NCP-GENL Data Preparation Practice Question
You are preparing a large instruction-tuning dataset for an LLM using NVIDIA NeMo. You need to ensure the dataset supports efficient training and evaluation. Which TWO steps are essential in the data preparation pipeline? (Choose two.)
⚠ Common exam trap
The trap here is treating optional enhancements like tokenizer retraining or augmentation as mandatory, when the truly essential steps are formatting and splitting.
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
✓
Convert the dataset into a format compatible with NeMo, such as JSONL with input and output fields
Formatting the dataset into a NeMo-compatible structure such as JSONL with input and output fields ensures the data loader can consume it, while splitting into training, validation, and test sets enables proper model evaluation and hyperparameter tuning. These two steps are foundational; other activities like tokenizer training or augmentation are optional and context-dependent.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Convert the dataset into a format compatible with NeMo, such as JSONL with input and output fields
Why this is correct
NeMo's instruction-tuning pipeline expects data in a structured format, commonly JSONL with fields like input and output or prompt and completion. Converting the raw dataset into this format ensures the data loader can parse and batch examples correctly. Without this step, training would fail or require custom parsing code, so it is essential.
- ✗
Train a tokenizer from scratch on the dataset
Why it's wrong here
Training a new tokenizer is not always necessary and can be harmful if the base model already has a well-trained tokenizer. For instruction tuning, you typically reuse the model's existing tokenizer to maintain compatibility. Unless the domain has highly specialized vocabulary, retraining the tokenizer is an optional optimization, not an essential step.
- ✗
Apply data augmentation using back-translation
Why it's wrong here
Back-translation is a data augmentation technique that can increase diversity, but it is not essential for every instruction-tuning dataset. It adds complexity and may introduce translation artifacts. The scenario asks for essential steps, and augmentation is optional and domain-dependent, so it does not qualify as universally required.
- ✗
Remove all punctuation from the text
Why it's wrong here
Removing punctuation is an aggressive normalization that can destroy meaning and is not a standard requirement for instruction tuning. Most LLMs handle punctuation well and rely on it for syntax and semantics. This step is neither necessary nor recommended as a general practice, so it is not an essential part of the pipeline.
- ✓
Split the dataset into training, validation, and test sets
Why this is correct
Splitting the data into training, validation, and test sets is essential for measuring generalization and tuning hyperparameters. The validation set guides model selection, while the test set provides an unbiased final evaluation. Without distinct splits, you cannot reliably detect overfitting or compare experiments, making this a core preparation step.
About these practice questions
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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.