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NVIDIA Generative AI LLMs Sample Questions:

1. What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)

A) Cheaper computational costs during inference.
B) Trained without the need for labeled data.
C) It is easier to explain the predictions.
D) Single generic model can do more than one task.
E) Smaller latency, higher throughput.

2. What is a Tokenizer in Large Language Models (LLM)?

A) A machine learning algorithm that predicts the next word/token in a sequence of text.
B) A technique used to convert text data into numerical representations called tokens for machine learning.
C) A method to remove stop words and punctuation marks from text data.
D) A tool used to split text into smaller units called tokens for analysis and processing.

3. When preprocessing text data for an LLM fine-tuning task, why is it critical to apply subword tokenization (e.
g., Byte-Pair Encoding) instead of word-based tokenization for handling rare or out-of-vocabulary words?

A) Subword tokenization removes punctuation and special characters to simplify text input.
B) Subword tokenization breaks words into smaller units, enabling the model to generalize to unseen words.
C) Subword tokenization reduces the model's computational complexity by eliminating embeddings.
D) Subword tokenization creates a fixed-size vocabulary to prevent memory overflow.

4. You are working on developing an application to classify images of animals and need to train a neural model.
However, you have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?

A) Dropout
B) Transfer learning
C) Random initialization
D) Early stopping

5. Which aspect in the development of ethical AI systems ensures they align with societal values and norms?

A) Achieving the highest possible level of prediction accuracy in AI models.
B) Developing AI systems with autonomy from human decision-making.
C) Ensuring AI systems have explicable decision-making processes.
D) Implementing complex algorithms to enhance AI's problem-solving capabilities.

Solutions: