1Z0-1127-25 LLM Fundamentals Practice Question
Which tokenization algorithm is commonly used in models like GPT and BERT and builds tokens by merging the most frequent pairs of characters or subwords iteratively?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Byte-Pair Encoding (BPE)
Byte-Pair Encoding (BPE) is a subword tokenization method that starts with individual characters and merges the most frequent pairs iteratively until a vocabulary size is reached.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
WordPiece
Why it's wrong here
WordPiece is similar to BPE but uses a different merging criterion based on likelihood; it is used in BERT.
- ✗
SentencePiece
Why it's wrong here
SentencePiece is a tokenizer that can be used with BPE or unigram models; it is not an algorithm itself.
- ✗
Unigram tokenization
Why it's wrong here
Unigram tokenization is based on a language model and removes tokens; it is not the iterative merging algorithm described.
- ✓
Byte-Pair Encoding (BPE)
Why this is correct
BPE is the algorithm that iteratively merges the most frequent byte pairs to build a subword vocabulary.
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