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NEW QUESTION # 72
Why use NVIDIA GPUDirect Storage in an AI cluster?
Answer: A
Explanation:
NVIDIA GPUDirect Storage is used to create a direct data path between storage and GPU memory. NVIDIA' s GPUDirect Storage Overview Guide states: "GPUDirect Storage (GDS) enables a direct data path for direct memory access (DMA) transfers between GPU memory and storage, which avoids a bounce buffer through the CPU." It also says this direct path can relieve system bandwidth bottlenecks and reduce CPU latency and utilization load.
NVIDIA's GPUDirect Storage technical blog further explains that GPUDirect Storage enables "a direct data path between local or remote storage, like NVMe or NVMe over Fabric (NVMe-oF), and GPU memory." Therefore, the correct answer is C: it enables peer-to-peer memory transfers between GPUs and NVMe storage.
Why the other options are incorrect: GPUDirect Storage is not primarily about TCP/IP transmission between GPUs and CPUs. It does simplify and bypass parts of the traditional CPU-centric storage path, but the more precise answer is direct GPU-memory-to-storage transfer. It does not increase GPU clock rates.
Reference: NVIDIA GPUDirect Storage Overview Guide; NVIDIA Developer Blog - A Direct Path Between Storage and GPU Memory.
NEW QUESTION # 73
Which NVIDIA parallel computing platform and programming model allows developers to program in popular languages and express parallelism through extensions?
Answer: A
Explanation:
CUDA (Compute Unified Device Architecture) is NVIDIA's foundational parallel computing platform and programming model. It enables developers to harness GPU parallelism by extending popular languages such as C, C++, and Fortran with parallelism-specific constructs (e.g., kernel launches, thread management).
CUDA also provides bindings for languages like Python (via libraries like PyCUDA), making it versatile for a wide range of developers. In contrast, CUML and CUGRAPH are higher-level libraries built on CUDA for specific machine learning and graph analytics tasks, not general-purpose programming models.
(Reference: NVIDIA CUDA Programming Guide, Introduction)
NEW QUESTION # 74
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: B
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 # 75
A retail company wants to implement an AI-based system to predict customer behavior and personalize product recommendations across its online platform. The system needs to analyze vast amounts of customer data, including browsing history, purchase patterns, and social media interactions. Which approach would be the most effective for achieving these goals?
Answer: B
Explanation:
Deploying a deep learning model that uses a neural network with multiple layers for feature extraction and prediction is the most effective approach for predicting customer behavior and personalizing recommendations in retail. Deep learning excels at processing large, complex datasets (e.g., browsing history, purchase patterns, social media interactions) by automatically extracting features through multiple layers, enabling accurate predictions and personalized outputs. NVIDIA GPUs, such as those in DGX systems, accelerate these models, and tools like NVIDIA Triton Inference Server deploy them for real-time recommendations, as highlighted in NVIDIA's "State of AI in Retail and CPG" report and "AI Infrastructure for Enterprise" documentation.
Unsupervised learning (A) clusters data but lacks predictive power for recommendations. Rule-based systems (B) are rigid and cannot adapt to complex patterns. Linear regression (C) oversimplifies the problem, missing nuanced interactions. Deep learning, supported by NVIDIA's AI ecosystem, is the industry standard for this use case.
NEW QUESTION # 76
A warehousing company wants to improve its efficiency by bringing automation to its warehouses. They're considering maintaining warehouse robots, which can learn on the go regarding new or re-assigned stocking in the warehouses. They are requesting NVIDIA support on this new venture and they're wondering which AI stacks can meet this need. What is the appropriate platform for this customer's use case?
Answer: C
Explanation:
NVIDIA Isaac is designed for AI-powered robotics applications, including warehouse automation, allowing robots to learn and adapt to new tasks, navigate environments, and interact with objects efficiently.
NEW QUESTION # 77
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