not support the requested MIG configurations and hence can lead to resource management problems. When other instances or processes already consume all available resources (C), the operation can't continue. A GPU in a bad state might cause issues, but the specific error message points to resource exhaustion more directly. MIG does not bypass resource checks (E).

NEW QUESTION # 16
You are using the NVIDIA Container Toolkit in a Kubernetes environment with multiple GPUs per node. You want to ensure that pods can request specific GPUs on a node, rather than simply requesting 'any' GPU. Which Kubernetes feature, in conjunction with the NVIDIA Device Plugin, allows you to achieve this fine-grained GPU resource allocation?

A. Resource Quotas
B. Device Plugins API
C. Node Affinity
D. Topology Manager
E. Taints and Tolerations

Answer: D

Explanation:
The Kubernetes Topology Manager (C) allows you to align resource allocations (including GPIJs) with specific NIJMA nodes. This is critical for performance when dealing with multiple GPUs per node. Resource Quotas (A) limit resource usage but don't control specific GPU selection. Node Affinity (B) selects nodes based on labels, not specific GPUs. The Device Plugins API (D) enables GPIJ discovery, but the Topology Manager is needed for fine-grained allocation within a node. Taints and Tolerations (E) are used to prevent pods from being scheduled on certain nodes unless they have the corresponding toleration, and does not directly allow for the selection of a particular GPU.

NEW QUESTION # 17
You're deploying a multi-GPU training job on a cluster using Slurm. You need to ensure that the GPUs allocated to the job are healthy and functioning correctly before the training starts. What's the MOST effective approach to pre-validate the GPU hardware?

A. Allocate all available GPUs to the job and assume they are healthy.
B. Check the output of 'nvidia-smi' to ensure all GPUs are listed and have the expected memory.
C. Execute the NVIDIA Data Center GPU Manager (DCGM) diagnostic suite on the allocated GPUs.
D. Monitor the GPU temperature using 'nvidia-smi' during the first few minutes of the training job.
E. Run a simple CUDA vector addition program on each GPU and check for errors.

Answer: C

Explanation:
Using the DCGM diagnostic suite is the most thorough way to pre-validate GPU hardware. DCGM provides comprehensive tests to check GPU health, including memory, compute, and interconnects. A simple CUDA program or checking nvidia-smr provides basic validation, but not as comprehensive as DCGM. Monitoring temperature is reactive, not proactive. Assuming GPUs are healthy without validation is risky.

NEW QUESTION # 18
You are designing a large-scale AI training cluster spanning multiple racks. The networking topology necessitates both short-reach (within rack) and long-reach (inter-rack) connections. Which combination of cable types and transceivers is MOST cost-effective and suitable for this scenario, assuming a mix of 200GbE and 400GbE links?

A. Passive copper cables for all intra-rack connections and ER4 transceivers with single-mode fiber for all inter-rack connections.
B. AOC cables for all connections, both intra-rack and inter-rack.
C. DAC cables for all intra-rack connections, and a mix of SR4 and LR4 transceivers with multimode and single-mode fiber, respectively, for inter-rack connections.
D. DAC cables for all intra-rack connections and FR4 transceivers with single-mode fiber for inter-rack connections.
E. DAC cables for all intra-rack connections and AOC cables for all inter-rack connections.

Answer: C

Explanation:
DAC cables are cost-effective and suitable for short-reach, high-bandwidth connections within a rack. For inter-rack connections, SR4 transceivers with multimode fiber (for shorter inter-rack links) and LR4 transceivers with single-mode fiber (for longer inter-rack links) provide a good balance of cost and performance. AOCs are generally more expensive than DACs. ER4 is overkill for many inter-rack scenarios and is more expensive than LR4.

NEW QUESTION # 19
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