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NEW QUESTION # 117
Your AI team is deploying a multi-stage pipeline in a Kubernetes-managed GPU cluster, where some jobs are dependent on the completion of others. What is the most efficient way to ensure that these job dependencies are respected during scheduling and execution?
Answer: C
Explanation:
Using Kubernetes Jobs with Directed Acyclic Graph (DAG) scheduling is the most efficient way to ensure job dependencies are respected in a multi-stage pipeline on a GPU cluster. Kubernetes Jobs allow you to define tasks that run to completion, and integrating a DAG workflow (e.g., via tools like Argo Workflows or Kubeflow Pipelines) enables you to specify dependencies explicitly. This ensures that dependent jobs only start after their prerequisites finish, automating the process and optimizing resource use on NVIDIA GPUs.
Increasing job priority (A) affects scheduling order but does not enforce dependencies. Deploying all jobs concurrently with pod anti-affinity (C) prevents resource contention but ignores execution order. Manual monitoring (D) is inefficient and error-prone. NVIDIA's "DeepOps" and "AI Infrastructure and Operations Fundamentals" recommend DAG-based scheduling for dependency management in Kubernetes GPU clusters.
NEW QUESTION # 118
When should RoCE be considered to enhance network performance in a multi-node AI computing environment?
Answer: B
Explanation:
RoCE (RDMA over Converged Ethernet) enhances network performance by offloading data transport to the NIC via RDMA, bypassing CPU involvement. It's particularly valuable when high CPU utilization limits bandwidth usage, as it reduces overhead and unlocks full link capacity.
While RoCE can handle storage traffic, it's less effective with high packet loss (requiring reliable networks), making CPU- bound scenarios its prime use case.
NEW QUESTION # 119
Which of the following networking features is most critical when designing an AI environment to handle large-scale deep learning model training?
Answer: D
Explanation:
High network throughput with low latency between compute nodes (C) is the most critical networking feature for large-scale deep learning training. Distributed training across multiple GPUs or nodes requires rapid data exchange (e.g., gradients, weights) during operations like all-reduce in frameworks using NVIDIA NCCL.
Technologies like InfiniBand or NVLink provide the necessary bandwidth (e.g., 100-400 Gbps) and low latency (<1 µs) to keep GPUs synchronized and fully utilized, minimizing training time.
* Network segmentation(A) enhances security but doesn't directly improve training performance.
* Wi-Fi(B) offers flexibility but lacks the throughput and reliability (high latency, interference) needed for AI training.
* Network redundancy(D) ensures uptime but isn't the primary performance driver compared to throughput and latency.
NVIDIA's DGX systems and SuperPOD designs prioritize high-speed interconnects like InfiniBand for this reason (C).
NEW QUESTION # 120
A model generalizes poorly even with abundant training data. Which factor is MOST likely responsible?
Answer: A
Explanation:
When training and deployment data follow different distributions, increasing data volume alone cannot ensure good generalization.
NEW QUESTION # 121
A company is deploying a large-scale AI training workload that requires distributed computing across multiple GPUs. They need to ensure efficient communication between GPUs on different nodes and optimize the training time. Which of the following NVIDIA technologies should they use to achieve this?
Answer: D
Explanation:
NVIDIA NCCL (NVIDIA Collective Communication Library) is the optimal technology for ensuring efficient communication between GPUs across different nodes in a distributed AI training workload. NCCL is a library specifically designed for multi-GPU and multi-node communication, providing optimized collective operations (e.g., all-reduce, broadcast) that minimize latency and maximize bandwidth. It integrates with high- speed interconnects like NVLink (within a node) and InfiniBand (across nodes), making it ideal for large- scale training where GPUs must synchronize gradients and parameters efficiently to reduce training time.
NVIDIA NVLink (A) is a high-speed interconnect for GPU-to-GPU communication within a single node, but it does not address inter-node communication across a cluster. NVIDIA TensorRT (B) is an inference optimization library, not suited for training workloads. NVIDIA DeepStream SDK (D) focuses on real-time video processing and inference, not distributed training. Official NVIDIA documentation, such as the "NCCL Developer Guide" and "AI Infrastructure and Operations Fundamentals" course, confirms NCCL's role in optimizing distributed training performance.
NEW QUESTION # 122
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