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NVIDIA Generative AI LLMs Sample Questions:

1. What is the fundamental role of LangChain in an LLM workflow?

A) To act as a replacement for traditional programming languages.
B) To reduce the size of AI foundation models.
C) To orchestrate LLM components into complex workflows.
D) To directly manage the hardware resources used by LLMs.

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

A) Automatic conversion of models to ONNX format for cross-platform deployment.
B) Built-in support for CPU-based data preprocessing pipelines.
C) Simplified API for classical machine learning algorithms like SVM.
D) Seamless integration with PyTorch and TensorRT for GPU-accelerated training and inference.

3. In the context of a natural language processing (NLP) application, which approach is most effectivefor implementing zero-shot learning to classify text data into categories that were not seen during training?

A) Use rule-based systems to manually define the characteristics of each category.
B) Train the new model from scratch for each new category encountered.
C) Use a pre-trained language model with semantic embeddings.
D) Use a large, labeled dataset for each possible category.

4. Which metric is commonly used to evaluate machine-translation models?

A) Perplexity
B) BLEU score
C) ROUGE score
D) F1 Score

5. Which model deployment framework is used to deploy an NLP project, especially for high-performance inference in production environments?

A) HuggingFace
B) NVIDIA Triton
C) NVIDIA DeepStream
D) NeMo

Solutions: