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1. What is the defining characteristic of the training data used in supervised learning?

A) The data is generated randomly by the algorithm.
B) The data is unlabeled, and the model must find patterns on its own
C) The data is always image-based.
D) The data is labeled, meaning each example is paired with a target output.

2. The primary goal of a supervised learning model is to:

A) Discover hidden patterns without any guidance
B) Reduce the dimensionality of the input data for visualization.
C) Generalize from the training data to make accurate predictions on new, unseen data.
D) Memorize the entire training dataset perfectly.

3. In the analogy of a child learning from flashcards, the animal's name on the card represents what component of supervised learning?

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A) The loss function
B) The input features.
C) The label or target output.
D) The model's parameters.

4. Which of the following tasks is a classic example of a classification problem?

A) Estimating the annual revenue of a company.
B) Forecasting the temperature for tomorrow.
C) Diagnosing a tumor as malignant or benign based on medical images.
D) Predicting the selling price of a house based on its features.

5. A model that predicts the continuous value of a stock price for the next day is solving a:

A) Clustering problem.
B) Regression problem.
C) Dimensionality reduction problem
D) Classification problem

6. What is the core objective of unsupervised learning?

A) To predict a target variable based on labeled examples
B) To classify emails into spam and non-spam folders
C) To achieve perfect accuracy on a held-out test set.
D) To discover the inherent structure, patterns, or relationships within unlabeled data.

7. In the analogy of a child grouping toys without instructions, the act of putting all the cars together is most similar to which unsupervised learning technique?

A) Regression
B) Clustering
C) Reinforcement Learning.
D) Classification

8. Grouping customers based solely on their purchasing behavior, without pre-defined categories, is an application of:

A) Clustering, a type of unsupervised learning.
B) A support vector machine for classification.
C) Linear Regression, a type of supervised learning.
D) Logistic Regression, a type of supervised learning.

9. The main goal of dimensionality reduction techniques like PCA is to:

A) Predict a continuous output variable.
B) Increase the number of features to improve model accuracy.
C) Assign categorical labels to each data point.
D) Reduce the number of features while preserving the most important information in the data.

10. Market basket analysis, which finds rules like "if chips then soda," is a classic example of:
Association rule learning in unsupervised learning.
Classification in supervised learning.
Regression in supervised learning.
Deep learning with neural networks.

A) Regression in supervised learning.
B) Association rule learning in unsupervised learning.
C) Deep learning with neural networks.
D) Classification in supervised learning.

11. Semi-supervised learning is particularly useful in real-world scenarios because:

A) Labeling data is often expensive and time-consuming, so it leverages a small labeled set with a large unlabeled set.
B) It is simpler to implement than unsupervised learning.
C) It is always more accurate than fully supervised learning.
D) It requires no labeled data at all.

12. The fundamental question that a regression model aims to answer is:

A) "Which category?"
B) "How much?" or "How many?"
C) "Is this pattern anomalous?"
D) "What is the underlying group?"

13. The fundamental question that a classification model aims to answer is:

A) "How much?" or "How many?"
B) "What is the correlation between these variables?"
C) "How can I reduce the number of features?"
D) "Which category?" or "What class?"

14. Which algorithm is most directly designed for predicting a continuous target variable?

A) Linear Regression.
B) Logistic Regression.
C) k-Nearest Neighbors for classification.
D) Decision Tree for classification.

15. A model that uses patient data to assign a label of "High," "Medium," or "Low" risk for a disease is performing:

A) Regression.
B) Dimensionality reduction.
C) Multi-class classification.
D) Clustering.

16. In a Decision Tree used for classification, what do the leaf nodes represent?

A) The final class labels or decisions.
B) The average value of a continuous target.
C) The input features for a new data point.
D) The probability of moving to the next node.

17. In a Regression Tree, what is typically represented at the leaf nodes?

A) A categorical class label.
B) The name of the feature used for splitting.
C) A random number.
D) A continuous value, often the mean of the target values of the training instances that reach the leaf.

18. A key strength of Decision Trees is their:

A) Immunity to overfitting on noisy datasets.
B) Interpretability; the model's decision-making process is easy to understand and visualize.
C) Superior performance on all types of data compared to other algorithms.
D) Guarantee to find the global optimum for any dataset.

19. The "kernel trick" used in Support Vector Machines (SVMs) allows them to:

A) Find a linear separating hyperplane in a high-dimensional feature space, even when the data is not linearly separable in the original space.
B) Initialize the weights of a neural network.
C) Grow a tree structure by making sequential decisions.
D) Perform linear regression more efficiently.

20. The "support vectors" in an SVM are the:

A) The axes of the original feature space.
B) All data points in the training set.
C) The weights of a neural network layer.
D) Data points that are closest to the decision boundary and most critical for defining the optimal hyperplane.

21. When comparing Decision Trees and SVMs, a primary advantage of SVMs is:

A) Their inherent resistance to any form of overfitting.
B) Their effectiveness in high-dimensional spaces and their ability to model complex, non-linear decision boundaries.
C) Their lower computational cost for very large datasets.
D) Their superior interpretability and simplicity.

22. The process in supervised learning where a model's parameters are adjusted to minimize the difference between its predictions and the true labels is called:

A) Training or model fitting.
B) Data preprocessing.
C) Dimensionality reduction.
D) Clustering.

23. A key challenge in unsupervised learning is evaluating model performance because:

A) The models are always less accurate than supervised models.
B) The data is always too small.
C) The algorithms are not well-defined.
D) There are no ground truth labels to compare the results against.

24. The task of reducing a 50-dimensional dataset to a 2-dimensional plot for visualization is best accomplished by:

A) A Classification algorithm like Logistic Regression.
B) An Association rule learning algorithm.
C) A Regression algorithm like Linear Regression.
D) Dimensionality Reduction techniques like Principal Component Analysis (PCA).

25. If an e-commerce company wants to automatically group its products into categories without any pre-existing labels, it should use:

A) Regression, a supervised learning method.
B) Clustering, an unsupervised learning method.
C) Classification, a supervised learning method.
D) A neural network for image recognition.

26. The core building block of a neural network is a(n):

A) Principal component.
B) Artificial neuron or perceptron, which receives inputs, applies a transformation, and produces an output.
C) Decision node in a tree.
D) Support vector.

27. In a neural network, the function inside a neuron that determines its output based on the weighted sum of its inputs is called the:

A) Activation function.
B) Kernel function.
C) Optimization algorithm.
D) Loss function.

28. Which of the following is a non-linear activation function crucial for allowing neural networks to learn complex patterns?

A) A constant function.
B) The identity function (f(x) = x).
C) Rectified Linear Unit (ReLU).
D) The mean squared error function.

29. The process of "training" a neural network involves:

A) Iteratively adjusting the weights and biases to minimize a loss function.
B) Randomly assigning weights and never changing them.
C) Clustering the input data.
D) Manually setting the weights based on expert knowledge.

30. Backpropagation is the algorithm used in neural networks to:

A) Efficiently calculate the gradient of the loss function with respect to all the weights in the network, enabling the use of gradient descent.
B) Perform clustering on the output layer.
C) Initialize the weights before training.
D) Visualize the network's architecture.

31. Deep Learning is a subfield of machine learning that primarily uses:

A) Neural networks with many layers (hence "deep").
B) Simple linear regression models.
C) K-means clustering exclusively.
D) Decision trees with a single split.

32. A key advantage of deep neural networks over shallower models is their ability to:

A) Automatically learn hierarchical feature representations from data.
B) Be perfectly interpretable, like a decision tree.
C) Always train faster and with less data.
D) Operate without any need for data preprocessing.

33. Convolutional Neural Networks (CNNs) are particularly well-suited for tasks involving:

A) Tabular data with many categorical features.
B) Image data, due to their architecture which exploits spatial locality.
C) Unsupervised clustering of audio signals.
D) Text data and natural language processing.

34. The "convolution" operation in a CNN is designed to:

A) Detect local features (like edges or textures) in the input by applying a set of learnable filters.
B) Perform the final classification.
C) Initialize the weights of the network.
D) Flatten the input into a single vector.

35. Recurrent Neural Networks (RNNs) are designed to handle:

A) Independent and identically distributed (IID) data points.
B) Static, non-temporal data.
C) Only image data.
D) Sequential data, like time series or text, due to their internal "memory" of previous inputs.

36. The "vanishing gradient" problem in deep networks refers to:

A) The gradients becoming too large and causing numerical instability.
B) The model overfitting to the training data.
C) The loss function reaching a perfect value of zero.
D) The gradients becoming exceedingly small as they are backpropagated through many layers, which can halt learning in early layers.

37. The "training set" is used to:

A) Fit the model's parameters (e.g., the weights in a neural network).
B) Provide an unbiased evaluation of a final model's performance.
C) Tune the model's hyperparameters.
D) Deploy the model in a production environment.

38. The "validation set" is primarily used for:

A) Data preprocessing and cleaning.
B) The final, unbiased assessment of the model's generalization error.
C) The initial training of the model's weights.
D) Tuning hyperparameters and making decisions about the model architecture during development.

39. The "test set" should be:

A) Used as part of the training data to improve accuracy.
B) Ignored in the machine learning pipeline.
C) Used repeatedly to tune the model's hyperparameters.
D) Used only once, for a final evaluation of the model's performance on unseen data after model development is complete.

40. Overfitting occurs when a model:

A) Learns the training data too well, including its noise and outliers, and performs poorly on new, unseen data.
B) Is evaluated using the training set instead of a test set.
C) Is too simple to capture the trends in the data.
D) Fails to learn the underlying pattern in the training data.

41. A common technique to reduce overfitting in neural networks is:

A) Increasing the model's capacity by adding more layers.
B) Dropout, which randomly ignores a subset of neurons during training.
C) Training for more epochs without any checks.
D) Using a smaller training dataset.

42. The "bias" of a model refers to:

A) The error from sensitivity to small fluctuations in the training set, leading to overfitting.
B) The error from erroneous assumptions in the learning algorithm, leading to underfitting.
C) The weights connecting the input layer to the hidden layer.
D) The activation function used in the output layer.

43. The "variance" of a model refers to:

A) The error from erroneous assumptions in the learning algorithm, leading to underfitting.
B) The speed at which the model trains.
C) The error from sensitivity to small fluctuations in the training set, leading to overfitting.
D) The intercept term in a linear regression model.

44. The "bias-variance tradeoff" implies that:

A) Bias and variance can be minimized to zero simultaneously.
B) Only variance is important for model performance.
C) Decreasing bias will typically increase variance, and vice versa. The goal is to find a balance.
D) Only bias is important for model performance.

45. A learning curve that shows high training accuracy but low validation accuracy is a classic sign of:

A) Underfitting.
B) Perfect model performance.
C) A well-generalized model.
D) Overfitting.

46. In a neural network, the "loss function" (or cost function) measures:

A) The speed of the backpropagation algorithm.
B) The number of layers in the network.
C) How well the model is performing on the training data; it's the quantity we want to minimize during training.
D) The accuracy on the test set.

47. Gradient Descent is an optimization algorithm that:

A) Is only used for unsupervised learning.
B) Iteratively adjusts parameters in the direction that reduces the loss function.
C) Randomly searches the parameter space for a good solution.
D) Guarantees finding the global minimum for any loss function.

48. The "learning rate" in gradient descent controls:

A) The activation function for the output layer.
B) The size of the step taken during each parameter update. A rate that is too high can cause divergence, while one that is too low can make training slow.
C) The number of layers in a neural network.
D) The amount of training data used in each epoch.

49. "Epoch" in neural network training refers to:

A) The processing of a single training example.
B) The final evaluation on the test set.
C) One complete pass of the entire training dataset through the learning algorithm.
D) A type of regularization technique.

50. "Batch Size" in neural network training refers to:

A) The number of validation examples.
B) The total number of examples in the training set.
C) The number of layers in the network.
D) The number of training examples used in one forward/backward pass before the model's parameters are updated.

51. "Stochastic Gradient Descent" (SGD) uses a batch size of:

A) Exactly 50% of the training set.
B) A random number between 1 and 100.
C) 1, meaning the parameters are updated after each individual training example.
D) The entire training set.

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