1Z0-1127-25 LLM Fundamentals Practice Question
An organization needs to select a tokenisation algorithm for a multilingual LLM that will process English, Chinese, and Korean text efficiently. Which tokenisation method is BEST suited for this requirement?
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
✓
SentencePiece
SentencePiece is language-agnostic, works directly on raw text without requiring pre-tokenisation (e.g., whitespace splitting), and handles languages like Chinese and Korean where word boundaries are not obvious. BPE and WordPiece typically require pre-tokenisation, making them less suitable for CJK languages.
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 also relies on pre-tokenisation, making it suboptimal for languages without explicit word boundaries.
- ✗
Byte-Pair Encoding (BPE)
Why it's wrong here
BPE typically requires pre-tokenisation (e.g., splitting on whitespace), which is problematic for Chinese and Korean where spaces are not used between words.
- ✓
SentencePiece
Why this is correct
SentencePiece treats the input as a raw byte stream and does not require pre-tokenisation, making it ideal for multilingual corpora including CJK languages.
- ✗
Character-level tokenisation
Why it's wrong here
Character-level tokenisation produces very long sequences, increasing computational cost and reducing efficiency for long texts.
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