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 the function of “k-nearest neighbors”? A) Enhances data quality B) Aggregates predictions from multiple models C) Optimizes hyperparameters D) Classifies based on nearest neighbors 2 / 45 2) What does “precision” measure in classification models? A) Proportion of false negatives B) Proportion of true positives C) Total number of correct predictions D) Number of correctly predicted instances 3 / 45 3) What is cross-validation? A) Regularization B) Cross-validation C) Batch normalization D) Dropout 4 / 45 4) What is the purpose of normalization in ML? A) Increase data dimensionality B) Optimize model interpretability C) Scale features to similar range D) Simplify feature selection 5 / 45 5) Which method is used to avoid overfitting in machine learning? A) Regularization B) Cross-validation C) Batch normalization D) Dropout 6 / 45 6) What does the term “bagging” mean in ensemble learning? A) Improve accuracy B) Train models on subsets C) Train models on subsets D) Increase bias 7 / 45 7) Which activation function is commonly used in hidden layers of neural networks? A) Softmax B) Tanh C) Sigmoid D) ReLU 8 / 45 8) What is the main function of an activation function? A) Introduces non-linearity B) Introduces non-linearity C) Optimizes weights D) Adjusts learning rate 9 / 45 9) What is “ensemble learning”? A) Reduces data dimensionality B) Increases model complexity C) Simplifies model interpretation D) Combines multiple models 10 / 45 10) What is “data augmentation” in deep learning? A) Combines weak learners B) Increases model complexity C) Reduces overfitting D) Increases training data size 11 / 45 11) What is the main goal of clustering in machine learning? A) Separate dissimilar data points B) Identify outliers C) Group similar data points D) Group similar data points 12 / 45 12) What is the purpose of the “rectified linear unit” (ReLU) activation? A) Increases learning rate B) Outputs zero for negative inputs C) Outputs zero for positive inputs D) Reduces the number of layers 13 / 45 13) What is “one-hot encoding” used for in machine learning? A) Transforms categorical variables into binary vectors B) Reduces dimensionality C) Combines weak learners D) Increases model accuracy 14 / 45 14) Which method is used to avoid overfitting in machine learning? A) Regularization B) Cross-validation C) Batch normalization D) Dropout 15 / 45 15) What is “backpropagation” in neural networks? A) Reduces overfitting B) Increases dimensionality C) Updates neural network weights D) Selects the best model 16 / 45 16) What is the main benefit of using a convolutional neural network (CNN)? A) Effective for image data B) Easier to implement C) Effective for image data D) Require less data 17 / 45 17) What is the role of “learning rate” in optimization algorithms? A) Reduces overfitting B) Selects the best model C) Controls step size for weight updates D) Measures feature importance 18 / 45 18) What is k-fold cross-validation used for? A) Reduce model complexity B) Reduce variance C) Assess model performance D) Assess model performance 19 / 45 19) What is the difference between classification and regression? A) Classification predicts categories B) Classification predicts categories C) Regression predicts categories D) Both are the same 20 / 45 20) What is “t-SNE” (t-Distributed Stochastic Neighbor Embedding) used for? A) Visualizes high-dimensional data B) Combines weak learners C) Reduces overfitting D) Increases model accuracy 21 / 45 21) What is “bagging” in machine learning? A) Reduces data quality B) Improves model accuracy C) Optimizes hyperparameters D) Increases model complexity 22 / 45 22) What is the function of a neuron in a neural network? A) Transmits information B) Receives output C) Processes inputs and outputs a signal D) Processes inputs and outputs a signal 23 / 45 23) What does “regularization” prevent in machine learning? A) Prevents overfitting B) Enhances interpretability C) Increases model complexity D) Reduces training time 24 / 45 24) 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 25 / 45 25) What is “random search” in hyperparameter tuning? A) Tunes hyperparameters sequentially B) Selects parameters based on gradients C) Randomly selects hyperparameter combinations D) Trains models with different datasets 26 / 45 26) What is “early stopping” in machine learning? A) Stops training when validation performance decreases B) Reduces feature complexity C) Increases the number of iterations D) Reduces training time 27 / 45 27) 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 28 / 45 28) What is “dropout” used for in neural networks? A) Prevents overfitting by ignoring neurons B) Reduces the number of features C) Increases model accuracy D) Reduces training time 29 / 45 29) What is the difference between L1 and L2 regularization? A) L1 adds squared penalties, L2 adds absolute value penalties B) L1 reduces variance C) L1 adds absolute value penalties, L2 adds squared penalties D) Both are the same 30 / 45 30) Which algorithm is commonly used for clustering? A) Learning through environment interaction B) Learning through rewards and penalties C) Learning without supervision D) Learning with labeled data 31 / 45 31) What is regularization in machine learning? A) PCA B) K-means C) SVM D) Random Forest 32 / 45 32) What is “validation set” used for in machine learning? A) Trains the model B) Tunes model hyperparameters C) Used for final evaluation of the model D) Reduces dimensionality 33 / 45 33) What is “random search” in hyperparameter tuning? A) Randomly selects hyperparameters B) Increases model accuracy C) Combines weak learners D) Exhaustively searches hyperparameters 34 / 45 34) What is the purpose of “dropout” in neural networks? A) Reduces the learning rate B) Randomly removes neurons C) Reduces training time D) Increases model complexity 35 / 45 35) Which algorithm is used for regression tasks? A) Linear Regression B) K-means clustering C) Decision Trees D) Naive Bayes 36 / 45 36) What is the difference between supervised and unsupervised learning? A) Measures accuracy B) Measures loss C) Measures the training time D) Measures the error between predictions and true values 37 / 45 37) What is the main objective of clustering algorithms? A) Reduce training time B) Optimize model performance C) Classify labeled data D) Group similar data 38 / 45 38) What is the purpose of “gradient descent”? A) Minimize loss function B) Train the model faster C) Increase model complexity D) Normalize input features 39 / 45 39) What is the purpose of k-nearest neighbors (KNN)? A) Instance-based learning B) Supervised learning C) Instance-based learning D) Unsupervised learning 40 / 45 40) 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 41 / 45 41) What does the term ‘bias’ mean in machine learning? A) Sigmoid B) Tanh C) ReLU D) Softmax 42 / 45 42) What does “ensemble learning” refer to in machine learning? A) Increase data dimensionality B) Combine multiple models C) Combine multiple models D) Reduce model complexity 43 / 45 43) What is a latent variable in machine learning? A) Hidden variable B) Hidden variable C) Label D) Feature variable 44 / 45 44) What is the function of “ReLU” in neural networks? A) Increases model complexity B) Simplifies model structure C) Reduces training time D) Introduces non-linearity 45 / 45 45) What is “ridge regression” in machine learning? A) Adds penalty for large coefficients B) Increases the model's accuracy C) Selects the most important features D) Reduces model complexity 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 14Share on WhatsApp10Share on LinkedIn5Share on YouTube9Share on Facebook Comments 0