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| Section | Objectives |
|---|---|
| Networking for AI Infrastructure | - High-speed interconnects (InfiniBand, Ethernet) - Bandwidth and latency considerations |
| AI Operations and Lifecycle Management | - Monitoring and observability of AI systems - Model deployment workflows |
| Storage and Data Pipelines | - Distributed storage concepts - Data throughput for training workloads |
| AI Infrastructure Fundamentals | - Accelerated computing concepts (GPU vs CPU workloads) - AI workload architecture overview |
| Performance, Reliability, and Troubleshooting | - Performance tuning for GPU workloads - Common infrastructure failure diagnostics |
| System and Cluster Architecture | - Cluster design for AI workloads - DGX / HGX systems overview |
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質問 # 110
Your AI team is running a distributed deep learning training job on an NVIDIA DGX A100 clusterusing multiple nodes. The training process is slowing down significantly as the model size increases. Which of the following strategies would be most effective in optimizing the training performance?
正解：C
解説：
Enabling Mixed Precision Training is the most effective strategy to optimize training performance on an NVIDIA DGX A100 cluster as model size increases. Mixed precision uses lower-precision data types (e.g., FP16) alongside FP32, reducing memory usage and leveraging Tensor Cores on A100 GPUs for faster computation without significant accuracy loss. This approach, detailed in NVIDIA's "Mixed Precision Training Guide," accelerates training by allowing larger models to fit in GPU memory and speeding up matrix operations, addressing slowdowns in distributed setups.
Data parallelism (B) distributes data but may not help if memory constraints slow computation. Decreasing nodes (C) reduces parallelism, worsening performance. Increasing batch size (D) can strain memory further, exacerbating slowdowns. NVIDIA's DGX A100 documentation highlights mixed precision as a key optimization for large models.
質問 # 111
What is the importance of a job scheduler in an AI resource-constrained cluster?
正解：C
解説：
In a resource-constrained AI cluster, a job scheduler (e.g., Slurm) efficiently allocates limited resources (GPUs, CPUs) to workloads, optimizing utilization and job execution time. It prioritizes based on policies, not just first-come-first-served, and doesn't add resources or run all jobs simultaneously, focusing instead on resource optimization.
質問 # 112
In your multi-tenant AI cluster, multiple workloads are running concurrently, leading to some jobs experiencing performance degradation. Which GPU monitoring metric is most critical for identifying resource contention between jobs?
正解：B
解説：
GPU Utilization Across Jobs is the most critical metric for identifying resource contention in a multi-tenant cluster. It shows how GPU resources are divided among workloads, revealing overuse or starvation via tools like nvidia-smi. Option B (temperature) indicates thermal issues, not contention. Option C (network latency) affects distributed tasks. Option D (memory bandwidth) is secondary. NVIDIA's DCGM supports this metric for contention analysis.
質問 # 113
Which NVIDIA platform is used to build and deploy commercial-grade, AI-powered robots?
正解：B
解説：
NVIDIA Isaac is the platform designed for developing and deploying AI-powered robots, providing simulation, AI models, and SDKs tailored for robotics applications.
質問 # 114
An AI research team is working on a large-scale natural language processing (NLP) model that requires both data preprocessing and training across multiple GPUs. They need to ensure that the GPUs are used efficiently to minimize training time. Which combination of NVIDIA technologies should they use?
正解：C
解説：
NVIDIA DALI (Data Loading Library) and NVIDIA NCCL (Collective Communications Library) are the best combination for efficient GPU use in NLP model training. DALI accelerates data preprocessing (e.g., tokenization) on GPUs, reducing CPU bottlenecks, while NCCL optimizes inter-GPU communication for distributed training, minimizing latency and maximizing utilization. Option A (TensorRT) focuses on inference, not training. Option B (DeepStream) targets video analytics. Option D (cuDNN, NGC) supports neural ops and model access but lacks preprocessing/communication focus. NVIDIA's NLP workflows recommend DALI and NCCL for efficiency.
質問 # 115
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さらに、PassTest NCA-AIIOダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1szp2sSeSIC7Yk8h05MC94jP-F6Z5WDtv