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| Section | Objectives |
|---|---|
| Topic 1: Performance, Reliability, and Troubleshooting | - Performance tuning for GPU workloads - Common infrastructure failure diagnostics |
| Topic 2: Storage and Data Pipelines | - Data throughput for training workloads - Distributed storage concepts |
| Topic 3: AI Operations and Lifecycle Management | - Monitoring and observability of AI systems - Model deployment workflows |
| Topic 4: System and Cluster Architecture | - DGX / HGX systems overview - Cluster design for AI workloads |
| Topic 5: AI Infrastructure Fundamentals | - Accelerated computing concepts (GPU vs CPU workloads) - AI workload architecture overview |
| Topic 6: Networking for AI Infrastructure | - High-speed interconnects (InfiniBand, Ethernet) - Bandwidth and latency considerations |
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NEW QUESTION # 37
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: A
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 # 38
In an AI infrastructure setup, you need to optimize the network for high-performance data movement between storage systems and GPU compute nodes. Which protocol would be most effective for achieving low latency and high bandwidth in this environment?
Answer: C
Explanation:
Remote Direct Memory Access (RDMA) is the most effective protocol for optimizing network performance between storage systems and GPU compute nodes in an AI infrastructure. RDMA enables direct memory access between devices over high-speed interconnects (e.g., InfiniBand, RoCE), bypassing the CPU and reducing latency while providing high bandwidth. This is critical for AI workloads, where large datasets must move quickly to GPUs for training or inference, minimizing bottlenecks.
HTTP (A) and SMTP (B) are application-layer protocols for web and email, respectively, unsuitable for low- latency data movement. TCP/IP (D) is a general-purpose networking protocol but lacks the performance of RDMA for GPU-centric workloads. NVIDIA's "DGX SuperPOD Reference Architecture" and "AI Infrastructure and Operations" materials highlight RDMA's role in high-performance AI networking.
NEW QUESTION # 39
You are managing an AI-driven autonomous vehicle project that requires real-time decision-making and rapid processing of large data volumes from sensors like LiDAR, cameras, and radar. The AI models must run on the vehicle's onboard hardware to ensure low latency and high reliability. Which NVIDIA solutions would be most appropriate to use in this scenario? (Select two)
Answer: A,D
Explanation:
For an autonomous vehicle requiring onboard, low-latency AI processing:
* NVIDIA Jetson AGX Xavier(B) is a compact, power-efficient edge AI platform designed for real-time processing in embedded systems like vehicles. It supports sensor fusion (LiDAR, cameras) and deep learning inference with high reliability.
* NVIDIA DRIVE AGX Pegasus(D) is a purpose-built automotive AI platform for Level 4/5 autonomy, delivering high-performance computing for sensor data processing and decision-making with automotive-grade reliability.
* NVIDIA DGX A100(A) is a data center system, unsuitable for onboard vehicle use due to size and power requirements.
* NVIDIA GeForce RTX 3080(C) is a consumer GPU for gaming, lacking automotive certification or edge optimization.
* NVIDIA Tesla T4(E) is a data center GPU for inference, not designed for vehicle onboard processing.
NVIDIA's DRIVE and Jetson platforms are tailored for autonomous vehicles (B and D).
NEW QUESTION # 40
What NVIDIA tool should a data center administrator use to monitor NVIDIA GPUs?
Answer: C
Explanation:
The NVIDIA Data Center GPU Manager (DCGM) is the recommended tool for data center administrators to monitor NVIDIA GPUs. It provides real-time health monitoring, telemetry (e.g., utilization, temperature), and diagnostics, tailored for large-scale deployments. NetQ focuses on network monitoring, and there's no
"NVIDIA System Monitor" in this context, making DCGM the correct choice.(Note: The document incorrectly lists D; C is intended.) (Reference: NVIDIA DCGM Documentation, Overview Section)
NEW QUESTION # 41
Which platform allows for the simple deployment of scalable AI models in production?
Answer: A
Explanation:
NVIDIA Triton Inference Server enables the deployment of scalable AI models in production, supporting multiple frameworks, concurrent model execution, and high-performance inference across CPUs and GPUs.
NEW QUESTION # 42
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