NVIDIA NCA-GENL Exam - Topic 5 Question 10 Discussion

Actual exam question for NVIDIA's NCA-GENL exam

Question #: 10
Topic #: 5

[All NCA-GENL Questions]

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

AA method to remove stop words and punctuation marks from text data.

BA machine learning algorithm that predicts the next word/token in a sequence of text.

CA tool used to split text into smaller units called tokens for analysis and processing.

DA technique used to convert text data into numerical representations called tokens for machine learning.

Show Suggested Answer

Suggested Answer: C

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.

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

by Lucy at Aug 17, 2025, 01:45 AM

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Adelle

5 months ago

Totally with you on C! Makes the most sense.

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Katheryn

5 months ago

I thought it was more about converting text to numbers?

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Wava

6 months ago

Definitely C! That's how LLMs understand text.

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Carey

6 months ago

Wait, so it’s not just about removing punctuation?

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Dominga

6 months ago

A tokenizer breaks text into tokens for easier processing.

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Norah

6 months ago

I feel like I might confuse tokenization with other preprocessing steps, but I think it’s more about splitting text than removing stop words.

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Luke

6 months ago

I practiced a question similar to this, and I think tokenization is definitely about creating tokens, which makes me lean towards option C.

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Jerry

7 months ago

I remember something about tokenization being crucial for LLMs, but I can't recall if it's just for splitting text or if it also involves numerical conversion.

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Christene

7 months ago

I think a tokenizer is about breaking text into smaller parts, so maybe option C? But I’m not entirely sure.

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Leonard

7 months ago

From what I remember, a Tokenizer is a tool used to split text into smaller units called tokens for analysis and processing. That sounds like the best description, so I'll go with option C.

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Dortha

7 months ago

A Tokenizer is a machine learning algorithm that predicts the next word/token in a sequence of text. I'm confident that's the correct answer, which is option B.

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Sage

7 months ago

Hmm, I'm a bit confused on this one. Is a Tokenizer a method to remove stop words and punctuation marks from text data? Or is it a technique to convert text into numerical representations for machine learning? I'll have to think this through carefully.

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Lauran

8 months ago

I'm pretty sure a Tokenizer is a tool used to split text into smaller units called tokens for analysis and processing. That sounds like option C to me.

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