- A
WordPiece tokenizer
Why wrong: WordPiece may split code into more tokens due to character-level merging.
- B
SentencePiece tokenizer with unigram LM
Why wrong: Unigram tokenizer may not be as efficient for code as BPE.
- C
BPE tokenizer trained on code corpora
BPE learns frequent subword patterns in code, reducing token count.
- D
Character-level tokenization
Why wrong: Character-level yields many tokens per code snippet.
Question 550 of 991
1Z0-1127 LLM Fundamentals Practice Question
This 1Z0-1127 practice question tests your understanding of llm fundamentals. 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.
A developer is building a code generation assistant and wants to minimize the number of API calls to the OCI Generative AI service. Which tokenization approach results in the lowest token count for a given code snippet?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
BPE tokenizer trained on code corpora
Option C is correct because BPE (Byte Pair Encoding) tokenizers trained specifically on code corpora learn subword units that align closely with programming language syntax (e.g., common keywords, operators, and variable patterns), resulting in fewer tokens for a given code snippet compared to general-purpose tokenizers. This reduces API calls by encoding more semantic meaning per token, directly minimizing token count.
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.
- ✗
WordPiece tokenizer
Why it's wrong here
WordPiece may split code into more tokens due to character-level merging.
- ✗
SentencePiece tokenizer with unigram LM
Why it's wrong here
Unigram tokenizer may not be as efficient for code as BPE.
- ✓
BPE tokenizer trained on code corpora
Why this is correct
BPE learns frequent subword patterns in code, reducing token count.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Character-level tokenization
Why it's wrong here
Character-level yields many tokens per code snippet.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that any subword tokenizer (like WordPiece or SentencePiece) is equally effective for code, but the trap is that only BPE trained on code corpora optimizes for the repetitive, syntax-heavy nature of programming languages, while others over-segment or use general-language frequency distributions.
Detailed technical explanation
How to think about this question
BPE tokenizers trained on code corpora leverage byte-level merging of frequent character pairs, which captures common code idioms (e.g., '==', 'def', 'self') as single tokens. This is particularly effective for minimizing token count because programming languages have a limited, repetitive vocabulary compared to natural language. In real-world scenarios, using a code-specific BPE tokenizer can reduce token count by 30-50% versus generic tokenizers, directly lowering API costs and latency.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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LLM Fundamentals — study guide chapter
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
LLM Fundamentals — This question tests LLM Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: BPE tokenizer trained on code corpora — Option C is correct because BPE (Byte Pair Encoding) tokenizers trained specifically on code corpora learn subword units that align closely with programming language syntax (e.g., common keywords, operators, and variable patterns), resulting in fewer tokens for a given code snippet compared to general-purpose tokenizers. This reduces API calls by encoding more semantic meaning per token, directly minimizing token count.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This 1Z0-1127 practice question is part of Courseiva's free Oracle certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 1Z0-1127 exam.
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