NVIDIA Updated NCA-AIIO Exam Questions and Answers by dalia

The Transformer model is the foundational architecture for modern large language models (LLMs). Introduced in the paper "Attention is All You Need," it uses stacked layers of self-attention mechanisms and feed-forward networks, often in encoder-decoder or decoder-only configurations, to efficiently capture long-range dependencies in text. While BERT (a specific Transformer-based model) and attention mechanisms (a component of Transformers) are related, the Transformer itself is the core concept. State space models are an alternative approach, not the primary basis for LLMs.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Large Language Models)

Deep learning, a subset of machine learning, relies on large datasets to train multi-layered neural networks, enabling them to learn hierarchical feature representations and complex patterns autonomously. While machine learning encompasses broader techniques (some requiring less data), deep learning’s dependence on vast data volumes distinguishes it. Inferencing, the application of trained models, typically uses smaller, real-time inputs rather than extensive training data.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Deep Learning Fundamentals)

The DGX H100 system uses high-speed NVIDIA ConnectX-7 QSFP56 ports (supporting 10 GbE and above) for in-band management and storage traffic, with no 1 Gb Ethernet interfaces allocated to in-band networks. A single 1 GbE RJ45 port exists, but it’s reserved for out-of-band Baseboard Management Controller (BMC) tasks, not in-band connectivity.

(Reference: NVIDIA DGX H100 System Documentation, Networking Section)

GPUs and CPUs are architecturally distinct due to their optimization goals. GPUs feature thousands of simpler cores designed for massive parallelism, excelling at executing many lightweight threads concurrently—ideal for tasks like matrix operations in AI. CPUs, conversely, have fewer, more complex cores optimized for sequential processing and handling intricate control flows, making them suited for serial tasks. This divergence in design means GPUs outperform CPUs in parallel workloads, while CPUs excel in single-threaded performance, contradicting claims of identical architectures or interchangeable use.

(Reference: NVIDIA GPU Architecture Whitepaper, Section on GPU vs. CPU Design)