Tokenization and vocabulary management: Practice Questions — Data Preparation (NVIDIA-Certified Professional: Generative AI LLMs)

Practice Questions on Tokenization and Vocabulary Management

These multiple-choice questions are designed to help candidates prepare for the NVIDIA-Certified Professional: Generative AI LLMs exam, focusing specifically on tokenization and vocabulary management within data preparation.

1. Which of the following best describes the purpose of tokenization in large language model training?

   • A. To convert raw text into a sequence of numerical vectors representing semantic meaning
   • B. To split raw text into smaller units such as words or subwords for model input
   • C. To remove stop words and punctuation from the dataset
   • D. To compress the dataset size by removing redundant sentences

   Correct Answer: B

   Explanation: Tokenization involves splitting raw text into smaller units (tokens), such as words or subwords, which can then be processed by the model. It is a fundamental step before converting tokens into numerical representations.

2. What is a key advantage of using subword tokenization methods like Byte Pair Encoding (BPE) over word-level tokenization?

   • A. It reduces the vocabulary size while effectively handling rare or unknown words
   • B. It eliminates the need for vocabulary management
   • C. It guarantees tokens correspond exactly to dictionary words
   • D. It increases the number of tokens per sentence to improve model accuracy

   Correct Answer: A

   Explanation: Subword tokenization like BPE breaks words into smaller units, allowing the model to represent rare or unseen words by combining known subwords, which reduces vocabulary size and improves generalization.

3. In vocabulary management, what is the primary reason for limiting the vocabulary size in a large language model?

   • A. To increase the model's ability to memorize training data
   • B. To reduce computational complexity and memory usage during training and inference
   • C. To ensure every possible word in the language is included
   • D. To avoid the need for tokenization

   Correct Answer: B

   Explanation: Limiting vocabulary size helps control the model's computational and memory requirements, making training and inference more efficient while balancing coverage of language tokens.

4. Which tokenization challenge is addressed by using a special token such as <UNK> in vocabulary management?

   • A. Representing unknown or out-of-vocabulary words during inference
   • B. Marking the beginning of a sentence
   • C. Separating punctuation from words
   • D. Encoding whitespace characters

   Correct Answer: A

   Explanation: The <UNK> token is used to represent words not present in the vocabulary, allowing the model to handle out-of-vocabulary inputs gracefully.

5. Which of the following is a common step when creating a vocabulary for a generative LLM?

   • A. Including all possible Unicode characters as tokens
   • B. Selecting tokens based on frequency thresholds in the training corpus
   • C. Randomly sampling tokens from the dataset
   • D. Using only whole words as tokens

   Correct Answer: B

   Explanation: Vocabulary is typically constructed by selecting tokens that meet frequency thresholds to balance vocabulary size and coverage, ensuring common tokens are included while rare tokens may be split or represented as subwords.

6. What is the main role of a tokenizer’s vocabulary file in the context of LLM training?

   • A. To store the model’s learned parameters
   • B. To map tokens to unique integer IDs for embedding lookup
   • C. To store raw training data
   • D. To encode the model architecture

   Correct Answer: B

   Explanation: The vocabulary file maps each token to a unique integer ID, which is essential for converting token sequences into numerical inputs for embedding layers during training and inference.

7. Which tokenization approach helps preserve semantic meaning by considering context during token splitting?

   • A. Rule-based tokenization
   • B. Byte Pair Encoding (BPE)
   • C. WordPiece tokenization
   • D. Contextual tokenization using neural tokenizers

   Correct Answer: D

   Explanation: Contextual tokenization uses neural models that consider surrounding context to decide token boundaries, improving semantic preservation over static methods like BPE or WordPiece.

8. Why is it important to handle special tokens such as <PAD>, <CLS>, and <SEP> in vocabulary management?

   • A. They are used to optimize GPU memory allocation
   • B. They provide structural cues to the model for sequence processing
   • C. They reduce the size of the training dataset
   • D. They replace rare tokens in the vocabulary

   Correct Answer: B

   Explanation: Special tokens like <PAD> (padding), <CLS> (classification start), and <SEP> (separator) help the model understand sequence boundaries and structure, which is critical for tasks such as classification and sequence generation.

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