A company is implementing a new network architecture and needs to consider the requirements and
considerations for training and inference. Which of the following statements is true about training
and inference architecture?
C
Explanation:
Training architectures are designed to maximize computational throughput and accelerate model
convergence, often by leveraging distributed systems with multiple GPUs or specialized accelerators
to process large datasets efficiently. This focus on performance ensures that models can be trained
quickly and effectively. In contrast, inference architectures prioritize minimizing response latency to
deliver real-time or near-real-time predictions, frequently employing techniques such as model
optimization (e.g., pruning, quantization), batching strategies, and deployment on edge devices or
optimized servers. These differing priorities mean that while there may be some overlap, the
architectures are tailored to their specific goals—performance for training and low latency for
inference.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Infrastructure
Considerations for AI Workloads; NVIDIA Documentation on Training and Inference Optimization)
For which workloads is NVIDIA Merlin typically used?
A
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)
Which NVIDIA parallel computing platform and programming model allows developers to program in
popular languages and express parallelism through extensions?
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)
Which of the following aspects have led to an increase in the adoption of AI? (Choose two.)
C, D
Explanation:
The surge in AI adoption is driven by two key enablers: high-powered GPUs and large amounts of
data. High-powered GPUs provide the massive parallel compute capabilities necessary to train
complex AI models, particularly deep neural networks, by processing numerous operations
simultaneously, significantly reducing training times. Simultaneously, the availability of large
datasets—spanning text, images, and other modalities—provides the raw material that modern AI
algorithms, especially data-hungry deep learning models, require to learn patterns and make
accurate predictions. While Moore’s Law (the doubling of transistor counts) has historically aided
computing, its impact has slowed, and rule-based machine learning has largely been supplanted by
data-driven approaches.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on AI Adoption Drivers)
In training and inference architecture requirements, what is the main difference between training
and inference?
B
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.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Training vs. Inference
Requirements)
Which of the following statements is true about GPUs and CPUs?
A
Explanation:
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)
Which two components are included in GPU Operator? (Choose two.)
A, C
Explanation:
The NVIDIA GPU Operator is a tool for automating GPU resource management in Kubernetes
environments. It includes two key components: GPU drivers, which provide the necessary software
to interface with NVIDIA GPUs, and the NVIDIA Data Center GPU Manager (DCGM), which offers
health monitoring, telemetry, and diagnostics for GPU clusters. Frameworks like PyTorch and
TensorFlow are separate AI development tools, not part of the GPU Operator, which focuses on
infrastructure rather than application layers.
(Reference: NVIDIA GPU Operator Documentation, Components Section)
Which phase of deep learning benefits the greatest from a multi-node architecture?
B
Explanation:
Training is the deep learning phase that benefits most from a multi-node architecture. It involves
compute-intensive operations—forward and backward passes, gradient computation, and
synchronization—across large datasets and complex models. Distributing these tasks across multiple
nodes with GPUs accelerates processing, reduces time to convergence, and enables handling models
too large for a single node. While data augmentation and inference can leverage multiple nodes,
their gains are less pronounced, as they typically involve lighter or more localized computation.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Multi-Node Training)
Which architecture is the core concept behind large language models?
C
Explanation:
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)
What is a key value of using NVIDIA NIMs?
A
Explanation:
NVIDIA NIMs (NVIDIA Inference Microservices) are pre-built, GPU-accelerated microservices with
standardized APIs, designed to simplify and accelerate AI model deployment across diverse
environments—clouds, data centers, and edge devices. Their key value lies in enabling fast, turnkey
inference without requiring custom deployment pipelines, reducing setup time and complexity.
While community support and SDK deployment may be tangential benefits, they are not the primary
focus of NIMs.
(Reference: NVIDIA NIMs Documentation, Overview Section)
The foundation of the NVIDIA software stack is the DGX OS. Which of the following Linux
distributions is DGX OS built upon?
A
Explanation:
DGX OS, the operating system powering NVIDIA DGX systems, is built on Ubuntu Linux, specifically
the Long-Term Support (LTS) version. It integrates Ubuntu’s robust base with NVIDIA-specific
enhancements, including GPU drivers, tools, and optimizations tailored for AI and high-performance
computing workloads. Neither Red Hat nor CentOS serves as the foundation for DGX OS, making
Ubuntu the correct choice.
(Reference: NVIDIA DGX OS Documentation, System Requirements Section)
What is the name of NVIDIA’s SDK that accelerates machine learning?
C
Explanation:
The CUDA Deep Neural Network library (cuDNN) is NVIDIA’s SDK specifically designed to accelerate
machine learning, particularly deep learning tasks. It provides highly optimized implementations of
neural network primitives—such as convolutions, pooling, normalization, and activation functions—
leveraging GPU parallelism. Clara focuses on healthcare applications, and RAPIDS accelerates data
science workflows, but cuDNN is the core SDK for machine learning acceleration.
(Reference: NVIDIA cuDNN Documentation, Introduction)
Which aspect of computing uses large amounts of data to train complex neural networks?
B
Explanation:
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)
Which of the following statements correctly differentiates between AI, Machine Learning, and Deep
Learning?
D
Explanation:
Artificial Intelligence (AI) is the overarching field encompassing techniques to mimic human
intelligence. Machine Learning (ML), a subset of AI, involves algorithms that learn from data. Deep
Learning (DL), a specialized subset of ML, uses neural networks with many layers to tackle complex
tasks. This hierarchical relationship—DL within ML, ML within AI—is the correct differentiation,
unlike the reversed or conflated options.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on AI, ML, and DL
Definitions)
How is the architecture different in a GPU versus a CPU?
B
Explanation:
A GPU’s architecture is designed for massive parallelism, featuring thousands of lightweight cores
that execute simple instructions across vast data elements simultaneously—ideal for tasks like AI
training. In contrast, a CPU has fewer, complex cores optimized for sequential execution and
branching logic. GPUs don’t function as PCIe controllers (a hardware role), nor are they single-core
designs, making the parallel execution focus the key differentiator.
(Reference: NVIDIA GPU Architecture Whitepaper, Section on GPU Design Principles)