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| Section | Objectives |
|---|---|
| Topic 1: AI Infrastructure Fundamentals | - AI workload architecture overview - Accelerated computing concepts (GPU vs CPU workloads) |
| Topic 2: System and Cluster Architecture | - DGX / HGX systems overview - Cluster design for AI workloads |
| Topic 3: Performance, Reliability, and Troubleshooting | - Performance tuning for GPU workloads - Common infrastructure failure diagnostics |
| Topic 4: Storage and Data Pipelines | - Data throughput for training workloads - Distributed storage concepts |
| Topic 5: AI Operations and Lifecycle Management | - Model deployment workflows - Monitoring and observability of AI systems |
| Topic 6: Networking for AI Infrastructure | - High-speed interconnects (InfiniBand, Ethernet) - Bandwidth and latency considerations |
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NEW QUESTION # 109
Which NVIDIA platform is designed for Edge deployments?
Answer: B
Explanation:
NVIDIA Jetson is designed for AI at the edge, providing compact, power-efficient platforms for deploying AI applications in robots, drones, IoT devices, and other edge environments.
NEW QUESTION # 110
How does NVSwitch contribute to accelerating network performance in an AI environment?
Answer: A
Explanation:
NVSwitch enables direct, high-speed communication between multiple GPUs within a server or cluster, bypassing the CPU, which reduces latency and significantly accelerates AI training and inference workloads.
NEW QUESTION # 111
Which component of the NVIDIA software stack is primarily responsible for optimizing deep learning models for inference in production environments?
Answer: C
Explanation:
NVIDIA TensorRT is primarily responsible for optimizing deep learning models for inference, enhancing speed and efficiency on GPUs in production. Option A (DIGITS) is for training. Option B (Triton) serves models, leveraging TensorRT. Option D (CUDA) is a foundational platform. NVIDIA's TensorRT docs confirm its inference optimization role.
NEW QUESTION # 112
You are managing an AI infrastructure using NVIDIA GPUs to train large language models for a social media company. During training, you observe that the GPU utilization is significantly lower than expected, leading to longer training times. Which of the following actions is most likely to improve GPU utilization and reduce training time?
Answer: C
Explanation:
Using mixed precision training (A) is most likely to improve GPU utilization and reduce training time. Mixed precision combines FP16 and FP32 computations, leveraging NVIDIA Tensor Cores (e.g., in A100 GPUs) to perform more operations per cycle. This increases throughput, reduces memory usage, and keeps GPUs busier, addressing low utilization. It's widely supported in frameworks like PyTorch and TensorFlow via NVIDIA's Apex or automatic mixed precision (AMP).
* Decreasing model complexity(B) might speed up training but sacrifices accuracy, not addressing utilization directly.
* Increasing batch size(C) can improve utilization but risks memory overflows if too large, and doesn't optimize compute efficiency like mixed precision.
* Reducing learning rate(D) affects convergence, not GPU utilization.
NVIDIA promotes mixed precision for large language models (A).
NEW QUESTION # 113
NVIDIA AI Factories are designed primarily to support which part of the AI/MLOps pipeline?
Answer: A
Explanation:
NVIDIA defines an AI factory as "a specialized computing infrastructure designed to create value from data by managing the entire AI life cycle, from data ingestion to training, fine-tuning, and high-volume AI inference." NVIDIA also says the NVIDIA Enterprise AI Factory is a validated design that provides full-stack guidance for "building and deploying an on-premises AI factory" and that it "simplifies deployment, mitigates risk, and accelerates the path to production AI." This confirms that NVIDIA AI Factories are not just storage expansions, backup systems, or manual test environments. They are designed to support the full AI lifecycle, including data ingestion/preparation, training or fine-tuning, deployment, and production inference.
Reference: NVIDIA AI Factory Glossary; NVIDIA Enterprise AI Factory solution page.
NEW QUESTION # 114
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