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NVIDIA NCA-GENL Exam
Generative AI LLMs (Page 5 )

Updated On: 9-Feb-2026
Page 4 of 20
PDF with 95 Questions

What is the fundamental role of LangChain in an LLM workflow?

  1. To act as a replacement for traditional programming languages.
  2. To reduce the size of AI foundation models.
  3. To orchestrate LLM components into complex workflows.
  4. To directly manage the hardware resources used by LLMs.

Answer(s): C

Explanation:

LangChain is a framework designed to simplify the development of applications powered by large language models (LLMs) by orchestrating various components, such as LLMs, external data sources,

memory, and tools, into cohesive workflows. According to NVIDIA's documentation on generative AI workflows, particularly in the context of integrating LLMs with external systems, LangChain enables developers to build complex applications by chaining together prompts, retrieval systems (e.g., for RAG), and memory modules to maintain context across interactions. For example, LangChain can integrate an LLM with a vector database for retrieval-augmented generation or manage conversational history for chatbots. Option A is incorrect, as LangChain complements, not replaces, programming languages. Option B is wrong, as LangChain does not modify model size. Option D is inaccurate, as hardware management is handled by platforms like NVIDIA Triton, not LangChain.


Reference:

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user- guide/docs/en/stable/nlp/intro.html
LangChain Official Documentation: https://python.langchain.com/docs/get_started/introduction



What type of model would you use in emotion classification tasks?

  1. Auto-encoder model
  2. Siamese model
  3. Encoder model
  4. SVM model

Answer(s): C

Explanation:

Emotion classification tasks in natural language processing (NLP) typically involve analyzing text to predict sentiment or emotional categories (e.g., happy, sad). Encoder models, such as those based on transformer architectures (e.g., BERT), are well-suited for this task because they generate contextualized representations of input text, capturing semantic and syntactic information. NVIDIA's NeMo framework documentation highlights the use of encoder-based models like BERT or RoBERTa for text classification tasks, including sentiment and emotion classification, due to their ability to encode input sequences into dense vectors for downstream classification. Option A (auto-encoder) is used for unsupervised learning or reconstruction, not classification. Option B (Siamese model) is typically used for similarity tasks, not direct classification. Option D (SVM) is a traditional machine learning model, less effective than modern encoder-based LLMs for NLP tasks.


Reference:

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



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?

  1. Use rule-based systems to manually define the characteristics of each category.
  2. Use a large, labeled dataset for each possible category.
  3. Train the new model from scratch for each new category encountered.
  4. Use a pre-trained language model with semantic embeddings.

Answer(s): D

Explanation:

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.


Reference:

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."



Which technology will allow you to deploy an LLM for production application?

  1. Git
  2. Pandas
  3. Falcon
  4. Triton

Answer(s): D

Explanation:

NVIDIA Triton Inference Server is a technology specifically designed for deploying machine learning models, including large language models (LLMs), in production environments. It supports high- performance inference, model management, and scalability across GPUs, making it ideal for real- time LLM applications. According to NVIDIA's Triton Inference Server documentation, it supports frameworks like PyTorch and TensorFlow, enabling efficient deployment of LLMs with features like dynamic batching and model ensemble. Option A (Git) is a version control system, not a deployment tool. Option B (Pandas) is a data analysis library, irrelevant to model deployment. Option C (Falcon) refers to a specific LLM, not a deployment platform.


Reference:

NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton- inference-server/user-guide/docs/index.html



Which Python library is specifically designed for working with large language models (LLMs)?

  1. NumPy
  2. Pandas
  3. HuggingFace Transformers
  4. Scikit-learn

Answer(s): C

Explanation:

The HuggingFace Transformers library is specifically designed for working with large language models (LLMs), providing tools for model training, fine-tuning, and inference with transformer-based architectures (e.g., BERT, GPT, T5). NVIDIA's NeMo documentation often references HuggingFace Transformers for NLP tasks, as it supports integration with NVIDIA GPUs and frameworks like PyTorch for optimized performance. Option A (NumPy) is for numerical computations, not LLMs. Option B (Pandas) is for data manipulation, not model-specific tasks. Option D (Scikit-learn) is for traditional machine learning, not transformer-based LLMs.


Reference:

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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