1. Scenario: A company wants to classify customer emails as either “complaint” or “praise.” Which machine learning type should they use?
A: Supervised learning
2. In unsupervised learning, what is the primary goal?
A: Identifying hidden structures or patterns in data
3. Scenario: A financial institution wants to detect anomalies in transactions that could indicate fraud. Which learning type is most appropriate?
A: Unsupervised learning
4. Which algorithm would be best suited for discovering the underlying structure of a dataset with no predefined labels?
A: K-Means
5. If you are trying to maximize cumulative rewards in a changing environment, which learning type would you use?
A: Reinforcement learning
6. Which of the following algorithms is most commonly used for clustering in unsupervised learning?
A: K-Means
7. Which type of learning is typically used in applications like autonomous driving or game AI?
A: Reinforcement learning
8. Scenario:
Your company has a large customer base, and you want to segment them into groups based on their purchasing behavior. You don’t have any predefined labels for these groups, and you want the model to find patterns on its own. Which machine learning type would you use?
A: Unsupervised learning
9. A robot learns by receiving rewards for correct movements in a maze. What learning method best suits this scenario?
A: Reinforcement learning
10. Scenario:
A company wants to group products that customers frequently buy together. It has no predefined labels for these product groups but wants the model to identify associations on its own. Which machine learning type is appropriate?
A: Unsupervised learning
11. A financial institution wants to detect fraudulent transactions in real-time, using historical labeled data. What learning type would be most suitable?
A: Supervised learning
12. Scenario:
An airline wants to optimize flight routes by considering weather patterns, fuel consumption, and air traffic. The system learns through trial and error, improving its efficiency by receiving feedback based on how successful previous flights were. Which machine learning type fits this problem?
A: Reinforcement learning
13. Which method should you choose to classify customer emails into categories based on labeled data?
A: Supervised learning
14. Scenario:
A robot is tasked with navigating a maze. It starts without any knowledge of the maze layout and learns the best path by trying different actions and receiving rewards when it reaches checkpoints. Which learning approach is best suited for this task?
A: Reinforcement learning
15. If you need to identify groups of customers based on their purchasing patterns but have no labels for these groups, which learning method should you choose?
A: Unsupervised learning
16. In reinforcement learning, how does the agent learn to make decisions?
A: By receiving rewards or penalties from the environment
17. Scenario: You want to segment customers into groups based on purchasing behavior, without having predefined categories. Which approach would be most appropriate?
A: Unsupervised learning
18. What type of algorithm is often used when you want to segment data without predefined categories?
A: Clustering
19. Which of the following is true about machine learning?
A: It enables systems to learn from data and improve over time.
20. Scenario: A marketing team wants to divide their customers into groups based on buying patterns but has no prior labels. What type of machine learning is appropriate?
A: Unsupervised learning
21. Which of the following best describes supervised learning?
A: Learning from labeled data to predict future outcomes
22. What is the best machine learning method for categorizing emails as “spam” or “not spam” when the data includes labels for each email?
A: Supervised learning
23. A healthcare provider wants to develop a model to detect early signs of diabetes from patient medical records. The data includes both the medical history and the outcome of whether the patient developed diabetes. What is the most suitable learning type?
A: Supervised learning
24. To optimize a robot’s movements through a maze, where it learns by receiving feedback from the environment, which machine learning technique should be applied?
A: Reinforcement learning
25. Which of the following models is most appropriate for classification tasks in supervised learning?
A: Support Vector Machine
26. Scenario: You are building a recommendation system for a streaming service and need to find relationships between different movies. Which model type would be most suitable?
A: Unsupervised learning
27. Which algorithm is often used in supervised learning for continuous value prediction?
A: Linear Regression
28. Which model would be best for reducing the dimensionality of large datasets while preserving the most important information?
A: Principal Component Analysis
29. You are developing a spam filter for an email service. The model is provided with labeled data that identifies whether emails are spam or not. Which machine learning method will be most suitable?
A: Supervised learning
30. You’re designing a model to predict stock prices based on historical data. The dataset contains labeled information on stock prices over the past ten years. Which type of learning would you implement?
A: Supervised learning
31. A business wants to detect fraud in real-time transactions. The data is labeled, indicating whether past transactions were fraudulent or legitimate. What type of learning would be most appropriate for this task?
A: Supervised learning
32. When attempting to uncover hidden patterns in data without predefined labels, which type of learning is appropriate?
A: Unsupervised learning