1. What is the primary goal of Artificial Intelligence?
A. To replace human workers with robots
B. To create systems that can perform tasks that require human intelligence
C. To develop the most complex software
D. To create video games
Explanation: The primary goal of AI is to create systems that can perform tasks requiring human intelligence.
2. Which of the following is a type of machine learning?
A. Supervised Learning
B. Unsupervised Learning
C. Reinforcement Learning
D. All of the above
Explanation: The three main types of machine learning are Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
3. What is a neural network inspired by?
A. Human brain
B. Computer algorithms
C. Mathematical equations
D. Animal instincts
Explanation: Neural networks are inspired by the human brain's structure and function.
4. In supervised learning, what is the purpose of the training data?
A. To make predictions based on the input data
B. To find patterns in the data without any guidance
C. To provide labeled examples for the model to learn from
D. To test the model's accuracy
Explanation: In supervised learning, the training data provides labeled examples for the model to learn from.
5. What does 'overfitting' refer to in machine learning?
A. A model that performs well on training data but poorly on new data
B. A model that performs poorly on both training and test data
C. A model that performs well on both training and test data
D. A model that has too few parameters
Explanation: Overfitting occurs when a model performs well on training data but poorly on new data.
6. Which of the following algorithms is used for classification tasks?
A. Linear Regression
B. K-Nearest Neighbors
C. K-Means Clustering
D. Principal Component Analysis
Explanation: K-Nearest Neighbors is commonly used for classification tasks.
7. What is the purpose of a confusion matrix?
A. To visualize the performance of a classification algorithm
B. To store large amounts of data
C. To track the progress of a machine learning model
D. To confuse the model
Explanation: A confusion matrix is used to visualize the performance of a classification algorithm.
8. What is 'reinforcement learning'?
A. Learning from labeled data
B. Learning from data without labels
C. Learning by interacting with the environment and receiving rewards or penalties
D. Learning by mimicking human behavior
Explanation: Reinforcement learning involves learning by interacting with the environment and receiving rewards or penalties.
9. Which type of machine learning involves finding hidden patterns in data without any labels?
A. Supervised Learning
B. Unsupervised Learning
C. Reinforcement Learning
D. Semi-supervised Learning
Explanation: Unsupervised learning involves finding hidden patterns in data without any labels.
10. What is a 'feature' in the context of machine learning?
A. A characteristic or attribute used to describe each data point
B. The final output of a machine learning model
C. The process of evaluating a machine learning model
D. A type of machine learning algorithm
Explanation: In machine learning, a feature is a characteristic or attribute used to describe each data point.
11. What does "Responsible AI" primarily aim to achieve?
A. Faster deployment of AI systems
B. Increased revenue from AI solutions
C. Ethical, transparent, and accountable AI systems
D. Maximized automation of human tasks
Explanation: Responsible AI aims to create ethical, transparent, and accountable AI systems.
12. Which of the following is a principle of Responsible AI?
A. Profit maximization
B. Transparency
C. Complexity
D. Obscurity
Explanation: Transparency is a key principle of Responsible AI.
13. What is "bias" in the context of AI, and why is it important to address?
A. Bias refers to the speed of an AI algorithm; addressing it makes the AI faster.
B. Bias refers to the system's favoritism toward certain outcomes; addressing it ensures fairness and equity.
C. Bias refers to the AI's ability to learn; addressing it makes the AI smarter.
D. Bias refers to the cost of AI development; addressing it makes AI cheaper.
Explanation: Bias in AI refers to the system's favoritism toward certain outcomes; addressing it ensures fairness and equity.
14. Which practice is crucial for maintaining privacy in AI systems?
A. Collecting as much data as possible
B. Sharing data publicly for transparency
C. Implementing data anonymization and encryption techniques
D. Avoiding data collection
Explanation: Implementing data anonymization and encryption techniques is crucial for maintaining privacy in AI systems.
15. What is the role of explainability in AI?
A. To make AI decisions faster
B. To reduce the cost of AI implementation
C. To help humans understand how AI systems make decisions
D. To increase the complexity of AI systems
Explanation: The role of explainability in AI is to help humans understand how AI systems make decisions.
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