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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Essential AI Knowledge | 38% | - Compare and contrast GPU and CPU architectures - Explain the key AI use cases and industries - Differentiate the concepts of AI, machine learning, and deep learning - Explain the factors contributing to recent rapid improvements and adoption of AI - Describe the software components related to the life cycle of AI development and deployment - 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 |
| Topic 2: AI Infrastructure | 40% | - Identify hardware requirements for specific AI training task use cases - Identify key components and considerations of a cluster of an accelerated infrastructure - Identify and describe DC networking protocols and key concepts - 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 - Explain the purpose and benefits of a DPU in a datacenter - Identify facility requirements - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures - Determine networking requirements for AI workloads |
| Topic 3: AI Operations | 22% | - Describe AI cluster orchestration and job scheduling essentials - Articulate the key measures and criteria related to monitoring GPUs - Describe AI data center management and monitoring essentials - Identify the key considerations for virtualizing accelerated infrastructure |
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NEW QUESTION # 60
A large healthcare provider wants to implement an AI-driven diagnostic system that can analyze medical images across multiple hospitals. The system needs to handle large volumes of data, comply with strict data privacy regulations, and provide fast, accurate results. The infrastructure should also support future scaling as more hospitals join the network. Which approach using NVIDIA technologies would best meet the requirements for this AI-driven diagnostic system?
Answer: C
Explanation:
Deploying the AI model on NVIDIA DGX A100 systems in a centralized data center with NVIDIA Clara is the best approach for an AI-driven diagnostic system in healthcare. The DGX A100provides high- performance GPU computing for training and inference on large medical image datasets, while NVIDIA Clara offers a healthcare-specific AI platform with pre-trained models, privacy-preserving tools (e.g., federated learning), and scalability features. A centralized data center ensures compliance with privacy regulations (e.g., HIPAA) via secure data handling and supports future scaling as more hospitals join.
Generic CPU servers with TensorFlow (A) lack the GPU acceleration needed for fast, large-scale image analysis. Quadro RTX GPUs (B) are for visualization, not enterprise-scale AI diagnostics. Jetson Nano (C) is for edge inference, not centralized, scalable diagnostic systems. NVIDIA's "Clara Documentation" and "AI Infrastructure for Enterprise" validate this approach for healthcare AI.
NEW QUESTION # 61
A financial institution is implementing a real-time fraud detection system using deep learning models. The system needs to process large volumes of transactions with very low latency to identify fraudulent activities immediately. During testing, the team observes that the system occasionally misses fraudulent transactions under heavy load, and latency spikes occur. Which strategy would best improve the system's performance and reliability?
Answer: C
Explanation:
Implementing model parallelism to split the deep learning model across multiple NVIDIA GPUs is the best strategy to improve performance and reliability for a real-time fraud detection system under heavy load.
Model parallelism divides the computational workload of a large model across GPUs, reducing latency and increasing throughput by leveraging parallel processing capabilities, a strength of NVIDIA's architecture (e.
g., TensorRT, NCCL). This addresses latency spikes and missed detections by ensuring the system scales with demand. Option A (CPU cluster) sacrifices GPU acceleration, increasing latency. Option B (reducing complexity) may lower accuracy, undermining fraud detection. Option C (larger dataset) improves training but not inference performance. NVIDIA's fraud detection use cases highlight model parallelism as a key optimization technique.
NEW QUESTION # 62
A financial institution is implementing an AI-driven fraud detection system that needs to process millions of transactions daily in real-time. The system must rapidly identify suspicious activity and trigger alerts, while also continuously learning from new data to improve accuracy. Which architecture is most appropriate for this scenario?
Answer: C
Explanation:
A hybrid setup with multi-GPU servers (e.g., NVIDIA DGX) for training and edge devices (e.g., NVIDIA Jetson) for inference is most appropriate. Multi-GPU servers handle continuous training on large datasets with high compute power, while edge devices enable low-latency inference for real-time fraud detection, balancing scalability and speed. Option A (single GPU) lacks scalability. Option B (edge-only ARM) can't handle training demands. Option D (CPU-based) sacrifices GPU acceleration. NVIDIA's fraud detection architectures endorse this hybrid model.
NEW QUESTION # 63
A simul-ation is bottlenecked by memory transfer speeds. Which GPU architectural feature addresses this?
Answer: A
Explanation:
The correct answer is A because memory-transfer bottlenecks are addressed by GPU memory-system features such as high-bandwidth memory, shared memory, cache, and high-bandwidth interconnects or buses.
NVIDIA's Blackwell tuning guide describes the GPU memory system and states that the NVIDIA B200 GPU supports HBM3 and HBM3e high-bandwidth memory with capacity up to 180 GB. NVIDIA's CUDA tuning documentation also describes shared memory as an important architectural resource available per streaming multiprocessor, which helps reduce slower memory traffic when used effectively.
Why the other options are incorrect: GPUs are not normally wired as main disk controllers. Increasing generic PCIe I/O ports does not directly solve simulation memory-transfer bottlenecks inside GPU execution.
Dedicated inference ASICs are not the general NVIDIA GPU architectural feature used to address memory- transfer performance in simulation workloads.
Reference: NVIDIA CUDA Blackwell Tuning Guide; NVIDIA CUDA Ada GPU Architecture Tuning Guide.
NEW QUESTION # 64
Which of the following NVIDIA compute platforms is best suited for deploying AI workloads at the edge with minimal latency?
Answer: A
Explanation:
NVIDIA Jetson (D) is best suited for deploying AI workloads at the edge with minimal latency. The Jetson family (e.g., Jetson Nano, AGX Xavier) is designed for compact, power-efficient edge computing, delivering real-time AI inference for applications like IoT, robotics, and autonomous systems. It integrates GPU, CPU, and I/O in a single module, optimized for low-latency processing on-site.
* NVIDIA GRID(A) is for virtualized GPU sharing, not edge deployment.
* NVIDIA Tesla(B) is a data center GPU, too power-hungry for edge use.
* NVIDIA RTX(C) targets gaming/workstations, not edge-specific needs.
Jetson's edge focus is well-documented by NVIDIA (D).
NEW QUESTION # 65
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