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NEW QUESTION # 27
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 # 28
You are tasked with transforming a traditional data center into an AI-optimized data center using NVIDIA DPUs (Data Processing Units). One of your goals is to offload network and storage processing tasks from the CPU to the DPU to enhance performance and reduce latency. Which scenario best illustrates the advantage of using DPUs in this transformation?
Answer: D
Explanation:
Using DPUs to handle network traffic encryption and decryption, freeing up CPU resources for AI workloads, best illustrates the advantage of NVIDIA DPUs (e.g., BlueField) in an AI-optimizeddata center. DPUs are specialized processors designed to offload networking, storage, and security tasks (e.g., encryption, RDMA) from CPUs, reducing latency and improving overall system performance. This allows CPUs and GPUs to focus on compute-intensive AI tasks like training and inference, as outlined in NVIDIA's "BlueField DPU Documentation" and "AI Infrastructure for Enterprise" resources.
Offloading training to DPUs (B) is incorrect, as DPUs are not designed for AI computation. Parallel preprocessing with CPUs (C) misaligns with DPU capabilities. GPU memory management (D) remains a GPU function, not a DPU task. NVIDIA emphasizes DPUs for network/storage offload, making (A) the best scenario.
NEW QUESTION # 29
In training and inference architecture requirements, what is the main difference between training and inference?
Answer: A
Explanation:
The primary distinction between training and inference lies in their operational demands. Training necessitates large amounts of data to iteratively optimize model parameters, often involving extensive datasets processed in batches across multiple GPUs to achieve convergence.
Inference, however, is designed for real-time or low-latency processing, where trained models are deployed to make predictions on new inputs with minimal delay, typically requiring less data volume but high responsiveness. This fundamental difference shapes their respective architectural designs and resource allocations.
NEW QUESTION # 30
You are leading a project to implement a real-time fraud detection system for a financial institution. The system needs to analyze transactions in real-time using a deep learning model that has been trained on large datasets. The inference workload must be highly scalable and capable of processing thousands of transactions per second with minimal latency. Your deployment environment includes NVIDIA A100 GPUs in a Kubernetes-managed cluster. Which approach would be most suitable to deploy and manage your deep learning inference workload?
Answer: D
Explanation:
NVIDIA Triton Inference Server with Kubernetes is the most suitable approach for deploying and managing a real-time fraud detection system on NVIDIA A100 GPUs. Triton provides a scalable, low-latency inference platform with features like dynamic batching and model management, ideal for processing thousands of transactions per second. Integration with Kubernetes (via NVIDIA GPU Operator) ensures high availability, scalability, and orchestration in a cluster, as outlined in NVIDIA's "Triton Inference Server Documentation" and "DeepOps" resources. This meets the financial institution's needs for real-time, high-throughput inference.
TensorRT standalone (A) optimizes models but lacks deployment scalability. Kafka with GPUs (C) is a messaging system, not an inference solution. CUDA with Docker (D) is a development tool, not a production deployment platform. Triton with Kubernetes is NVIDIA's recommended approach.
NEW QUESTION # 31
What is the maximum number of GPU instances you can partition with NVIDIA MIG (Multi- Instance GPU)?
Answer: C
NEW QUESTION # 32
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