100 multiple-choice questions (MCQs) on Artificial Intelligence (AI) with answers and explanations can provide a great resource for study or teaching.
1. What is Artificial Intelligence?
- A) A field of computer science that focuses on creating machines capable of intelligent behavior
- B) A study of natural intelligence
- C) A programming technique for system control
- D) A method for evaluating human behavior
Explanation: AI involves simulating human intelligence processes by machines, especially computer systems.
2. Which of the following is a branch of Artificial Intelligence?
- A) Neural Networks
- B) Quantum Computing
- C) Data Structures
- D) None of the above
Explanation: Neural networks are a branch of AI that mimic the human brain's functioning in processing information.
3. What does the Turing Test evaluate?
- A) Machine learning ability
- B) Machine translation ability
- C) Machine intelligence
- D) Machine speed
Explanation: The Turing Test evaluates a machine's ability to exhibit intelligent behavior equivalent to or indistinguishable from a human.
4. Which AI technology mimics human neurons?
- A) Expert Systems
- B) Neural Networks
- C) Genetic Algorithms
- D) Fuzzy Logic
Explanation: Neural networks are inspired by the structure and functioning of the human brain's neural connections.
5. Who is considered the father of AI?
- A) Alan Turing
- B) John McCarthy
- C) Marvin Minsky
- D) Geoffrey Hinton
Explanation: John McCarthy coined the term "Artificial Intelligence" and is considered the father of the field.
6. What is Machine Learning?
- A) A subset of AI focused on programming computers to learn from data
- B) The process of embedding AI in robots
- C) A branch of engineering focused on making machines run faster
- D) None of the above
Explanation: Machine learning is a subset of AI where computers learn from data and improve over time.
7. Which algorithm is used in supervised learning?
- A) K-means
- B) Regression
- C) Apriori
- D) DBSCAN
Explanation: Regression is a supervised learning algorithm used for prediction.
8. What is Deep Learning?
- A) A subset of machine learning involving neural networks with multiple layers
- B) A technology that enables hardware control
- C) A system for data storage
- D) None of the above
Explanation: Deep learning uses neural networks with many layers to model complex patterns in data.
9. Which of these is an application of AI?
- A) Self-driving cars
- B) Email spam filtering
- C) Medical diagnosis
- D) All of the above
Explanation: AI applications are widespread, including self-driving cars, spam filters, and healthcare diagnostics.
10. What is Reinforcement Learning?
- A) Learning based on rewards and punishments
- B) Learning from labeled data
- C) Unsupervised learning
- D) None of the above
Explanation: Reinforcement learning involves learning from rewards for correct actions and penalties for incorrect actions.
11. Which method is used for voice recognition in AI?
- A) Natural Language Processing (NLP)
- B) Convolutional Neural Networks (CNNs)
- C) Linear Regression
- D) None of the above
Explanation: NLP techniques are used to process and recognize human language, including voice.
12. Which programming language is most commonly used for AI?
- A) Python
- B) HTML
- C) CSS
- D) PHP
Explanation: Python is popular for AI development due to its vast libraries like TensorFlow and PyTorch.
13. What is a Neural Network's "hidden layer"?
- A) The input layer
- B) The output layer
- C) The middle layers that process information
- D) The activation function
Explanation: Hidden layers in a neural network are intermediate layers that process and transform inputs into outputs.
14. Which AI type is used for recommendation systems?
- A) Supervised Learning
- B) Unsupervised Learning
- C) Reinforcement Learning
- D) Semi-supervised Learning
Explanation: Unsupervised learning algorithms identify patterns without labeled data, useful for recommendations.
15. What does NLP stand for?
- A) Natural Logic Programming
- B) Neural Language Processing
- C) Natural Language Processing
- D) None of the above
Explanation: NLP stands for Natural Language Processing, which involves interacting with computers using human language.
16. Which of these is a commonly used Machine Learning library?
- A) TensorFlow
- B) Angular
- C) WordPress
- D) Node.js
Explanation: TensorFlow is a popular library for developing machine learning models.
17. Which AI field is used for diagnosing diseases?
- A) Expert Systems
- B) Robotics
- C) Genetic Programming
- D) Network Security
Explanation: Expert systems are used in medical fields to simulate the decision-making ability of a human expert.
18. What is Genetic Algorithm inspired by?
- A) Neural structures in the brain
- B) The natural selection process
- C) The chemical reaction process
- D) None of the above
Explanation: Genetic algorithms simulate the process of natural selection and are used in optimization problems.
19. Which machine learning algorithm is used for classification problems?
- A) K-Nearest Neighbors (KNN)
- B) K-means clustering
- C) Linear Regression
- D) None of the above
Explanation: KNN is a supervised learning algorithm commonly used for classification.
20. Which is NOT an example of AI?
- A) Facial recognition
- B) Text summarization
- C) Microsoft Word
- D) Self-driving cars
Explanation: Microsoft Word is a word processing software without AI-based functionalities.
21. Which of the following is a common goal of AI?
- A) Automate repetitive tasks
- B) Mimic human intelligence
- C) Reduce errors in computing
- D) All of the above
Explanation: AI aims to automate tasks, reduce errors, and mimic human intelligence to enhance productivity and accuracy.
22. Which algorithm is commonly used in AI for finding patterns in large datasets?
- A) Linear Regression
- B) Decision Tree
- C) Apriori
- D) None of the above
Explanation: The Apriori algorithm is used in data mining to identify patterns or associations in large datasets.
23. What is overfitting in machine learning?
- A) When the model performs well on training data but poorly on new data
- B) When the model performs well on all datasets
- C) When the model underperforms on all data
- D) None of the above
Explanation: Overfitting occurs when a model learns the training data too well, including noise, leading to poor generalization on unseen data.
24. Which of these is an example of unsupervised learning?
- A) Decision Tree
- B) Linear Regression
- C) K-means clustering
- D) Logistic Regression
Explanation: K-means clustering is an unsupervised learning algorithm used for grouping data without labeled responses.
25. What is supervised learning?
- A) Learning without any input data
- B) Learning from labeled data
- C) Learning through rewards and penalties
- D) None of the above
Explanation: Supervised learning requires labeled data, where the model learns from input-output pairs to make predictions.
26. Which model is best suited for predicting binary outcomes?
- A) Decision Tree
- B) Logistic Regression
- C) K-means Clustering
- D) Linear Regression
Explanation: Logistic regression is ideal for binary classification problems, such as predicting a yes/no outcome.
27. Which AI technique uses “if-then” rules?
- A) Neural Networks
- B) Genetic Algorithms
- C) Expert Systems
- D) Reinforcement Learning
Explanation: Expert systems often rely on “if-then” rules to simulate human decision-making in specific domains.
28. What is the main goal of reinforcement learning?
- A) Minimize cost functions
- B) Maximize rewards over time
- C) Improve data accuracy
- D) Solve regression problems
Explanation: Reinforcement learning aims to maximize cumulative rewards by taking actions in an environment.
29. Which of the following is not an application of machine learning?
- A) Spam filtering
- B) Facial recognition
- C) Game playing
- D) Text formatting in Word documents
Explanation: Text formatting is a standard feature in word processors and doesn’t involve machine learning.
30. Which algorithm is primarily used for predicting continuous values?
- A) Decision Tree
- B) Logistic Regression
- C) Linear Regression
- D) K-means Clustering
Explanation: Linear regression predicts continuous values by modeling the relationship between independent and dependent variables.
31. Which of the following AI types require human assistance to learn?
- A) Unsupervised Learning
- B) Supervised Learning
- C) Reinforcement Learning
- D) None of the above
Explanation: Supervised learning requires labeled data, meaning human assistance is needed to label the data for training.
32. What is pruning in a decision tree?
- A) Adding branches
- B) Removing branches to avoid overfitting
- C) Adding nodes
- D) Removing nodes to avoid underfitting
Explanation: Pruning simplifies a decision tree by removing branches that have little predictive power, reducing overfitting.
33. Which of these is an AI search technique?
- A) Depth-first search
- B) Binary search
- C) Randomized search
- D) All of the above
Explanation: Depth-first search is a fundamental AI technique used in problem-solving and pathfinding tasks.
34. What is a hyperparameter in machine learning?
- A) A parameter used for controlling the learning process
- B) A parameter learned from data
- C) An output of the model
- D) A test accuracy score
Explanation: Hyperparameters control the training process and are set before training the model.
35. What is a common evaluation metric for classification problems?
- A) Mean Squared Error
- B) Precision
- C) Gradient Descent
- D) Clustering
Explanation: Precision is a common metric to evaluate the performance of classification models.
36. In neural networks, what does “backpropagation” refer to?
- A) An activation function
- B) The process of sending outputs back to the input
- C) An optimization algorithm for adjusting weights
- D) None of the above
Explanation: Backpropagation is used to adjust the weights in a neural network by minimizing the error in predictions.
37. What does NLP in AI stand for?
- A) Natural Language Processing
- B) National Language Programming
- C) Neural Language Processing
- D) Neural Linguistic Programming
Explanation: NLP stands for Natural Language Processing, which deals with the interaction between computers and human language.
38. Which of the following is an example of supervised learning?
- A) Principal Component Analysis
- B) K-means Clustering
- C) Linear Regression
- D) Self-organizing maps
Explanation: Linear regression is a supervised learning algorithm that learns from labeled data.
39. Which of these techniques is not commonly used for classification?
- A) Decision Tree
- B) K-means Clustering
- C) Support Vector Machine
- D) Naive Bayes
Explanation: K-means clustering is typically used for clustering, not classification, as it is an unsupervised learning technique.
40. What is “feature scaling”?
- A) Scaling output values
- B) Standardizing the range of features
- C) Scaling image dimensions
- D) Adding new features
Explanation: Feature scaling involves standardizing or normalizing the range of features to improve model performance.
41. Which AI method uses evolutionary algorithms?
- A) Neural Networks
- B) Genetic Algorithms
- C) Decision Trees
- D) Linear Regression
Explanation: Genetic algorithms use evolutionary concepts like selection, crossover, and mutation to solve optimization problems.
42. What does “big data” refer to?
- A) Small datasets
- B) Large and complex datasets
- C) Data with missing values
- D) None of the above
Explanation: Big data refers to datasets that are too large or complex for traditional data processing applications.
43. Which of the following is a neural network model?
- A) Convolutional Neural Network (CNN)
- B) Linear Regression
- C) Decision Tree
- D) None of the above
Explanation: Convolutional Neural Networks (CNNs) are specialized neural networks primarily used for processing structured grid data, like images.
44. What is “transfer learning”?
- A) Learning from previous tasks to improve new tasks
- B) Moving data from one model to another
- C) Learning without any labeled data
- D) None of the above
Answer: A
Explanation: Transfer learning involves taking a pre-trained model on one task and fine-tuning it for a different but related task.45. Which of the following is a common activation function used in neural networks?
- A) ReLU (Rectified Linear Unit)
- B) Sigmoid
- C) Tanh
- D) All of the above
Explanation: ReLU, sigmoid, and tanh are all commonly used activation functions in neural networks.
46. What is the purpose of data normalization?
- A) To increase dataset size
- B) To improve data consistency and accuracy
- C) To eliminate duplicates
- D) To reduce computational costs
Explanation: Data normalization standardizes the range of independent variables or features in the dataset, improving model performance.
47. Which of the following is a disadvantage of decision trees?
- A) They are easy to interpret
- B) They are prone to overfitting
- C) They can handle both numerical and categorical data
- D) They are non-parametric
Explanation: Decision trees are prone to overfitting, especially with complex trees and small datasets.
48. What does “ensemble learning” involve?
- A) Using a single model for predictions
- B) Combining multiple models to improve predictions
- C) Learning from unsupervised data
- D) None of the above
Explanation: Ensemble learning combines multiple models to produce better predictions than any single model could achieve.
49. What is a common use case for convolutional neural networks (CNNs)?
- A) Time series forecasting
- B) Image classification
- C) Text generation
- D) Data clustering
Explanation: CNNs are particularly well-suited for image classification tasks due to their ability to capture spatial hierarchies in data.
50. What does “confusion matrix” help evaluate?
- A) Data preprocessing steps
- B) Model performance in classification tasks
- C) The number of features in a dataset
- D) None of the above
Explanation: A confusion matrix summarizes the performance of a classification model by comparing predicted and actual classifications.
51. What does “natural language generation” (NLG) refer to?
- A) The ability of machines to understand human language
- B) The ability of machines to generate human-like text
- C) The process of classifying text data
- D) None of the above
Explanation: NLG is a subfield of NLP that focuses on generating human-like text based on data input.
52. What is “feature extraction”?
- A) The process of choosing the right model
- B) The technique of selecting a subset of relevant features
- C) The process of cleaning data
- D) None of the above
Explanation: Feature extraction involves selecting a subset of relevant features from the dataset to improve model performance.
53. Which of the following algorithms is used for clustering?
- A) K-means
- B) Linear Regression
- C) Logistic Regression
- D) Decision Tree
Explanation: K-means is a popular clustering algorithm used to partition data into K distinct groups.
54. What is “dropout” in neural networks?
- A) A technique to increase the size of the dataset
- B) A method for regularization to prevent overfitting
- C) A technique to reduce computation time
- D) None of the above
Explanation: Dropout is a regularization technique used to prevent overfitting by randomly setting a fraction of input units to zero during training.
55. Which of the following is NOT a type of neural network?
- A) Recurrent Neural Network (RNN)
- B) Convolutional Neural Network (CNN)
- C) Support Vector Machine (SVM)
- D) Fully Connected Network
Explanation: Support Vector Machine (SVM) is a traditional machine learning algorithm, not a type of neural network.
56. What is the purpose of cross-validation?
- A) To increase dataset size
- B) To evaluate the model's performance on unseen data
- C) To speed up the training process
- D) None of the above
Explanation: Cross-validation helps assess how the results of a statistical analysis will generalize to an independent dataset.
57. Which of the following is a challenge in deep learning?
- A) Need for large amounts of data
- B) High computational cost
- C) Overfitting
- D) All of the above
Explanation: Deep learning requires large datasets, significant computational resources, and is prone to overfitting.
58. What is the main advantage of using decision trees?
- A) They require a lot of data
- B) They are easy to interpret and visualize
- C) They are fast to train
- D) None of the above
Explanation: Decision trees are intuitive and provide a clear visual representation of decisions and their possible consequences.
59. In the context of AI, what does “bias” refer to?
- A) A method for selecting features
- B) A systematic error introduced by an algorithm
- C) A type of optimization
- D) None of the above
Explanation: Bias in AI refers to the systematic error introduced by an algorithm due to assumptions made in the learning process.
60. Which of the following is an application of reinforcement learning?
- A) Image classification
- B) Game playing
- C) Data clustering
- D) Text generation
Explanation: Reinforcement learning is often used in game playing, where an agent learns to make decisions based on rewards.
61. What is the purpose of using a validation set in machine learning?
- A) To train the model
- B) To fine-tune hyperparameters
- C) To test the model on unseen data
- D) None of the above
Explanation: A validation set is used to tune the model's hyperparameters and evaluate its performance during training.
62. What does “data augmentation” refer to?
- A) Increasing the size of a dataset by creating modified versions of existing data
- B) Removing duplicates from a dataset
- C) Normalizing data
- D) None of the above
Explanation: Data augmentation is a technique used to increase the size of a dataset by creating altered copies of existing data.
63. Which of the following is a type of unsupervised learning?
- A) Linear Regression
- B) K-means Clustering
- C) Decision Trees
- D) Logistic Regression
Explanation: K-means clustering is an unsupervised learning algorithm used to group similar data points.
64. What is the role of the “loss function” in machine learning?
- A) To evaluate the model's accuracy
- B) To quantify how well the model's predictions match the actual data
- C) To optimize the model's hyperparameters
- D) None of the above
Explanation: The loss function measures the difference between the predicted output and the actual output, guiding the optimization process.
65. What does “hyperparameter tuning” refer to?
- A) The process of changing the model architecture
- B) The process of adjusting model parameters before training
- C) The process of cleaning data
- D) None of the above
Explanation: Hyperparameter tuning involves adjusting the configuration parameters of a model to improve its performance.
66. Which of the following best describes “overfitting”?
- A) The model performs well on training data but poorly on unseen data
- B) The model performs equally well on both training and unseen data
- C) The model is too simple to capture the underlying patterns in data
- D) None of the above
Explanation: Overfitting occurs when a model learns the training data too well, including noise, resulting in poor generalization to new data.
67. What does “gradient descent” do?
- A) It generates new data
- B) It finds the optimal parameters for a model
- C) It increases the model's complexity
- D) None of the above
Explanation: Gradient descent is an optimization algorithm used to minimize the loss function by iteratively adjusting model parameters.
68. Which of the following is an advantage of using deep learning?
- A) It requires less data
- B) It can automatically learn feature representations
- C) It is less computationally intensive
- D) None of the above
Explanation: Deep learning models can automatically learn hierarchical feature representations from data without manual feature engineering.
69. What does “word embedding” refer to?
- A) A technique for representing words in a continuous vector space
- B) A method for cleaning text data
- C) A way to visualize data
- D) None of the above
Explanation: Word embedding is a technique that maps words or phrases to vectors of real numbers in a continuous vector space.
70. Which algorithm is commonly used for classification tasks?
- A) K-means Clustering
- B) Support Vector Machine (SVM)
- C) Principal Component Analysis (PCA)
- D) None of the above
Explanation: Support Vector Machine (SVM) is a widely used algorithm for classification tasks.
71. What is “backpropagation”?
- A) A method for training models
- B) An algorithm used to compute gradients in neural networks
- C) A type of optimization algorithm
- D) None of the above
Explanation: Backpropagation is an algorithm used for training neural networks by computing gradients of the loss function with respect to the weights.
72. What is a “recommendation system”?
- A) A system that predicts future outcomes
- B) A system that suggests products or services based on user preferences
- C) A system that analyzes time series data
- D) None of the above
Explanation: A recommendation system uses algorithms to suggest products or services to users based on their preferences and behavior.
73. Which of the following is a common use case for reinforcement learning?
- A) Stock price prediction
- B) Robotic control
- C) Image segmentation
- D) Spam detection
Explanation: Reinforcement learning is often used in robotic control, where agents learn to perform tasks through trial and error.
74. What is the purpose of “feature scaling”?
- A) To remove irrelevant features
- B) To normalize the range of independent variables
- C) To increase the complexity of the model
- D) None of the above
Explanation: Feature scaling ensures that features are on a similar scale, which is important for many algorithms, especially those that rely on distance metrics.
75. What does “natural language processing” (NLP) encompass?
- A) Only text classification
- B) Only speech recognition
- C) The interaction between computers and human language
- D) None of the above
Explanation: Natural Language Processing (NLP) involves the interaction between computers and human (natural) languages, including understanding and generating text and speech.
76. What is “dimensionality reduction”?
- A) Increasing the number of features in a dataset
- B) Reducing the number of features while preserving important information
- C) Normalizing feature values
- D) None of the above
- Answer: B
Explanation: Dimensionality reduction techniques, like PCA, reduce the number of features while retaining as much information as possible.
77. Which of the following is an application of computer vision?
- A) Text generation
- B) Image recognition
- C) Time series forecasting
- D) None of the above
Explanation: Image recognition is a common application of computer vision, which enables computers to interpret and process visual information from the world.
78. What is “supervised learning”?
- A) Learning from unlabeled data
- B) Learning from labeled data
- C) Learning through reinforcement
- D) None of the above
Explanation: Supervised learning involves training a model on a labeled dataset, where the input data is paired with the correct output.
79. What is a “hyperparameter”?
- A) A parameter learned during training
- B) A parameter set before the training process
- C) A measure of model performance
- D) None of the above
Explanation: Hyperparameters are parameters that are set before the learning process begins, affecting the training and performance of the model.
80. Which of the following is a common technique for preventing overfitting?
- A) Using more complex models
- B) Using dropout in neural networks
- C) Reducing the size of the training dataset
- D) None of the above
Explanation: Dropout is a regularization technique used to prevent overfitting by randomly dropping units from the neural network during training.
81. What is “machine translation”?
- A) Translating text manually
- B) Automatically translating text from one language to another using algorithms
- C) Translating programming languages
- D) None of the above
Explanation: Machine translation refers to the use of algorithms to automatically translate text from one language to another.
82. Which of the following describes “unsupervised learning”?
- A) Learning with labeled data
- B) Learning patterns from unlabeled data
- C) Learning through trial and error
- D) None of the above
Explanation: Unsupervised learning involves discovering patterns or structures in data without labeled responses.
83. What is “predictive analytics”?
- A) Predicting future outcomes based on historical data
- B) Analyzing current data only
- C) Data cleaning
- D) None of the above
Explanation: Predictive analytics involves using statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
84. Which of the following is a challenge of AI?
- A) Data privacy
- B) Model interpretability
- C) Bias in algorithms
- D) All of the above
Explanation: All the listed options are significant challenges in AI, affecting how models are built and used.
85. What is a “neural network”?
- A) A computer network that simulates the way the human brain operates
- B) A type of traditional algorithm
- C) A hardware component
- D) None of the above
Explanation: A neural network is a computational model inspired by the way biological neural networks in the human brain process information.
86. What does “ensemble learning” refer to?
- A) Combining multiple models to improve performance
- B) Training a single model
- C) Learning from data sequentially
- D) None of the above
Explanation: Ensemble learning involves combining the predictions from multiple models to improve accuracy and robustness.
87. Which technique is commonly used for time series analysis?
- A) K-means Clustering
- B) ARIMA (AutoRegressive Integrated Moving Average)
- C) Decision Trees
- D) None of the above
Explanation: ARIMA is a widely used statistical method for analyzing and forecasting time series data.
88. What is the purpose of “data preprocessing”?
- A) To visualize data
- B) To prepare raw data for analysis
- C) To build models directly from raw data
- D) None of the above
Explanation: Data preprocessing is the process of transforming raw data into a format suitable for analysis, which may include cleaning, normalization, and feature selection.
89. What is “transfer learning”?
- A) Using a model trained on one task to improve performance on another task
- B) Learning from unlabeled data
- C) Training a model from scratch
- D) None of the above
Explanation: Transfer learning involves taking a pre-trained model and adapting it to a new, but related, task, often resulting in faster convergence and improved performance.
90. What is “data augmentation”?
- A) The process of generating new training samples from existing data
- B) The process of cleaning data
- C) The process of reducing data
- D) None of the above
Explanation: Data augmentation involves creating altered copies of existing data to increase the diversity of the training set, which can help improve model robustness.
91. Which of the following techniques is used for feature selection?
- A) PCA (Principal Component Analysis)
- B) Linear Regression
- C) K-Nearest Neighbors
- D) None of the above
Explanation: PCA is a technique often used for feature selection by transforming the data into a new coordinate system where the greatest variance lies along the first coordinate.
92. What does “active learning” refer to?
- A) Learning from static datasets
- B) A type of learning where the model can query an oracle for labels
- C) Learning without any feedback
- D) None of the above
Explanation: Active learning involves a learning model that can select the most informative data points to query an oracle (human annotator) for labels, improving the efficiency of the learning process.
93. Which of the following describes a “decision boundary”?
- A) A line that separates different classes in a classification problem
- B) A threshold for regression models
- C) The process of making decisions
- D) None of the above
Explanation: A decision boundary is a boundary that separates different classes in a classification problem, indicating where the model predicts different class labels.
94. What is “feature extraction”?
- A) The process of identifying the most important features from raw data
- B) The process of removing all features from a dataset
- C) The process of increasing the number of features
- D) None of the above
Explanation: Feature extraction is the process of transforming raw data into a set of usable features for model training, often simplifying the input space.
95. What does “reinforcement learning” involve?
- A) Learning from labeled data
- B) Learning through trial and error with rewards and punishments
- C) Learning from static datasets
- D) None of the above
Explanation: Reinforcement learning involves training agents to make decisions by rewarding desired actions and punishing undesired actions, leading to optimal behavior over time.
96. Which of the following is an example of “semi-supervised learning”?
- A) Using only labeled data
- B) Using both labeled and unlabeled data
- C) Learning without any labels
- D) None of the above
Explanation: Semi-supervised learning uses a combination of a small amount of labeled data and a large amount of unlabeled data to improve learning accuracy.
97. What is “data normalization”?
- A) Making data fit a normal distribution
- B) Scaling data to a specific range
- C) Removing duplicates from data
- D) None of the above
Explanation: Data normalization refers to the process of scaling individual data points to fit within a specific range, typically [0, 1] or [-1, 1].
98. Which of the following is NOT a type of neural network?
- A) Convolutional Neural Network (CNN)
- B) Recurrent Neural Network (RNN)
- C) Decision Tree
- D) Generative Adversarial Network (GAN)
Explanation: A decision tree is not a type of neural network; it is a traditional machine learning algorithm used for classification and regression.
99. What is “bias-variance tradeoff”?
- A) The balance between a model’s ability to minimize bias and variance
- B) The tradeoff between training and testing accuracy
- C) The tradeoff between feature selection and model complexity
- D) None of the above
Explanation: The bias-variance tradeoff is the balance between the error introduced by bias (error due to overly simplistic assumptions in the learning algorithm) and variance (error due to too much complexity in the learning algorithm).
100. What is “cloud computing”?
- A) Computing services provided over the internet
- B) Traditional on-premises computing
- C) Only storing data in the cloud
- D) None of the above
Explanation: Cloud computing refers to the delivery of various computing services (storage, processing, and networking) over the internet, enabling flexible resources and faster innovation.
Post a Comment