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Week-1_Quiz.md

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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