Exam NCA-GENL Topic 5 Question 39 Discussion

Actual exam question for NVIDIA's NCA-GENL exam
Question #: 39
Topic #: 5

What is a Tokenizer in Large Language Models (LLM)?

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

Suggested Answer: C Vote an answer

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

by Liz at Jun 07, 2025, 06:05 PM

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