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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic
Details

Topic 1

• Fundamentals of machine learning and neural networks: Covers the core concepts of how machine learning models learn from data, including the structure and function of neural networks that underpin large language models.

Topic 2

• Alignment: Addresses methods for ensuring LLM behavior is safe, accurate, and consistent with human intentions and values.

Topic 3

• Data analysis and visualization: Covers interpreting datasets and presenting insights through visual tools to support informed model development decisions.

Topic 4

• Experimentation: Explores running and evaluating trials to test model behavior, compare approaches, and validate generative AI solutions.

Topic 5

• LLM integration and deployment: Addresses connecting LLMs into real-world applications and deploying them reliably across production environments.

Topic 6

• Python libraries for LLMs: Covers key Python frameworks and tools — such as LangChain, Hugging Face, and similar libraries — used to build and interact with LLMs.

Topic 7

• Prompt engineering: Focuses on techniques for designing and refining input prompts to effectively guide LLM outputs toward desired results.

Topic 8

• Data preprocessing and feature engineering: Covers preparing raw data through cleaning, transformation, and feature selection to make it suitable for model training.

 

NVIDIA Generative AI LLMs Sample Questions (Q91-Q96):

NEW QUESTION # 91
Which Python library is specifically designed for working with large language models (LLMs)?

• A. HuggingFace Transformers
• B. NumPy
• C. Pandas
• D. Scikit-learn

Answer: A

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.
References:
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

 

NEW QUESTION # 92
Which metric is primarily used to evaluate the quality of the text generated by language models?

• A. Precision
• B. Recall
• C. Accuracy
• D. Perplexity

Answer: D

Explanation:
Perplexity is the primary metric used to evaluate the quality of text generated by language models, as emphasized in NVIDIA's Generative AI and LLMs course. Perplexity measures how well a language model predicts a sequence of tokens, with lower values indicating better performance, as the model is less
"surprised" by the data. It is calculated as the exponentiated average negative log-likelihood of the tokens in a test set, reflecting the model's ability to assign high probabilities to correct sequences. In generative tasks, perplexity is widely used because it directly assesses the model's fluency and coherence. Option B, Precision, and Option C, Recall, are metrics for classification tasks, not text generation. Option D, Accuracy, is also irrelevant for evaluating generative quality, as it applies to categorical predictions. The course notes:
"Perplexity is a key metric for evaluating language models, measuring how well the model predicts text sequences, with lower perplexity indicating higher-quality generation." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

 

NEW QUESTION # 93
Which of the following is an activation function used in neural networks?

• A. Diffusion function
• B. Mean Squared Error function
• C. K-means clustering function
• D. Sigmoid function

Answer: D

Explanation:
The sigmoid function is a widely used activation function in neural networks, as covered in NVIDIA's Generative AI and LLMs course. It maps input values to a range between 0 and 1, making it particularly useful for binary classification tasks and as a non-linear activation in early neural network architectures. The sigmoid function, defined as f(x) = 1 / (1 + e

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