admin Oct, Tue, 2024 EXAM MACHINE LEARNING 123456789101112131415161718192021222324252627282930313233343536373839404142434445 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. Good luck, and feel free to retake the exam to improve your score! 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. NameEmailPhone NumberUniversityCollegeDegreeDepartmentPass Out YearPass Out Year2014201520162017201820192020202120222023202420252026202720282029 1 / 45 1) What is “SVM” (Support Vector Machine) used for in machine learning? A) Combines weak learners B) Increases the number of features C) Reduces dimensionality D) Finds the best hyperplane for classification 2 / 45 2) What is a hyperplane in SVM? A) Linear regression line B) Data point C) Decision boundary D) Feature vector 3 / 45 3) What is the primary purpose of “cross-validation”? A) Assess model generalization B) Optimize hyperparameters C) Increase the training data size D) Reduce overfitting 4 / 45 4) What is “k-fold cross-validation”? A) Splits data into k subsets B) Reduces dimensionality C) Trains the model on multiple datasets D) Removes irrelevant features 5 / 45 5) Which activation function is commonly used in deep learning? A) Sigmoid B) Tanh C) ReLU D) Softmax 6 / 45 6) What is the main function of “Principal Component Analysis”? A) Retains variance B) Simplifies model complexity C) Increases training time D) Reduces feature count 7 / 45 7) Which type of model is used for unsupervised learning? A) Both are used for predicting continuous values B) Classification predicts continuous values, regression predicts categories C) Both are used for classification D) Classification predicts categories, regression predicts values 8 / 45 8) What is “feature importance” in decision trees? A) Combines multiple trees B) Reduces overfitting C) Measures feature contribution D) Increases model interpretability 9 / 45 9) What is the purpose of “mean squared error” in regression models? A) Measures impurity in the data B) Measures average squared error C) Reduces dimensionality D) Measures accuracy 10 / 45 10) What does “data visualization” facilitate? A) Simplifies feature selection B) Presents data in graphical format C) Increases data dimensionality D) Enhances model complexity 11 / 45 11) What is the primary purpose of the ROC curve? A) Improve model complexity B) Evaluate binary classifier performance C) Evaluate binary classifier performance D) Measure accuracy 12 / 45 12) What does “convolutional neural network” (CNN) specialize in? A) Processes images effectively B) Classifies text documents C) Reduces data dimensionality D) Analyzes time series data 13 / 45 13) What is a bagging technique in ML? A) Increase model bias B) Simplify model complexity C) Reduce variance D) Reduce variance 14 / 45 14) What is the purpose of the Adam optimizer in neural networks? A) Efficient training B) Increase training time C) Simplify model complexity D) Efficient training 15 / 45 15) What does “hyperparameter optimization” involve? A) Simplifying feature selection B) Increasing model complexity C) Reducing training time D) Selecting optimal parameters 16 / 45 16) Which algorithm is primarily used for clustering? A) Linear Regression B) K-means clustering C) Random Forest D) Decision Tree 17 / 45 17) What is “ensemble learning” in machine learning? A) Reduces the dimensionality of data B) Combines multiple models C) Increases model complexity D) Simplifies feature selection 18 / 45 18) What is “decision boundary” in classification? A) Error threshold for classification B) Surface separating different classes C) Number of iterations D) Model's stopping criterion 19 / 45 19) What is “R-squared” in regression models? A) Increases model complexity B) The sum of squared residuals C) Proportion of variance explained D) Measures overfitting 20 / 45 20) What is the role of the loss function in machine learning models? A) Measures accuracy B) Measures loss C) Measures the training time D) Measures the error between predictions and true values 21 / 45 21) What is the advantage of “mini-batch gradient descent”? A) Increases model complexity B) Improves interpretability C) Faster convergence D) Reduces overfitting 22 / 45 22) What is a hyperparameter in machine learning? A) A parameter set before training B) A parameter tuned after training C) A value determined by the model D) A value set by the model during training 23 / 45 23) What is the effect of increasing the depth of a decision tree? A) Potential overfitting B) Decrease variance C) Potential overfitting D) Improve interpretability 24 / 45 24) What is “decision tree pruning” used for? A) Combines weak learners B) Reduces overfitting C) Increases the learning rate D) Increases model complexity 25 / 45 25) What is a decision boundary in machine learning? A) Separates classes B) Separates classes C) Measures prediction accuracy D) Defines the model complexity 26 / 45 26) What is the goal of “transfer learning” in machine learning? A) Fine-tune a model for related tasks B) Train multiple models simultaneously C) Enhance model interpretability D) Perform feature scaling 27 / 45 27) What is the purpose of stratified sampling in data collection? A) Represent subgroups B) Randomly sample all data C) Represent subgroups D) Ignore certain classes 28 / 45 28) What is the purpose of a hyperparameter in ML? A) Determine training data B) Tune model performance C) Tune model performance D) Control feature selection 29 / 45 29) What is “recall” in classification tasks? A) Ratio of true positives to actual positives B) Ratio of true negatives to total positives C) Ratio of false negatives to true positives D) Ratio of false positives to total positives 30 / 45 30) What does “AUC” represent in ROC analysis? A) Evaluates training speed B) Represents model complexity C) Measures classifier performance D) Indicates data quality 31 / 45 31) What is the purpose of the “cost function” in supervised learning? A) Selects the optimal hyperparameters B) Improves computational efficiency C) Reduces overfitting D) Measures prediction error 32 / 45 32) What does overfitting mean in ML? A) Model learns noise B) Model is too simple C) Model learns noise D) Model underperforms 33 / 45 33) What is the purpose of using a confusion matrix? A) Summary of prediction results B) Summary of prediction results C) Calculates loss function D) Measures model accuracy 34 / 45 34) What is “underfitting” in machine learning? A) Model has too many features B) Model is too simple C) Model is too complex D) Model has high variance 35 / 45 35) Which algorithm is most suitable for dimensionality reduction? A) PCA B) K-means C) SVM D) Random Forest 36 / 45 36) What is the purpose of the “learning rate” in training neural networks? A) Controls weight updates B) Controls the number of neurons C) Determines the number of features D) Determines model complexity 37 / 45 37) What is “bagging” in ensemble learning? A) Combines weak learners B) Reduces model complexity C) Selects the best hyperparameters D) Aggregates predictions from multiple models 38 / 45 38) What does “one-hot encoding” achieve? A) Increases model complexity B) Simplifies training process C) Represents categories distinctly D) Reduces data quality 39 / 45 39) Which activation function is commonly used in hidden layers of neural networks? A) Softmax B) Tanh C) Sigmoid D) ReLU 40 / 45 40) What is the purpose of “gradient descent”? A) Increases model complexity B) Minimizes loss function C) Enhances interpretability D) Reduces training time 41 / 45 41) What does “k-fold cross-validation” help with? A) Reduces training time B) Increases data dimensionality C) Assesses model performance D) Simplifies model architecture 42 / 45 42) What is the purpose of “regularization” in machine learning? A) Speeds up convergence B) Increases model complexity C) Enhances feature selection D) Prevents overfitting 43 / 45 43) What does “early stopping” prevent in training models? A) Prevents overfitting B) Selects optimal hyperparameters C) Increases model accuracy D) Reduces training time 44 / 45 44) Which algorithm is used for regression tasks? A) Linear Regression B) K-means clustering C) Decision Trees D) Naive Bayes 45 / 45 45) What does the ROC curve represent in classification? A) Model complexity B) Performance of a classifier C) Performance of a classifier D) Feature importance Your score is LinkedIn Facebook Twitter VKontakte 0% ExamWEB DEVELOPMENT EXAM...Read MoreREACT JS PROGRAMMING EXAM...Read MorePYTHON FULL STACK...Read MorePYTHON EXAMS ...Read MoreMYSQL...Read MoreMACHINE LEARNING...Read MoreJAVASCRIPT PROGRAMMING EXAM...Read MoreJAVA SPRING BOOT...Read MoreJAVA PROGRAMMING EXAM...Read MoreJAVA FULL STACK...Read MoreHTML PROGRAMMING EXAM...Read MoreDEEP LEARNING...Read MoreDATA SCIENCE...Read MoreCSS PROGRAMMING EXAM...Read MoreANGULAR JS PROGRAMMING EXAM...Read More 13Share on WhatsApp6Share on LinkedIn4Share on YouTube9Share on Facebook Comments 0
