Master NVIDIA NCA-GENL Exam with Reliable Practice Questions
Upgrade to Premium[Python Libraries for LLMs]
Which feature of the HuggingFace Transformers library makes it particularly suitable for fine-tuning large language models on NVIDIA GPUs?
Correct : B
The HuggingFace Transformers library is widely used for fine-tuning large language models (LLMs) due to its seamless integration with PyTorch and NVIDIA's TensorRT, enabling GPU-accelerated training and inference. NVIDIA's NeMo documentation references HuggingFace Transformers for its compatibility with CUDA and TensorRT, which optimize model performance on NVIDIA GPUs through features like mixed-precision training and dynamic shape inference. This makes it ideal for scaling LLM fine-tuning on GPU clusters. Option A is incorrect, as Transformers focuses on GPU, not CPU, pipelines. Option C is partially true but not the primary feature for fine-tuning. Option D is false, as Transformers is for deep learning, not classical algorithms.
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
HuggingFace Transformers Documentation: https://huggingface.co/docs/transformers/index
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[Experimentation]
Which metric is commonly used to evaluate machine-translation models?
Correct : B
The BLEU (Bilingual Evaluation Understudy) score is the most commonly used metric for evaluating machine-translation models. It measures the precision of n-gram overlaps between the generated translation and reference translations, providing a quantitative measure of translation quality. NVIDIA's NeMo documentation on NLP tasks, particularly machine translation, highlights BLEU as the standard metric for assessing translation performance due to its focus on precision and fluency. Option A (F1 Score) is used for classification tasks, not translation. Option C (ROUGE) is primarily for summarization, focusing on recall. Option D (Perplexity) measures language model quality but is less specific to translation evaluation.
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
Papineni, K., et al. (2002). 'BLEU: A Method for Automatic Evaluation of Machine Translation.'
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[Data Analysis and Visualization]
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)
Correct : C, D
RAPIDS is an open-source suite of GPU-accelerated data science libraries developed by NVIDIA to speed up data processing and machine learning workflows. According to NVIDIA's RAPIDS documentation, its key advantages include:
Option C: Using GPUs for parallel processing, which significantly accelerates computations for tasks like data manipulation and machine learning compared to CPU-based processing.
Option D: Scaling to multiple GPUs, allowing RAPIDS to handle large datasets efficiently by distributing workloads across GPU clusters.
Option A is incorrect, as RAPIDS focuses on GPU, not CPU, performance. Option B (subsampling) is not a primary feature of RAPIDS, which aims for exact results. Option E (more memory) is a hardware characteristic, not a RAPIDS feature.
NVIDIA RAPIDS Documentation: https://rapids.ai/
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[Fundamentals of Machine Learning and Neural Networks]
In the context of a natural language processing (NLP) application, which approach is most effective for implementing zero-shot learning to classify text data into categories that were not seen during training?
Correct : D
Zero-shot learning allows models to perform tasks or classify data into categories without prior training on those specific categories. In NLP, pre-trained language models (e.g., BERT, GPT) with semantic embeddings are highly effective for zero-shot learning because they encode general linguistic knowledge and can generalize to new tasks by leveraging semantic similarity. NVIDIA's NeMo documentation on NLP tasks explains that pre-trained LLMs can perform zero-shot classification by using prompts or embeddings to map input text to unseen categories, often via techniques like natural language inference or cosine similarity in embedding space. Option A (rule-based systems) lacks scalability and flexibility. Option B contradicts zero-shot learning, as it requires labeled data. Option C (training from scratch) is impractical and defeats the purpose of zero-shot learning.
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
Brown, T., et al. (2020). 'Language Models are Few-Shot Learners.'
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[Experimentation]
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?
Correct : 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
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