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| Section | Weight | Objectives |
|---|---|---|
| Essential AI Knowledge | 38% | - Differentiate the concepts of AI, machine learning, and deep learning - Describe the NVIDIA software stack used in an AI environment - Compare and contrast training and inference architecture requirements and considerations - Explain the purpose and use case of various NVIDIA solutions - 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 GPU and CPU architectures - Explain the factors contributing to recent rapid improvements and adoption of AI |
| AI Operations | 22% | - 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 - Describe AI cluster orchestration and job scheduling essentials |
| AI Infrastructure | 40% | - Scale a GPU infrastructure for different use cases - Identify and describe DC networking protocols and key concepts - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Identify high speed DC network options and their use cases - Explain the purpose and benefits of a DPU in a datacenter - 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 key components and considerations of a cluster of an accelerated infrastructure - Identify facility requirements - Determine networking requirements for AI workloads |
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NEW QUESTION # 50
What is CUDA
Answer: B
Explanation:
CUDA is NVIDIA's parallel programming toolkit that enables developers to harness the power of NVIDIA GPUs for general-purpose computing, accelerating tasks such as deep learning, simulations, and scientific computations.
NEW QUESTION # 51
Which NVIDIA software provides the capability to virtualize a GPU?
Answer: C
Explanation:
NVIDIA vGPU (Virtual GPU) software enables GPU virtualization by partitioning a physical GPU into multiple virtual instances, assignable to virtual machines or containers for accelerated workloads. Horizon is a VMware product, and "virtGPU" isn't an NVIDIA offering, confirming vGPU as the correct solution.
NEW QUESTION # 52
You are working under the supervision of a senior AI engineer on a project involving large-scale data processing using NVIDIA GPUs. The task involves analyzing a large dataset of images to train a deep learning model. You need to ensure that the data pipeline is optimized for performance while minimizing resource usage. Which of the following techniques would best optimize the data pipeline for training a deep learning model on NVIDIA GPUs?
Answer: C
Explanation:
Implementing mixed precision training is the best technique to optimize the data pipeline for training a deep learning model on NVIDIA GPUs while minimizing resource usage. Mixed precision training uses lower- precision data types (e.g., FP16 instead of FP32), reducing memory consumption and speeding up computation without sacrificing accuracy. This allows larger batches to fit in GPU memory, improves throughput, and leverages Tensor Cores on NVIDIA GPUs (e.g., A100, H100), as detailed in NVIDIA's
"Mixed Precision Training Guide." It directly enhances pipeline efficiency by optimizing GPU resource utilization.
Loading the entire dataset into GPU memory (A) is impractical for large datasets and wastes resources. Data sharding across CPUs (B) offloads work from GPUs, slowing the pipeline. Data augmentation on the CPU (C) creates a bottleneck, as GPUs can handle augmentation faster. NVIDIA's documentation prioritizes mixed precision for performance and efficiency.
NEW QUESTION # 53
Which networking feature is most important for supporting distributed training of large AI models across multiple data centers?
Answer: D
Explanation:
High throughput with low latency WAN links between data centers is the most important networking feature for supporting distributed training of large AI models. Distributed training across multiple data centers requires rapid exchange of gradients and model parameters, which demands high-bandwidth, low-latency connections (e.g., InfiniBand or high-speed Ethernet over WAN). NVIDIA's "DGX SuperPOD Reference Architecture" and "AI Infrastructure for Enterprise" emphasize that network performance is critical for scaling AI training geographically, ensuring synchronization and minimizing training time.
QoS policies (B) prioritize traffic but don't address raw performance needs. Segregated segments (C) enhance security, not training efficiency. Wireless networking (D) lacks the reliability and bandwidth for data center AI. NVIDIA prioritizes high-throughput, low-latency networking for distributed training.
NEW QUESTION # 54
You manage a large-scale AI infrastructure where several AI workloads are executed concurrently across multiple NVIDIA GPUs. Recently, you observe that certain GPUs are underutilized while others are overburdened, leading to suboptimal performance and extended processing times. Which of the following strategies is most effective in resolving this imbalance?
Answer: C
Explanation:
Uneven GPU utilization in a multi-GPU infrastructure indicates poor workload distribution. Implementing dynamic GPU load balancing-using tools like NVIDIA Triton Inference Server or Kubernetes with GPU Operator-assigns tasks based on real-time GPU usage, ensuring balanced workloads and optimal performance. This strategy, common in DGX clusters, reduces processing times by preventing overburdening or idling.
Reducing batch size (Option B) lowers GPU demand uniformly but doesn't address imbalance and may reduce throughput. Increasing power limits (Option C) might boost underutilized GPUs slightly but doesn't fix distribution. Disabling overclocking (Option D) ensures consistency but not balance. Dynamic balancing is NVIDIA's recommended approach.
NEW QUESTION # 55
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