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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Operations | 22% | - Describe AI data center management and monitoring essentials - Identify the key considerations for virtualizing accelerated infrastructure - Articulate the key measures and criteria related to monitoring GPUs - Describe AI cluster orchestration and job scheduling essentials |
| Topic 2: AI Infrastructure | 40% | - Scale a GPU infrastructure for different use cases - Identify key components and considerations of a cluster of an accelerated infrastructure - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Explain the purpose and benefits of a DPU in a datacenter - Identify and describe DC networking protocols and key concepts - Determine networking requirements for AI workloads - Identify high speed DC network options and their use cases - Identify hardware requirements for specific AI training task use cases - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures - Identify facility requirements |
| Topic 3: Essential AI Knowledge | 38% | - Explain the factors contributing to recent rapid improvements and adoption of AI - Differentiate the concepts of AI, machine learning, and deep learning - Describe the software components related to the life cycle of AI development and deployment - Explain the key AI use cases and industries - Compare and contrast training and inference architecture requirements and considerations - Explain the purpose and use case of various NVIDIA solutions - Describe the NVIDIA software stack used in an AI environment - Compare and contrast GPU and CPU architectures |
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NEW QUESTION # 65
Which scenario BEST illustrates concept drift?
Answer: B
Explanation:
Concept drift occurs when the underlying data-generating process changes over time.
NEW QUESTION # 66
Your AI model training process suddenly slows down, and upon inspection, you notice that some of the GPUs in your multi-GPU setup are operating at full capacity while others are barely being used. What is the most likely cause of this imbalance?
Answer: B
Explanation:
Uneven GPU utilization in a multi-GPU setup often stems from an imbalanced data loading process. In distributed training, if data isn't evenly distributed across GPUs (e.g., via data parallelism), some GPUs receive more work while others idle, causing performance slowdowns. NVIDIA's NCCL ensures efficient communication between GPUs, but it relies on the data pipeline-managed by tools like NVIDIA DALI or PyTorch DataLoader-to distribute batches uniformly. A bottleneck in data loading, such as slow I/O or poor partitioning, is a common culprit, detectable via NVIDIA profiling tools like Nsight Systems.
Model code optimized for specific GPUs (Option A) is unlikely unless explicitly written to exclude certain GPUs, which is rare. Different GPU models (Option B) can cause imbalances due to varying capabilities, but NVIDIA frameworks typically handle heterogeneity; this would be a design flaw, not a sudden issue.
Improper installation (Option C) would likely cause complete failures, not partial utilization. Data distribution is the most probable and fixable cause, per NVIDIA's distributed training best practices.
NEW QUESTION # 67
What is the maximum number of MIG instances that an H100 GPU provides?
Answer: C
Explanation:
The NVIDIA H100 GPU supports up to 7 Multi-Instance GPU (MIG) partitions, allowing it to be divided into seven isolated instances for multi-tenant or mixed workloads. This capability leverages the H100's architecture to maximize resource flexibility and efficiency, with 7 being the documented maximum.
(Reference: NVIDIA H100 GPU Documentation, MIG Section)
NEW QUESTION # 68
You are supporting a senior engineer in troubleshooting an AI workload that involves real-time data processing on an NVIDIA GPU cluster. The system experiences occasional slowdowns during data ingestion, affecting the overall performance of the AI model. Which approach would be most effective in diagnosing the cause of the data ingestion slowdown?
Answer: D
Explanation:
Profiling the I/O operations on the storage system is the most effective approach to diagnose the cause of data ingestion slowdowns in a real-time AI workload on an NVIDIA GPU cluster. Slowdowns during ingestion often stem from bottlenecks in data transfer between storage and GPUs (e.g., disk I/O, network latency), which can starve the GPUs of data and degradeperformance. Tools like NVIDIA DCGM or system-level profilers (e.g., iostat, nvprof) can measure I/O throughput, latency, and bandwidth, pinpointing whether storage performance is the issue. NVIDIA's "AI Infrastructure and Operations" materials stress profiling I/O as a critical step in diagnosing data pipeline issues.
Switching frameworks (B) may not address the root cause if I/O is the bottleneck. Adding GPUs (C) increases compute capacity but doesn't solve ingestion delays. Optimizing inference code (D) improves model efficiency, not data ingestion. Profiling I/O is the recommended first step per NVIDIA guidelines.
NEW QUESTION # 69
You are responsible for managing an AI data center that handles large-scale deep learning workloads. The performance of your training jobs has recently degraded, and you've noticed that the GPUs are underutilized while CPU usage remains high. Which of the following actions would most likely resolve this issue?
Answer: B
Explanation:
GPU underutilization with high CPU usage during training suggests a bottleneck in the data pipeline, where CPUs can't feed data to GPUs fast enough, starving them of work. Optimizing the data pipeline for better I/O throughput-using NVIDIA DALI for GPU-accelerated data loading or improving storage (e.g., NVMe SSDs)
-ensures data reaches GPUs efficiently, maximizing utilization. This is a common issue in NVIDIA DGX systems, where pipeline optimization is critical for large-scale workloads.
Increasing GPU memory (Option A) doesn't address data delivery. Reducing batch size (Option B) might lower GPU demand but reduces throughput, not solving the root cause. Adding GPUs (Option C) exacerbates underutilization without fixing the bottleneck. NVIDIA's training optimization guides prioritize pipeline efficiency.
NEW QUESTION # 70
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