Which architecture is the core concept behind large language models?
Answer(s): C
The Transformer model underpins modern large language models, using self-attention layers and feed-forward networks in a stacked, encoder-decoder (or decoder-only) architecture to capture long-range dependencies efficiently.
What is a key value of using NVIDIA NIMs?
Answer(s): A
NVIDIA NIMs are packaged, GPU-accelerated inference microservices with industry-standard APIs that simplify and accelerate deploying AI models across clouds, data centers, and edge - enabling fast, turnkey AI inference without hand-crafting deployment pipelines.
The foundation of the NVIDIA software stack is the DGX OS.Which of the following Linux distributions is DGX OS built upon?
DGX OS is a tailored installation of Ubuntu Linux, incorporating Ubuntu LTS as its base and adding NVIDIA’s drivers, tooling, and optimizations on top.
What is the name of NVIDIA’s SDK that accelerates machine learning?
Answer(s): B
Which aspect of computing uses large amounts of data to train complex neural networks?
Deep learning leverages vast datasets to train multi-layer neural networks, enabling the models to automatically learn hierarchical feature representations and complex patterns directly from the data.
Which of the following statements correctly differentiates between AI, Machine Learning, and Deep Learning?
Answer(s): D
Deep Learning sits within Machine Learning as its most specialized class of algorithms, and Machine Learning itself is a subset of the broader field of Artificial Intelligence.
How is the architecture different in a GPU versus a CPU?
A GPU’s design centers on many lightweight cores optimized for executing the same simple instruction across thousands of data elements in parallel, whereas a CPU uses a few powerful cores tailored for sequential and branching tasks.
What factors have led to significant breakthroughs in Deep Learning?
Breakthroughs in deep learning have been driven by more powerful hardware (e.g., GPUs and TPUs), the emergence of massive curated datasets for training, and continual innovations in training algorithms (optimizers, architectures, regularization) that make it possible to effectively learn deep neural networks.
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good exam questions
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question 46, the answer should be data "virtualization" (not visualization).
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Pass this exam 3 days ago. The PDF version and the Xengine App is quite useful.
informative for me.
question 134s answer shoule be "dlp"
in 72 the answer must be [sys_user_has_role] table.
i appreciated the mix of multiple-choice and short answer questions. i passed my exam this morning.
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