Fundamentals of Generative AI Course: Practice Assessment I & Final Assessment II
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- Assessment:
- Quiz covering key concepts from all modules
Assessment Part I: Practice
The following questions cover all modules in the Generative AI course. Answer them as a warm-up for the final quiz below in Assessment Part II.
1. What is the key difference between generative and discriminative models?
2. Name three applications of Generative AI.
3. Briefly explain the difference between supervised, unsupervised, and reinforcement learning.
4. What are three types of generative models discussed in the course?
5. Which programming languages and libraries are typically used for implementing Generative AI?
6. What are some ethical concerns associated with Generative AI?
7. Give one example of a potential future trend in Generative AI.
This quiz is designed to assess understanding of the key concepts from the “Fundamentals of Generative AI” course. This quiz covers all four modules and includes a variety of question types.
Assessment Part II: Generative AI Quiz
Module 1: Introduction to Generative AI
- Multiple Choice: What is the primary difference between generative and discriminative models?
- A) Generative models can generate new data, while discriminative models classify existing data.
- B) Discriminative models focus on data structures, while generative models ignore data patterns.
- C) There is no difference; both terms refer to the same concept.
- D) Generative models require less data than discriminative models.
- True/False: Generative AI cannot be used in content creation applications.
Module 2: Core Concepts and Techniques
- Fill in the Blank: The three main types of machine learning are , , and __ learning.
- Short Answer: Explain what a Generative Adversarial Network (GAN) is in your own words.
- Multiple Choice: Which of the following models is NOT a generative model?
- A) Variational Autoencoder (VAE)
- B) Convolutional Neural Network (CNN)
- C) Generative Adversarial Network (GAN)
- D) Diffusion Model
Module 3: Implementing Generative AI
- Multiple Choice: Which programming languages and libraries are commonly used for implementing generative models?
- A) Python, TensorFlow, PyTorch
- B) Java, C++, HTML
- C) R, MATLAB, JavaScript
- D) C#, Ruby, Swift
- True/False: Data preprocessing is not necessary when building a generative model.
Module 4: Ethical Considerations and Future Trends
- Short Answer: Discuss one ethical implication of generative AI technology you learned in this course.
- Multiple Choice: Which of the following is a concern regarding bias in training data for generative models?
- A) It can improve the quality of the generated data.
- B) It can result in perpetuating stereotypes and misinformation.
- C) It has no impact on the model’s performance.
- D) It can only modify the structure of data without ethical implications.
- Short Answer: What is one emerging trend in the field of Generative AI that may shape its future?
Instructions for Completion
- Choose the best answer for multiple-choice questions.
- Write concise answers for short answer and fill-in-the-blank questions.
- Review and provide your answers before submitting.
This assessment aims to evaluate your understanding of the course material effectively. Good luck!