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 “grid search” in hyperparameter optimization? A) Increases the model's complexity B) Randomly selects hyperparameters C) Reduces model variance D) Exhaustively searches hyperparameters 2 / 45 2) 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 3 / 45 3) What does “transfer learning” allow? A) Reduces model complexity B) Simplifies data representation C) Leverages knowledge from other domains D) Increases training time 4 / 45 4) What is the purpose of “data wrangling”? A) Enhances interpretability B) Simplifies model structure C) Increases data complexity D) Cleans and transforms raw data 5 / 45 5) What is a confusion matrix? A) Measures accuracy B) Describes classification performance C) Describes classification performance D) Measures model complexity 6 / 45 6) What is “k-nearest neighbors” (k-NN) algorithm used for? A) Reduces dimensionality B) Combines weak learners C) Classifies data based on nearest neighbors D) Increases the learning rate 7 / 45 7) What is the primary goal of supervised learning? A) Reduce training time B) Learn from labeled data C) Optimize model complexity D) Learn from unlabeled data 8 / 45 8) What is the purpose of the “kernel trick” in SVM? A) Reduces overfitting B) Enables SVMs to work in higher dimensions C) Increases feature selection D) Reduces model complexity 9 / 45 9) Which activation function is commonly used in deep learning? A) Sigmoid B) Tanh C) ReLU D) Softmax 10 / 45 10) What does “outlier detection” aim to achieve? A) Increases model complexity B) Simplifies feature selection C) Reduces training time D) Improves data quality 11 / 45 11) 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 12 / 45 12) What is the purpose of “backpropagation”? A) Simplify model structure B) Improve training data C) Optimize neural network weights D) Increase model complexity 13 / 45 13) 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 14 / 45 14) What is the purpose of “grid search” in machine learning? A) Reduces dimensionality B) Exhaustive search for hyperparameters C) Reduces overfitting D) Measures feature importance 15 / 45 15) What does the term ‘bias’ mean in machine learning? A) Sigmoid B) Tanh C) ReLU D) Softmax 16 / 45 16) What is the role of “feature scaling” in machine learning? A) Reduces dimensionality B) Reduces the number of features C) Normalizes the feature ranges D) Increases model complexity 17 / 45 17) What does “feature importance” refer to? A) Indicate influential features B) Measure overall accuracy C) Indicate influential features D) Optimize hyperparameters 18 / 45 18) What does “ensemble learning” refer to? A) Combine multiple models B) Combine multiple models C) Simplify model interpretation D) Increase data dimensionality 19 / 45 19) What does “hyperparameter tuning” involve? A) Selecting features B) Balancing the dataset C) Optimizing learning control parameters D) Training the model 20 / 45 20) What is the role of “feature importance” in decision trees? A) Quantifies contribution of features B) Measures accuracy C) Increases model complexity D) Reduces the number of features 21 / 45 21) 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 22 / 45 22) What is the main difference between classification and regression? 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 23 / 45 23) What is the purpose of the learning rate in neural networks? A) A method to adjust weights B) An algorithm to maximize accuracy C) A method to classify data D) An optimization algorithm to minimize loss 24 / 45 24) What does “underfitting” indicate in a model? A) Model is too simple B) Model has high variance C) Model is too complex D) Model performs well on training data 25 / 45 25) What does “AUC” measure in model evaluation? A) Simplifies model structure B) Measures overall classifier performance C) Enhances interpretability D) Increases training speed 26 / 45 26) What is “data augmentation” in image processing? A) Enhances model complexity B) Simplifies model structure C) Increases training speed D) Expands dataset size 27 / 45 27) Which algorithm is best for classification problems? A) K-means B) Decision Trees C) Logistic Regression D) Support Vector Machines 28 / 45 28) 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 29 / 45 29) What is a learning rate in the context of gradient descent? A) Increases variance B) Determines weight adjustment C) Determines weight adjustment D) Reduces model bias 30 / 45 30) What does a learning curve represent in machine learning? A) Plot of training vs validation error B) Plot of loss function C) Plot of training vs validation error D) Plot of accuracy over time 31 / 45 31) What is gradient boosting used for? A) Reduce bias B) Increase model interpretability C) Reduce bias D) Reduce variance 32 / 45 32) What is the use of “ensemble methods” in machine learning? A) Simplifies model complexity B) Enhances data quality C) Improves accuracy and robustness D) Reduces training time 33 / 45 33) What is “overfitting” in machine learning? A) Model performs equally on all datasets B) Model underperforms on training data C) Model generalizes well to all data D) Model performs well on training, poorly on test data 34 / 45 34) What is the purpose of data normalization? A) Rescale data to a standard range B) Increase variance C) Remove outliers D) Rescale data to a standard range 35 / 45 35) What is the purpose of “grid search” in hyperparameter tuning? A) Reduces model complexity B) Exhaustively searches hyperparameters C) Randomly selects hyperparameters D) Combines weak learners 36 / 45 36) What is a recurrent neural network (RNN)? A) Process sequential data B) Process sequential data C) Reduce training time D) Increase model complexity 37 / 45 37) What is “AdaBoost” in ensemble learning? A) Combines weak learners B) Increases feature complexity C) Combines strong learners D) Reduces dimensionality 38 / 45 38) What is overfitting in machine learning? A) To map inputs to known outputs B) To predict future values C) To classify data into categories D) To minimize the loss function 39 / 45 39) What is the primary function of “validation set”? A) Increases data complexity B) Enhances interpretability C) Evaluates model performance during training D) Simplifies model structure 40 / 45 40) What is “L1 regularization” also known as? A) Elastic Net B) Lasso regularization C) Bayesian regularization D) Ridge regularization 41 / 45 41) What does the ROC curve represent in classification? A) Model complexity B) Performance of a classifier C) Performance of a classifier D) Feature importance 42 / 45 42) What is the purpose of “decision trees” in machine learning? A) Combines weak learners B) Reduces dimensionality C) Increases generalization ability D) Splits data into decision nodes 43 / 45 43) What is the “elbow method” in clustering analysis? A) Reduces overfitting B) Increases model complexity C) Measures feature importance D) Finds optimal number of clusters 44 / 45 44) What is “Ridge Regression” used for? A) Increases model complexity B) Selects relevant features C) Penalizes large coefficients D) Reduces the dimensionality of features 45 / 45 45) 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 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 16Share on WhatsApp10Share on LinkedIn5Share on YouTube9Share on Facebook Comments 0
