Welcome to your AI /ML
1.
AI/ML
Which of the following is an example of supervised learning?
Apriori Algorithm
Linear Regression
WhichK-Means Clustering
d) PCA
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2.
AI/ML
In machine learning, what is a ‘feature’?
A data attribute used as input
The final prediction
The algorithm used
A type of loss function
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3.
AI/ML
Which of the following is a classification algorithm?
Decision Tree
Apriori
PCA
K-Means
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4.
AI/ML
What is the main goal of supervised learning?
Reduce data size
Predict output using labeled data
Perform dimensionality reduction
Find patterns without labels
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5.
AI/ML
Which library is widely used for ML in Python?
NumPy
BeautifulSoup
Flask
TensorFlow
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6.
AI/ML
Which metric is commonly used for regression?
Accuracy
Recall
Mean Squared Error
Precision
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7.
AI/ML
Which of these is an unsupervised algorithm?
Random Forest
K-Means
Naive Bayes
Logistic Regression
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8.
AI/ML
What is overfitting?
Model uses too many outputs
Model has too few features
Model performs well on all data
Model fits too well on training data but fails on new data
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9.
AI/ML
Which of the following is NOT a type of machine learning?
Predictive Analytics
Unsupervised
Reinforcement
Supervised
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10.
AI/ML
Which activation function is most commonly used in hidden layers?
Softmax
Sigmoid
Tanh
ReLU
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11.
AI/ML
Which algorithm is best suited for text classification?
K-Means
Naive Bayes
Linear Regression
KNN
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12.
What does PCA stand for?
Principal Component Analysis
Partial Cluster Analysis Principal Correlation Approach
Predictive Component Algorithm
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13.
AI/ML
Gradient Descent is used to:
Minimize the cost function
Find missing values
Normalize data
Maximize loss
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14.
AI/ML
Which of these models is an ensemble method?
Random Forest
K-Means
Naive Bayes
Linear Regression
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15.
AI/ML
In neural networks, what is a ‘bias’?
Type of regularization
Error between prediction and truth
Weight assigned to output
Constant added to activation function
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16.
AI/ML
Which evaluation metric is used for imbalanced datasets?
R² Score
Precision-Recall or F1 Score
Accuracy
RMSE
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17.
AI/ML
Which optimizer is an improvement over traditional Gradient Descent?
Min-Max
Softmax
Adam
Sigmoid
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18.
AI/ML
Which of these techniques can help reduce overfitting?
Removing validation data
Adding more layers
Dropout Regularization
Increasing learning rate
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19.
AI/ML
What is the main difference between classification and regression?
Both predict continuous values
Regression uses trees, classification doesn’t
Classification uses unsupervised data
Classification predicts categories, regression predicts continuous values
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20.
AI/ML
What does the “kernel trick” refer to in SVMs?
Avoiding gradient descent
Reducing number of features
Initializing weights
Transforming data into higher dimensions
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21.
AI/ML
What is vanishing gradient problem?
Gradients explode in size
Model doesn’t learn due to high learning rate
Gradients become too small during backpropagation
Loss never decreases
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22.
AI/ML
What does the softmax function output?
Binary values
Raw scores
Probabilities that sum to 1
Gradients
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23.
AI/ML
What is the purpose of the learning rate in optimization?
Controls batch size
Defines model complexity
Measures accuracy
Determines size of steps during gradient updates
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24.
AI/ML
What is the purpose of a confusion matrix?
Show regression line
Visualize classification performance
Detect overfitting
Measure loss
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25.
AI/ML
Which of the following best describes Reinforcement Learning?
Learning through rewards and penalties
Learning without any data
Learning from unsupervised clusters
Learning from labeled data
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26.
None
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