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NEW QUESTION # 102
Your team is building an AI-powered application that requires the deployment of multiple models, each trained using different frameworks (e.g., TensorFlow, PyTorch, and ONNX). You need a deployment solution that can efficiently serve all these models in production, regardless of the framework they were built in.
Which software component should you choose?
Answer: D
Explanation:
NVIDIA Triton Inference Server is the best choice for deploying multiple models from different frameworks (TensorFlow, PyTorch, ONNX) in production. Triton provides a unified platform for serving models, supporting diverse frameworks with high performance on NVIDIA GPUs via features like dynamic batching and multi-model management. Option A (Clara Deploy SDK) is healthcare-specific. Option B (TensorRT) optimizes inference but isn't a full serving solution. Option C (DeepOps) aids deployment automation, not model serving. NVIDIA's Triton documentation emphasizes its versatility and efficiency for production inference across frameworks.
NEW QUESTION # 103
What is the primary command for checking the GPU utilization on a single DGX H100 system?
Answer: B
NEW QUESTION # 104
What is a common tool for container orchestration in AI clusters?
Answer: C
Explanation:
Kubernetes is the industry-standard tool for container orchestration in AI clusters, automating deployment, scaling, and management of containerized workloads. Slurm manages job scheduling, Apptainer (formerly Singularity) runs containers, and MLOps is a practice, not a tool, making Kubernetes the clear leader in this domain.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Container Orchestration)
NEW QUESTION # 105
In a distributed AI training environment, you notice that the GPU utilization drops significantly when the model reaches the backpropagation stage, leading to increased training time. What is the most effective way to address this issue?
Answer: B
Explanation:
Implementing mixed-precision training (D) is the most effective way to address low GPU utilization during backpropagation. Mixed precision uses FP16 alongside FP32, leveraging NVIDIA Tensor Cores to accelerate matrix operations in backpropagation, reducing compute time and memory usage. This keeps GPUs busier by increasing throughput, especially in distributed setups where synchronization waits can exacerbate idling.
* More layers(A) increases compute but may not target backpropagation efficiency and risks overfitting.
* Higher learning rate(B) affects convergence, not utilization directly.
* Data pipeline optimization(C) helps forward passes but not backpropagation compute bottlenecks.
NVIDIA's mixed precision is a proven solution for training efficiency (D).
NEW QUESTION # 106
An AI research team is working on a large-scale natural language processing (NLP) model that requires both data preprocessing and training across multiple GPUs. They need to ensure that the GPUs are used efficiently to minimize training time. Which combination of NVIDIA technologies should they use?
Answer: B
Explanation:
NVIDIA DALI (Data Loading Library) and NVIDIA NCCL (Collective Communications Library) are the best combination for efficient GPU use in NLP model training. DALI accelerates data preprocessing (e.g., tokenization) on GPUs, reducing CPU bottlenecks, while NCCL optimizes inter-GPU communication for distributed training, minimizing latency and maximizing utilization. Option A (TensorRT) focuses on inference, not training. Option B (DeepStream) targets video analytics. Option D (cuDNN, NGC) supports neural ops and model access but lacks preprocessing/communication focus. NVIDIA's NLP workflows recommend DALI and NCCL for efficiency.
NEW QUESTION # 107
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