Mastering Generative AI and Prompt Engineering: Principles, Best Practices and Use Cases
1.What is the subset of AI responsible for developing algorithms and statistical models?
Ans: Machine Learning
2.What is the key ingredient for AI and ML?
Ans: Data
3.What term is used to describe the smaller granular detail within machine learning?
Ans: Deep Learning
4. What is the primary focus of deep learning models?
Ans: Neural network architecture
5. In the machine learning workflow, what comes after data collection?
Ans: Data Preprocessing
6. What percentage of enterprise data is estimated to be structured data?
Ans: 80 to 90 %
7. What is metadata?
Ans: Data about Data
8. What is a feature in the context of machine learning?
Ans: A descriptor for an Instance
9. What is the term for the process of fine-tuning a machine learning model?
Ans: Model turning
10. How does feature engineering relate to machine learning?
Ans: it creates meaningful descriptors for data instances.
11. What are the four main types of machine learning mentioned in the module?
Ans: Supervised learning, unsupervised learning, semi-supervised learning, reinforcement Learning
12. In supervised learning, what does labeled data refer to?
Ans: Data with both feature values and corresponding output labels
13. How is unsupervised learning different from supervised learning?
Ans: Unsupervised learning works without labels and finds patterns on its own
14. What is reinforcement learning, and how is it similar to learning a video game?
Ans: Reinforcement learning is about learning through trial and error, similar to improving in a video game.
15. Which algorithm is commonly used for email spam filtering?
Ans: Naive Bayes classifier
16. In what real-life application is logistic regression frequently used?
Ans: Credit scoring for loans
17. What is the purpose of the evaluation phase in the machine learning model lifecycle?
Ans: To assess the model's performance and accuracy
18. Explain the concept of model deployment using an analogy.
Ans: Model deployment is like running a marathon after training
19.Why might model performance in production differ from its performance during training and evaluation?
Ans: Real-world conditions and data may vary from the training environment.
20. What is the purpose of model inference in the machine learning model lifecycle?
Ans: To predict outcomes or make decisions based on new Data
21. What is a neural network in the context of deep learning?
Ans: A mini-brain in your computer designed to pick up patterns
22. In a neural network, what is the role of the input layer?
Ans: It is the layer data is uploaded
23. What are the hidden layers in a neural network responsible for?
Ans: Identifying patterns
24. How does a neural network become smarter and more accurate over time?
Ans: By receiving more data and learning from it
25. What is the main difference between machine learning and deep learning?
Ans: Deep learning is suitable for complex tasks, while machine learning is for basic tasks
26. What is the primary requirement for deep learning to perform well?
Ans: A high level of computation power
27. Which of the following is NOT an application of deep learning mentioned in the module?
Ans: Controlling video games
28. How does deep learning contribute to improving healthcare, as mentioned in the module?
Ans: By recommending treatment options based on x-ray images
29. In deep learning, what is "back propagation" responsible for?
Ans: Adjusting weights and biases on mistakes
30. Why is deep learning often compared to a "super smartphone" compared to machine learning?
Ans: Because it can handle complex tasks and processes more Data
31. What is a key advantage of using AI like ChatGPT for brainstorming a professional report outline?
Ans: Its helps structure ideas logically and refine sections based on user input.
32. AI enhances emails by improving on the following aspects
Ans: Clarity, Professional tone, Personalisation
33. We can use prompt chaining to:
Ans: Break a complex task into smaller steps, passing the output of one prompt as input to the next
34. In shot prompting, the word “shot” refers to:
Ans: Examples that we share the model to guide its response
35. You are trying to teach an AI model how to rephrase sentences politely. You provide the following prompt:
"Rephrase this sentence politely: 'I need this done now!'
Example: 'Could you please prioritize this task?'"
What type of shot prompting does this represent?
Ans: One-shot prompting
36. What is the primary focus of the Utilitarian Framework for AI?
Ans: Enhancing customer satisfaction
37. Which core principle is emphasized in the Rights-based Framework?
Ans: Empowerment of individuals and communities
38. What does the Virtue Ethics Framework encourage businesses to cultivate in their AI endeavours?
Ans: Empathy and courage
39. What is the purpose of conducting AI audits in organizations?
Ans: To ensure transparency and accountability
40. Why is it important to establish clear responsibilities for AI outcomes?
Ans: To identify who is responsible for AI development and usage
41. How does transparency in AI help address biases in decision-making processes?
Ans: By providing insights into how decisions are made