ml

MACHINE LEARNING

Exam Instructions:

Total Number of Questions: 45
You will be required to answer a total of 45 multiple-choice questions.

Time Limit: 15 minutes for the entire exam
 Once the time is up, the exam will automatically submit.

Passing Criteria:
A minimum score of 50% is required to pass the exam 

Multiple Attempts:
You are allowed to take the exam multiple times. Only your highest score will be considered for certification.

Additional Instructions:

The exam timer cannot be paused once it begins.

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machine learning

MACHINE LEARNING

Test your MACHINE LEARNING skills with a challenging exam designed to evaluate your knowledge in programming, data structures, and algorithms

The certificate will be generated based on the information you provide in the form, so please ensure that all details are entered correctly.

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1) What is “SVM” (Support Vector Machine) used for in machine learning?

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2) What is a hyperplane in SVM?

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3) What is the primary purpose of “cross-validation”?

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4) What is “k-fold cross-validation”?

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5) Which activation function is commonly used in deep learning?

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6) What is the main function of “Principal Component Analysis”?

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7) Which type of model is used for unsupervised learning?

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8) What is “feature importance” in decision trees?

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9) What is the purpose of “mean squared error” in regression models?

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10) What does “data visualization” facilitate?

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11) What is the primary purpose of the ROC curve?

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12) What does “convolutional neural network” (CNN) specialize in?

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13) What is a bagging technique in ML?

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14) What is the purpose of the Adam optimizer in neural networks?

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15) What does “hyperparameter optimization” involve?

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16) Which algorithm is primarily used for clustering?

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17) What is “ensemble learning” in machine learning?

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18) What is “decision boundary” in classification?

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19) What is “R-squared” in regression models?

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20) What is the role of the loss function in machine learning models?

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21) What is the advantage of “mini-batch gradient descent”?

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22) What is a hyperparameter in machine learning?

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23) What is the effect of increasing the depth of a decision tree?

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24) What is “decision tree pruning” used for?

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25) What is a decision boundary in machine learning?

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26) What is the goal of “transfer learning” in machine learning?

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27) What is the purpose of stratified sampling in data collection?

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28) What is the purpose of a hyperparameter in ML?

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29) What is “recall” in classification tasks?

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30) What does “AUC” represent in ROC analysis?

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31) What is the purpose of the “cost function” in supervised learning?

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32) What does overfitting mean in ML?

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33) What is the purpose of using a confusion matrix?

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34) What is “underfitting” in machine learning?

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35) Which algorithm is most suitable for dimensionality reduction?

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36) What is the purpose of the “learning rate” in training neural networks?

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37) What is “bagging” in ensemble learning?

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38) What does “one-hot encoding” achieve?

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39) Which activation function is commonly used in hidden layers of neural networks?

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40) What is the purpose of “gradient descent”?

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41) What does “k-fold cross-validation” help with?

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42) What is the purpose of “regularization” in machine learning?

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43) What does “early stopping” prevent in training models?

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44) Which algorithm is used for regression tasks?

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45) What does the ROC curve represent in classification?

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