1. Interacting with Large Language Models (LLMs) differs from traditional machine learning models. Working with LLMs involves natural language input, known as a _____, resulting in output from the Large Language Model, known as the ______.
Choose the answer that correctly fill in the blanks.
- tunable request, completion
- prompt, completion
- prediction request, prediction response
- prompt, fine-tuned LLM
2. Large Language Models (LLMs) are capable of performing multiple tasks supporting a variety of use cases. Which of the following tasks supports the use case of converting code comments into executable code?
- Translation
- Information Retrieval
- Text summarization
- Invoke actions from text
3. What is the self-attention that powers the transformer architecture?
- The ability of the transformer to analyze its own performance and make adjustments accordingly.
- A mechanism that allows a model to focus on different parts of the input sequence during computation.
- A measure of how well a model can understand and generate human-like language.
- A technique used to improve the generalization capabilities of a model by training it on diverse datasets.
4. Which of the following stages are part of the generative AI model lifecycle mentioned in the course? (Select all that apply)
- Deploying the model into the infrastructure and integrating it with the application.
- Defining the problem and identifying relevant datasets.
- Performing regularization
- Manipulating the model to align with specific project needs.
- Selecting a candidate model and potentially pre-training a custom model.
5. "RNNs are better than Transformers for generative AI Tasks."
Is this true or false?
- True
- False
6. Which transformer-based model architecture has the objective of guessing a masked token based on the previous sequence of tokens by building bidirectional representations of the input sequence.
- Autoencoder
- Autoregressive
- Sequence-to-sequence
7. Which transformer-based model architecture is well-suited to the task of text translation?
- Autoencoder
- Autoregressive
- Sequence-to-sequence
8. Do we always need to increase the model size to improve its performance?
- True
- False
9. Scaling laws for pre-training large language models consider several aspects to maximize performance of a model within a set of constraints and available scaling choices. Select all alternatives that should be considered for scaling when performing model pre-training?
- [ ]Batch size: Number of samples per iteration
- Model size: Number of parameters
- Dataset size: Number of tokens
- Compute budget: Compute constraints
10. "You can combine data parallelism with model parallelism to train LLMs."
Is this true or false?
- True
- False