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| Section | Objectives |
|---|---|
| Topic 1: Storage and Data Pipelines | - Distributed storage concepts - Data throughput for training workloads |
| Topic 2: Networking for AI Infrastructure | - High-speed interconnects (InfiniBand, Ethernet) - Bandwidth and latency considerations |
| Topic 3: System and Cluster Architecture | - DGX / HGX systems overview - Cluster design for AI workloads |
| Topic 4: Performance, Reliability, and Troubleshooting | - Performance tuning for GPU workloads - Common infrastructure failure diagnostics |
| Topic 5: AI Operations and Lifecycle Management | - Model deployment workflows - Monitoring and observability of AI systems |
| Topic 6: AI Infrastructure Fundamentals | - Accelerated computing concepts (GPU vs CPU workloads) - AI workload architecture overview |
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NEW QUESTION # 104
What is the primary command for checking the GPU utilization on a single DGX H100 system?
Answer: A
NEW QUESTION # 105
Which architecture, training or inference, requires more data storage?
Answer: C
Explanation:
Training architecture generally requires more data storage because training uses large datasets, repeated data access, and checkpointing. NVIDIA DGX SuperPOD storage architecture documentation explains that storage performance requirements vary by model and dataset, and it specifically highlights training workloads, datasets, and checkpoint files as important storage design considerations. For large model use cases, NVIDIA notes that "peak performance for reads and writes are needed for creating and reading checkpoint files" and that training stops during checkpoint operations. NVIDIA also states that storage solutions for AI factories must support "large-scale AI training, fine-tuning, inference, KV cache, and retrieval-augmented generation," showing that storage is relevant across the lifecycle but especially demanding for training and fine-tuning.
Inference architecture typically stores the deployed model, runtime components, and sometimes cache or retrieval data. Training architecture must store training datasets, intermediate outputs, logs, and checkpoints, so it generally has the greater storage requirement.
Reference: NVIDIA DGX SuperPOD Storage Architecture; NVIDIA-Certified Storage documentation.
NEW QUESTION # 106
You are managing an AI data center where energy consumption has become a critical concern due to rising costs and sustainability goals. The data center supports various AI workloads, including model training, inference, and data preprocessing. Which strategy would most effectively reduce energy consumption without significantly impacting performance?
Answer: B
Explanation:
Dynamic Voltage and Frequency Scaling (DVFS) allows GPUs to adjust their power usage dynamically based on workload intensity, reducing energy consumption during low-demand periods while maintaining performance when needed. NVIDIA GPUs, such as those in DGX systems, support DVFS through tools like NVIDIA Management Library (NVML) and nvidia-smi, enabling fine-tuned power management. This approach balances efficiency and performance, critical for diverse AI workloads like training (high compute) and inference (variable demand), aligning with NVIDIA's energy-efficient computing initiatives.
Consolidating workloads onto a single GPU (Option A) risks overloading it, degrading performance and negating energy savings due to inefficiency. Scheduling workloads at night (Option C) addresses cost but not total consumption or sustainability, and it may delay time-sensitive tasks. Reducing clock speed universally (Option D) lowers power use but sacrifices performance across all workloads, which is impractical for an AI data center. DVFS is the most effective NVIDIA-supported strategy here.
NEW QUESTION # 107
For which workloads is NVIDIA Merlin typically used?
Answer: C
Explanation:
NVIDIA Merlin is a specialized, end-to-end framework engineered for building and deploying large-scale recommender systems. It streamlines the entire pipeline, including data preprocessing (e.g., feature engineering, data transformation), model training (using GPU-accelerated frameworks), and inference optimizations tailored for recommendation tasks. Unlike general-purpose tools for natural language processing or data analytics, Merlin is optimized to handle the unique challenges of recommendation workloads, such as processing massive user-item interaction datasets and delivering personalized results efficiently.
(Reference: NVIDIA Merlin Documentation, Overview Section)
NEW QUESTION # 108
A healthcare company is training a large convolutional neural network (CNN) for medical image analysis.
The dataset is enormous, and training is taking longer than expected. The team needs to speed up the training process by distributing the workload across multiple GPUs and nodes. Which of the following NVIDIA solutions will help them achieve optimal performance?
Answer: D
Explanation:
Training a large CNN on an enormous dataset across multiple GPUs and nodes requires efficient communication and data handling. NVIDIA NCCL (NVIDIA Collective Communications Library) optimizes inter-GPU and inter-node communication, enabling scalable data and model parallelism, while NVIDIA DALI (Data Loading Library) accelerates data loading and preprocessing on GPUs, reducing I/O bottlenecks.
Together, they speed up training by ensuring GPUs are fully utilized, a strategy central to NVIDIA's DGX systems and multi-node AI workloads.
cuDNN (Option A) accelerates CNN operations but focuses on single-GPU performance, not multi-node distribution. DeepStream SDK (Option C) is tailored for real-time video analytics, not training. TensorRT (Option D) optimizes inference, not training. NCCL and DALI are the optimal NVIDIA solutions for this distributed training scenario.
NEW QUESTION # 109
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