NCP-GENL Data Preparation Practice Question
A team is building a NeMo-based LLM pipeline and must tokenize a corpus that mixes English, Japanese, and Python source code. They plan to train a custom tokenizer with NVIDIA NeMo. Which tokenizer configuration best supports all three content types without excessive sequence length?
⚠ Common exam trap
The trap here is choosing a tokenizer algorithm without checking whether its training data covers all content types, since out-of-vocabulary scripts and code inflate sequence length regardless of algorithm.
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-level Byte-Pair Encoding with a vocabulary of 64,000 to 128,000 tokens trained on a balanced sample of all three content types.
Byte-level BPE with a large vocabulary trained on a balanced multilingual and code sample handles arbitrary scripts and symbols without unknown tokens. It compresses common English words, Japanese subwords, and Python identifiers into single tokens, controlling sequence length. Training on all three distributions aligns merge statistics with actual usage, unlike English-only or Japanese-only training, and the 64k-128k range provides enough capacity for multilingual plus code coverage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Byte-level Byte-Pair Encoding with a vocabulary of 64,000 to 128,000 tokens trained on a balanced sample of all three content types.
Why this is correct
Byte-level BPE avoids unknown tokens by operating on raw bytes, so any script or code symbol is representable. A large vocabulary of 64k-128k tokens captures common multilingual subwords and code identifiers, reducing sequence length. Training on a balanced sample ensures the merge rules reflect English, Japanese, and Python frequencies, which is exactly what this mixed corpus needs.
- ✗
SentencePiece unigram tokenizer trained exclusively on Japanese text.
Why it's wrong here
Training only on Japanese biases the unigram language model toward Japanese subwords; English words and Python syntax are then segmented inefficiently, often at character level, increasing sequence length. SentencePiece unigram is a valid algorithm, but the training data choice here ignores two of the three content types, so it cannot meet the scenario's multilingual and code requirements.
- ✗
WordPiece tokenization trained only on the English portion of the corpus.
Why it's wrong here
Training only on English means Japanese characters and code tokens are out-of-vocabulary and fall back to unknown or character-level pieces, producing very long sequences and poor representation. WordPiece can work multilingually, but restricting training data to English guarantees poor coverage of the other two content types, directly violating the scenario's need for balanced support.
- ✗
Byte-Pair Encoding with a vocabulary limited to 8,000 tokens.
Why it's wrong here
A small 8,000-token BPE vocabulary cannot represent multilingual text and code efficiently; many words and symbols are split into long character sequences, inflating sequence length and slowing training. BPE itself is viable, but the tiny vocabulary defeats the goal of compact encoding across three very different distributions, so this configuration fails the scenario's requirement.
About these practice questions
This NCP-GENL question is part of Courseiva's 352-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCP-GENL practice question is part of Courseiva's free NVIDIA 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 NCP-GENL exam.