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NVIDIA Generative AI LLMs Sample Questions (Q49-Q54):

NEW QUESTION # 49
What is a Tokenizer in Large Language Models (LLM)?

• A. A tool used to split text into smaller units called tokens for analysis and processing.
• B. A technique used to convert text data into numerical representations called tokens for machine learning.
• C. A method to remove stop words and punctuation marks from text data.
• D. A machine learning algorithm that predicts the next word/token in a sequence of text.

Answer: A

Explanation:
A tokenizer in the context of large language models (LLMs) is a tool that splits text into smaller units called tokens (e.g., words, subwords, or characters) for processing by the model. NVIDIA's NeMo documentation on NLP preprocessing explains that tokenization is a critical step in preparing text data, with algorithms like WordPiece, Byte-Pair Encoding (BPE), or SentencePiece breaking text into manageable units to handle vocabulary constraints and out-of-vocabulary words. For example, the sentence "I love AI" might be tokenized into ["I", "love", "AI"] or subword units like ["I", "lov", "##e", "AI"]. Option A is incorrect, as removing stop words is a separate preprocessing step. Option B is wrong, as tokenization is not a predictive algorithm. Option D is misleading, as converting text to numerical representations is the role of embeddings, not tokenization.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

NEW QUESTION # 50
When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?

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

Answer: D

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