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NVIDIA NCA-GENL Exam - Topic 3 Question 13 Discussion

In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?
B) Stratified k-fold cross-validation.
A) Single hold-out validation with a fixed test set.
C) Bootstrapping with random sampling.
D) Grid search for hyperparameter tuning.

NVIDIA NCA-GENL Exam - Topic 3 Question 13 Discussion

Actual exam question for NVIDIA's NCA-GENL exam
Question #: 13
Topic #: 3
[All NCA-GENL Questions]

In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?

Show Suggested Answer Hide Answer
Suggested Answer: B

Stratified k-fold cross-validation is a robust experimental design technique for evaluating machine learning models, especially on imbalanced datasets. It divides the dataset into k folds while preserving the class distribution in each fold, ensuring that the model is evaluated on representative samples of all classes. NVIDIA's NeMo documentation on model evaluation recommends stratified cross-validation for tasks like text classification to obtain reliable performance estimates, particularly when classes are unevenly distributed (e.g., in sentiment analysis with few negative samples). Option A (single hold-out) is less robust, as it may not capture class imbalance. Option C (bootstrapping) introduces variability and is less suitable for imbalanced data. Option D (grid search) is for hyperparameter tuning, not performance estimation.


NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/model_finetuning.html

by Glory at Nov 21, 2025, 03:12 PM

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Silva
4 months ago
B gives a better estimate of model performance overall.
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Holley
5 months ago
C could work, but B is more reliable for this task.
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Laurel
5 months ago
A is too risky. Fixed test set can mislead results.
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Troy
5 months ago
Agreed, B ensures each fold has the same distribution.
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Detra
5 months ago
B is definitely the best choice here.
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Elfriede
5 months ago
Surprised that people still use single hold-out validation!
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Leila
6 months ago
I thought bootstrapping was better for this?
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Johnna
6 months ago
Totally agree, it helps with imbalanced data!
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Elise
6 months ago
D) Grid search? More like grid snooze-fest. I'll take B) for the win!
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Danica
6 months ago
I'd go with C) Bootstrapping. It's like rolling the dice for your dataset!
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Kimbery
7 months ago
Definitely B. Stratified k-fold ensures the test sets are representative of the class distribution.
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Colton
7 months ago
I vaguely recall that single hold-out validation can lead to misleading results with imbalanced data, so I doubt it's the right choice here.
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Cherilyn
7 months ago
I’m leaning towards option B because it ensures that each fold has a representative distribution of classes, which seems crucial for evaluation.
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Gearldine
7 months ago
I practiced a similar question, and I feel like bootstrapping could also help, but it might not be as effective as stratified k-fold for this specific scenario.
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Whitley
7 months ago
I think I remember that stratified k-fold cross-validation is often recommended for imbalanced datasets, but I'm not entirely sure why it's better than the others.
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Dacia
7 months ago
I think B is the correct answer. Stratified k-fold cross-validation is designed to handle imbalanced datasets effectively.
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Glenna
8 months ago
Bootstrapping with random sampling could be an interesting approach, but I'm not sure if it's the most robust option for this task.
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Joseph
8 months ago
I'm a bit unsure about this one. I'll need to review my notes on experimental design techniques for imbalanced data.
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Rocco
8 months ago
B) Stratified k-fold cross-validation is the way to go!
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Terrilyn
8 months ago
B) Stratified k-fold cross-validation is the way to go for imbalanced datasets.
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Alease
8 months ago
I think B is the best choice. Stratified k-fold helps with imbalance.
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Zita
9 months ago
D is important, but it doesn't address the imbalance directly.
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Buddy
9 months ago
B) Stratified k-fold is the most robust option here. Gotta love that balanced sampling.
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Frederica
9 months ago
Stratified k-fold cross-validation sounds like the way to go here. It should help account for the imbalanced dataset.
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Malissa
9 months ago
Hmm, this seems like a tricky one. I'll need to think carefully about the pros and cons of each approach.
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Eugene
4 months ago
Grid search is great for tuning, but not for performance estimation.
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Chau
4 months ago
True, but I still prefer stratified k-fold for its reliability.
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Huey
4 months ago
Bootstrapping could work too, but it might introduce bias.
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Kris
4 months ago
Agreed! It helps maintain the class distribution.
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Dortha
4 months ago
I think stratified k-fold cross-validation is the best choice.
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