1Z0-1127-25 LLM Fundamentals Practice Question
Which tokenization algorithm is commonly used by models like GPT and BERT, and works by merging frequently occurring character pairs 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) starts with characters and merges the most frequent pairs to create subword units. WordPiece uses a similar likelihood-based approach. SentencePiece is a framework that can use BPE or unigram.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Morpheme-based tokenization
Why it's wrong here
Morpheme-based tokenization uses linguistic units, not frequency-based merging.
- ✓
Byte-Pair Encoding (BPE)
Why this is correct
BPE iteratively merges the most frequent character pairs to build a vocabulary of subword tokens.
- ✗
SentencePiece
Why it's wrong here
SentencePiece is a framework that can implement BPE or unigram tokenization; it is not the specific algorithm.
- ✗
WordPiece
Why it's wrong here
WordPiece merges based on likelihood, not frequency of pairs.
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