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| Section | Weight | Objectives |
|---|---|---|
| AI Operations | 22% | - Describe AI cluster orchestration and job scheduling essentials - 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 |
| AI Infrastructure | 40% | - Identify hardware requirements for specific AI training task use cases - Identify facility requirements - 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 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 - Determine networking requirements for AI workloads - Scale a GPU infrastructure for different use cases - Explain the purpose and benefits of a DPU in a datacenter - Identify and describe DC networking protocols and key concepts |
| Essential AI Knowledge | 38% | - Describe the software components related to the life cycle of AI development and deployment - Explain the purpose and use case of various NVIDIA solutions - Describe the NVIDIA software stack used in an AI environment - Explain the factors contributing to recent rapid improvements and adoption of AI - Differentiate the concepts of AI, machine learning, and deep learning - Compare and contrast GPU and CPU architectures - Explain the key AI use cases and industries - Compare and contrast training and inference architecture requirements and considerations |
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NEW QUESTION # 26
When deploying high-density workloads in a data center, what are the three main resource constraints that need to be considered?
Answer: B
Explanation:
High-density workloads (e.g., GPU clusters for AI) strain data center resources, primarily power (to supply dense servers), cooling (to dissipate heat from tightly packed hardware), and physical space (to house equipment). While processing speed, bandwidth, and other factors matter, power, cooling, and space are the physical constraints most critical to deployment feasibility.
NEW QUESTION # 27
A retail company is considering using AI to enhance its operations. They want to improve customer experience, optimize inventory management, and personalize marketing campaigns. Which AI use case would be most impactful in achieving these goals?
Answer: B
Explanation:
AI-powered recommendation systems are the most impactful use case for improving customer experience, optimizing inventory, and personalizing marketing in retail. These systems, accelerated by NVIDIA GPUs and deployed via Triton Inference Server, analyze customer behavior to deliver tailored suggestions, driving sales, reducing overstock, and enhancing campaigns. NVIDIA's "State of AI in Retail and CPG" report highlights recommendation systems as a top retail AI application.
NLP chatbots (B) improve support but don't address inventory or marketing directly. Fraud detection (C) is security-focused, not operational. Image recognition (D) aids warehousing but lacks broad impact. NVIDIA prioritizes recommendations for retail goals.
NEW QUESTION # 28
What is the primary command for checking the GPU utilization on a single DGX H100 system?
Answer: A
NEW QUESTION # 29
How is the architecture different in a GPU versus a CPU?
Answer: C
Explanation:
A GPU's architecture is designed for massive parallelism, featuring thousands of lightweight cores that execute simple instructions across vast data elements simultaneously-ideal for tasks like AI training. In contrast, a CPU has fewer, complex cores optimized for sequential execution and branching logic. GPUs don' t function as PCIe controllers (a hardware role), nor are they single-core designs, making the parallel execution focus the key differentiator.
(Reference: NVIDIA GPU Architecture Whitepaper, Section on GPU Design Principles)
NEW QUESTION # 30
In training and inference architecture requirements, what is the main difference between training and inference?
Answer: B
Explanation:
The primary distinction between training and inference lies in their operational demands. Training necessitates large amounts of data to iteratively optimize model parameters, often involving extensive datasets processed in batches across multiple GPUs to achieve convergence. Inference, however, is designed for real- time or low-latency processing, where trained models are deployed to make predictions on new inputs with minimal delay, typically requiring less data volume but high responsiveness. This fundamental difference shapes their respective architectural designs and resource allocations.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Training vs. Inference Requirements)
NEW QUESTION # 31
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