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NVIDIA Generative AI LLMs Sample Questions (Q70-Q75):

NEW QUESTION # 70
You are working with a data scientist on a project that involves analyzing and processing textual data to extract meaningful insights and patterns. There is not much time for experimentation and you need to choose a Python package for efficient text analysis and manipulation. Which Python package is best suited for the task?

• A. spaCy
• B. Matplotlib
• C. NumPy
• D. Pandas

Answer: A

Explanation:
For efficient text analysis and manipulation in NLP projects, spaCy is the most suitable Python package, as emphasized in NVIDIA's Generative AI and LLMs course. spaCy is a high-performance library designed specifically for NLP tasks, offering robust tools for tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and word vector generation. Its efficiency and pre-trained models make it ideal for extracting meaningful insights from text under time constraints. Option A, NumPy, is incorrect, as it is designed for numerical computations, not text processing. Option C, Pandas, is useful for tabular data manipulation but lacks specialized NLP capabilities. Option D, Matplotlib, is for data visualization, not text analysis. The course highlights: "spaCy is a powerful Python library for efficient text analysis and manipulation, providing tools for tokenization, entity recognition, and other NLP tasks, making it ideal for processing textual data." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 71
When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?

• A. ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.
• B. ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.
• C. ReLU and sigmoid both have a range of 0 to 1.
• D. ReLU is a linear function while sigmoid is non-linear.

Answer: B

Explanation:
ReLU (Rectified Linear Unit) and sigmoid are activation functions used in neural networks. According to NVIDIA's deep learning documentation (e.g., cuDNN and TensorRT), ReLU, defined as f(x) = max(0, x), is computationally efficient because it involves simple thresholding, avoiding expensive exponential calculations required by sigmoid, f(x) = 1/(1 + e

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