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| Section | Weight | Objectives |
|---|---|---|
| Essential AI Knowledge | 38% | - Accelerated computing use cases and industry applications - Purpose and benefits of DPU in data center - AI, Machine Learning, and Deep Learning concepts and differences - GPU vs CPU architecture and characteristics - NVIDIA software stack and components in AI environment |
| AI Operations | 22% | - Operational best practices for NVIDIA solutions - Scaling and maintenance considerations - AI infrastructure monitoring and management basics - Cluster orchestration and job scheduling concepts |
| AI Infrastructure | 40% | - On-premises vs cloud infrastructure comparison - Hardware requirements for training and inference workloads - Data center power, cooling and physical requirements - GPU cluster design and configuration principles - AI networking fundamentals and considerations |
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NEW QUESTION # 88
Which NVIDIA technology provides the broadest ecosystem for parallel computation across languages?
Answer: D
Explanation:
CUDA is the correct answer because it is NVIDIA's core parallel computing platform and programming model. NVIDIA's CUDA Programming Guide states: "CUDA is a parallel computing platform and programming model developed by NVIDIA that enables dramatic increases in computing performance by harnessing the power of the GPU." It also says CUDA is widely used in "deep learning, scientific computing, and high-performance computing (HPC)." NVIDIA's CUDA platform page further confirms the "across languages" part of the question: "Developers can program in languages such as C++, Python, and Fortran or leverage GPU-accelerated libraries and frameworks like PyTorch." Why the other options are incorrect: cuGraph is a GPU-accelerated graph analytics library, not the broad parallel-computing ecosystem. OpenCL is an open standard, but it is not NVIDIA's broadest software ecosystem. Triton Inference Server is for serving and managing AI inference workloads, not general-purpose parallel computation.
Reference: NVIDIA CUDA Programming Guide; NVIDIA CUDA Platform for Accelerated Computing.
NEW QUESTION # 89
A research team is deploying a deep learning model on an NVIDIA DGX A100 system. The model has high computational demands and requires efficient use of all available GPUs. During the deployment, they notice that the GPUs are underutilized, and the inter-GPU communication seems to be a bottleneck. The software stack includes TensorFlow, CUDA, NCCL, and cuDNN. Which of the following actions would most likely optimize the inter-GPU communication and improve overall GPU utilization?
Answer: A
Explanation:
Ensuring NVIDIA Collective Communications Library (NCCL) is configured correctly for optimal bandwidth utilization is the most effective action to optimize inter-GPU communication and improve utilization on an NVIDIA DGX A100. NCCL accelerates multi-GPU operations by optimizing data transfers (e.g., via NVLink, InfiniBand), critical for high-demand models. Underutilization and bottlenecks suggest suboptimal NCCL settings (e.g., topology, ring order). Option A (disable cuDNN) hampers performance, as cuDNN accelerates neural network primitives. Option B (more data parallel jobs) may worsen communication overhead. Option D (single GPU) reduces scalability. NVIDIA's DGX A100 documentation recommends NCCL tuning for distributed training efficiency.
NEW QUESTION # 90
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: D,E
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 # 91
Which characteristic best describes inference when comparing inference to deep learning training?
Answer: A
Explanation:
Inference requires smaller amounts of hardware because it only needs to run the already trained model to make predictions, unlike training, which demands extensive computation and memory resources.
NEW QUESTION # 92
Why use NVIDIA GPUDirect Storage in an AI cluster?
Answer: A
Explanation:
GPUDirect Storage allows GPUs to directly access NVMe storage without CPU intervention, enabling high-throughput, low-latency data transfers that bypass traditional CPU-centric I/O paths and accelerate data-intensive AI workloads.
NEW QUESTION # 93
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