NVIDIA NCA-GENL Quiz 1 Topic 1 Questions 1-5

Question: 1

[Python Libraries for LLMs]

Which feature of the HuggingFace Transformers library makes it particularly suitable for fine-tuning large language models on NVIDIA GPUs?

ABuilt-in support for CPU-based data preprocessing pipelines.

BSeamless integration with PyTorch and TensorRT for GPU-accelerated training and inference.

CAutomatic conversion of models to ONNX format for cross-platform deployment.

DSimplified API for classical machine learning algorithms like SVM.

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Question: 2

[LLM Integration and Deployment]

What is Retrieval Augmented Generation (RAG)?

ARAG is an architecture used to optimize the output of an LLM by retraining the model with domain-specific data.

BRAG is a methodology that combines an information retrieval component with a response generator.

CRAG is a method for manipulating and generating text-based data using Transformer-based LLMs.

DRAG is a technique used to fine-tune pre-trained LLMs for improved performance.

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Question: 3

[LLM Integration and Deployment]

What are some methods to overcome limited throughput between CPU and GPU? (Pick the 2 correct responses)

AIncrease the clock speed of the CPU.

BUsing techniques like memory pooling.

CUpgrade the GPU to a higher-end model.

DIncrease the number of CPU cores.

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Question: 4

[Fundamentals of Machine Learning and Neural Networks]

What type of model would you use in emotion classification tasks?

AAuto-encoder model

BSiamese model

CEncoder model

DSVM model

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Question: 5

[Experimentation]

In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?

ASingle hold-out validation with a fixed test set.

BStratified k-fold cross-validation.

CBootstrapping with random sampling.

DGrid search for hyperparameter tuning.

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NVIDIA NCA-GENL Quiz:1 Topic:1 Questions:1-5