- A
Common tokenization methods include word-based and subword-based.
Correct. Common tokenization methods include word-based (splitting into whole words) and subword-based (e.g., Byte-Pair Encoding, WordPiece). Subword tokenization is widely used in modern LLMs.
- B
All tokens have the same embedding size.
Why wrong: Option B is incorrect. While all tokens in a given large language model are mapped to embeddings of the same fixed dimension (e.g., 768 or 1024), the phrasing 'same embedding size' could be misinterpreted. The two correct statements are A and E.
- C
Tokens are only used during training.
Why wrong: Incorrect. Tokens are used during both training and inference. During inference, input text is tokenized and processed by the model.
- D
Tokens are always whole words.
Why wrong: Incorrect. Tokens are not always whole words; subword tokenization splits words into smaller units (e.g., 'un' + 'believe' + 'able') to handle rare words and morphology.
- E
The maximum number of tokens a model can process is called the context window.
Correct. The context window is the maximum number of tokens the model can process at once, limiting the length of input text.
1Z0-1127 Fundamentals of Large Language Models Practice Question
This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO statements about tokens in large language models are correct?
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
Common tokenization methods include word-based and subword-based.
Option A is correct because common tokenization methods in large language models include word-based tokenization (splitting text into whole words) and subword-based tokenization (like Byte-Pair Encoding or WordPiece), which handle out-of-vocabulary words and morphological variations more effectively. Subword tokenization is widely used in models like GPT and BERT to balance vocabulary size and coverage.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Common tokenization methods include word-based and subword-based.
Why this is correct
Correct. Common tokenization methods include word-based (splitting into whole words) and subword-based (e.g., Byte-Pair Encoding, WordPiece). Subword tokenization is widely used in modern LLMs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
All tokens have the same embedding size.
Why it's wrong here
Option B is incorrect. While all tokens in a given large language model are mapped to embeddings of the same fixed dimension (e.g., 768 or 1024), the phrasing 'same embedding size' could be misinterpreted. The two correct statements are A and E.
- ✗
Tokens are only used during training.
Why it's wrong here
Incorrect. Tokens are used during both training and inference. During inference, input text is tokenized and processed by the model.
- ✗
Tokens are always whole words.
Why it's wrong here
Incorrect. Tokens are not always whole words; subword tokenization splits words into smaller units (e.g., 'un' + 'believe' + 'able') to handle rare words and morphology.
- ✓
The maximum number of tokens a model can process is called the context window.
Why this is correct
Correct. The context window is the maximum number of tokens the model can process at once, limiting the length of input text.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the distinction between tokenization methods and the fixed embedding dimension, leading candidates to incorrectly assume that tokens vary in embedding size or that tokens must be whole words.
Detailed technical explanation
How to think about this question
Under the hood, tokenization converts raw text into a sequence of integer IDs using a fixed vocabulary, where each ID corresponds to a learned embedding vector. Subword methods like Byte-Pair Encoding (BPE) iteratively merge frequent character pairs, allowing the model to handle rare or unseen words by composing known subword units—this is critical for multilingual models or domain-specific jargon. The context window (option E) defines the maximum token sequence length the model can process due to positional encoding constraints, typically 512 for BERT or 2048 for GPT-3.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Common tokenization methods include word-based and subword-based. — Option A is correct because common tokenization methods in large language models include word-based tokenization (splitting text into whole words) and subword-based tokenization (like Byte-Pair Encoding or WordPiece), which handle out-of-vocabulary words and morphological variations more effectively. Subword tokenization is widely used in models like GPT and BERT to balance vocabulary size and coverage.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 30, 2026
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