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NVIDIA NCA-GENL Exam Syllabus Topics:

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NVIDIA Generative AI LLMs Sample Questions (Q86-Q91):

NEW QUESTION # 86
What is Retrieval Augmented Generation (RAG)?

• A. RAG is a technique used to fine-tune pre-trained LLMs for improved performance.
• B. RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.
• C. RAG is a methodology that combines an information retrieval component with a response generator.
• D. RAG is an architecture used to optimize the output of an LLM by retraining the model with domain- specific data.

Answer: C

Explanation:
Retrieval-Augmented Generation (RAG) is a methodology that enhances the performance of large language models (LLMs) by integrating an information retrieval component with a generative model. As described in the seminal paper by Lewis et al. (2020), RAG retrieves relevant documents from an external knowledge base (e.g., using dense vector representations) and uses them to inform the generative process, enabling more accurate and contextually relevant responses. NVIDIA's documentation on generative AI workflows, particularly in the context of NeMo and Triton Inference Server, highlights RAG as a technique to improve LLM outputs by grounding them in external data, especially for tasks requiring factual accuracy or domain- specific knowledge. OptionA is incorrect because RAG does not involve retraining the model but rather augments it with retrieved data. Option C is too vague and does not capture the retrieval aspect, while Option D refers to fine-tuning, which is a separate process.
References:
Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

NEW QUESTION # 87
Which of the following is an activation function used in neural networks?

• A. K-means clustering function
• B. Mean Squared Error function
• C. Diffusion function
• D. Sigmoid function

Answer: D

Explanation:
The sigmoid function is a widely used activation function in neural networks, as covered in NVIDIA's Generative AI and LLMs course. It maps input values to a range between 0 and 1, making it particularly useful for binary classification tasks and as a non-linear activation in early neural network architectures. The sigmoid function, defined as f(x) = 1 / (1 + e