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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure | 40% | - Identify and describe DC networking protocols and key concepts - Identify facility requirements - Determine networking requirements for AI workloads - Explain the purpose and benefits of a DPU in a datacenter - Identify key components and considerations of a cluster of an accelerated infrastructure - 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 high speed DC network options and their use cases - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Scale a GPU infrastructure for different use cases |
| Topic 2: AI Operations | 22% | - Describe AI cluster orchestration and job scheduling essentials - Describe AI data center management and monitoring essentials - Articulate the key measures and criteria related to monitoring GPUs - Identify the key considerations for virtualizing accelerated infrastructure |
| Topic 3: Essential AI Knowledge | 38% | - Differentiate the concepts of AI, machine learning, and deep learning - Compare and contrast GPU and CPU architectures - Explain the factors contributing to recent rapid improvements and adoption of AI - Compare and contrast training and inference architecture requirements and considerations - Explain the key AI use cases and industries - Describe the NVIDIA software stack used in an AI environment - Explain the purpose and use case of various NVIDIA solutions - Describe the software components related to the life cycle of AI development and deployment |
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問題 #81
A customer is evaluating an AI cluster for training and is questioning why they should use a large number of nodes. Why would multi-node training be advantageous?
答案：B
解題說明：
Multi-node training is advantageous when a model's size--its parameters, activations, and gradients-exceeds the memory capacity of a single GPU. By sharding the model across multiple nodes (using techniques like data parallelism or model parallelism), training becomes feasible and efficient. User count and inference scale are unrelated to training architecture needs, which focus on compute and memory distribution.
問題 #82
Your AI infrastructure team is managing a deep learning model training pipeline that uses NVIDIA GPUs.
During the model training phase, you observe inconsistent performance, with some GPUs underutilized while others are at full capacity. What is the most effective strategy to optimize GPU utilization across the training cluster?
答案：D
解題說明：
Using NVIDIA's Multi-Instance GPU (MIG) feature to partition GPUs is the most effective strategy to optimize utilization across a training cluster with inconsistent performance. MIG, available on NVIDIA A100 GPUs, allows a single GPU to be divided into isolated instances, each assigned to specific workloads, ensuring balanced resource use and preventing underutilization. Option A (mixed precision) improves performance but doesn't address uneven GPU usage. Option B (fewer GPUs) risks reducing throughput without solving the issue. Option D (disabling auto-scaling) limits adaptability, worsening imbalance.
NVIDIA's documentation on MIG highlights its role in optimizing multi-workload clusters, making it ideal for this scenario.
問題 #83
You are tasked with virtualizing the GPU resources in a multi-tenant AI infrastructure where different teams need isolated access to GPU resources. Which approach is most suitable for ensuring efficient resource sharing while maintaining isolation between tenants?
答案：C
解題說明：
NVIDIA vGPU (Virtual GPU) Technology is the most suitable approach for virtualizing GPU resources in a multi-tenant AI infrastructure while ensuring efficient sharing and isolation. vGPU allows multiple VMs to share a physical GPU with dedicated memory and compute slices, providing isolation via virtualization while maximizing resource utilization. NVIDIA's vGPU documentation highlights its use in enterprise environments for secure, scalable AI workloads. Option B (GPU passthrough) dedicates entire GPUs, reducing sharing efficiency. Option C (containers without isolation) risks resource contention. Option D (CPU-based virtualization) excludes GPU acceleration. vGPU is NVIDIA's recommended solution for this scenario.
問題 #84
Which of the following statements is true about Kubernetes orchestration?
答案：C,D
解題說明：
Kubernetes excels in container orchestration with advanced scheduling (assigning workloads based on resource needs and availability) and load balancing (distributing traffic across pods via Services). It's not inherently bare-metal (it runs on various platforms), and inferencing capability depends on applications, not Kubernetes itself, making B and D the true statements.
問題 #85
In a virtualized AI environment, you are responsible for managing GPU resources across several VMs running different AI workloads. Which approach would most effectively allocate GPU resources to maximize performance and flexibility?
答案：B
解題說明：
Implementing GPU virtualization to allow multiple VMs to share GPU resources dynamically based on demand is the most effective approach for maximizing performance and flexibility in a virtualized AI environment. NVIDIA's GPU virtualization (e.g., via vGPU or GPU Operator in Kubernetes) enables time- slicing or partitioning (e.g., MIG on A100 GPUs), allowing workloads to access GPU resources as needed.
This optimizes utilization and adapts to varying demands, as outlined in NVIDIA's "GPU Virtualization Guide" and "AI Infrastructure for Enterprise." A single VM (A) limits scalability. Dedicated GPUs per VM (B) wastes resources when idle. GPU passthrough (D) restricts sharing, reducing flexibility. NVIDIA recommends virtualization for efficient resource allocation in virtualized AI setups.
問題 #86
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