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 a perceptron in neural networks? A) Simple binary classification model B) Simple binary classification model C) Performs classification tasks D) Performs regression tasks 2 / 45 2) What is the primary focus of “predictive analytics”? A) Increases model complexity B) Simplifies feature selection C) Enhances data quality D) Identifies future outcomes 3 / 45 3) What is “data augmentation” in image processing? A) Enhances model complexity B) Simplifies model structure C) Increases training speed D) Expands dataset size 4 / 45 4) Which algorithm is used for clustering in machine learning? A) K-means B) Decision Trees C) Logistic Regression D) Support Vector Machines 5 / 45 5) 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 6 / 45 6) 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 7 / 45 7) What is regularization in machine learning? A) PCA B) K-means C) SVM D) Random Forest 8 / 45 8) 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 9 / 45 9) What is “stochastic gradient descent” (SGD) used for? A) Reduces overfitting B) Updates model with small batch C) Selects the best model D) Reduces the learning rate 10 / 45 10) What is the main goal of unsupervised learning? A) Identify patterns in unlabeled data B) Train on labeled data C) Predict labels D) Identify patterns in unlabeled data 11 / 45 11) What is “dropout” in neural networks? A) Reducing learning rate B) Randomly dropping neurons C) Removing outliers D) Increasing neurons 12 / 45 12) What is the function of “gradient boosting”? A) Builds models in parallel B) Reduces the dimensionality of the model C) Builds models sequentially D) Decreases overfitting 13 / 45 13) Which activation function is commonly used in hidden layers of neural networks? A) Softmax B) Tanh C) Sigmoid D) ReLU 14 / 45 14) 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 15 / 45 15) What is cross-validation? A) Regularization B) Cross-validation C) Batch normalization D) Dropout 16 / 45 16) What is the primary goal of a “regression model”? A) Minimize model complexity B) Classify discrete outcomes C) Predict continuous outcomes D) Optimize data preprocessing 17 / 45 17) What is the main goal of supervised 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 18 / 45 18) What is the purpose of early stopping in model training? A) Train the model longer B) Halt training when model stops improving C) Increase model bias D) Halt training when model stops improving 19 / 45 19) Which algorithm is used for market basket analysis? A) K-means B) Random Forest C) Apriori D) Apriori 20 / 45 20) What is k-fold cross-validation used for? A) Reduce model complexity B) Reduce variance C) Assess model performance D) Assess model performance 21 / 45 21) What does “transfer learning” refer to in ML? A) Use pre-trained model B) Use pre-trained model C) Train from scratch D) Optimize hyperparameters 22 / 45 22) What is the primary purpose of “cross-validation”? A) Assess model generalization B) Optimize hyperparameters C) Increase the training data size D) Reduce overfitting 23 / 45 23) 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 24 / 45 24) What is the goal of “predictive modeling”? A) Enhances data quality B) Makes predictions about future events C) Simplifies feature selection D) Increases model complexity 25 / 45 25) What is “stochastic gradient descent”? A) Increases data complexity B) Increases convergence speed C) Reduces training accuracy D) Simplifies model structure 26 / 45 26) What is a hyperplane in SVM? A) Linear regression line B) Data point C) Decision boundary D) Feature vector 27 / 45 27) What does “hyperparameter optimization” involve? A) Simplifying feature selection B) Increasing model complexity C) Reducing training time D) Selecting optimal parameters 28 / 45 28) 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 29 / 45 29) What is the key idea behind “support vector machines” (SVM)? A) Maximizes margin between classes B) Reduces dimensionality C) Minimizes the number of features D) Increases model complexity 30 / 45 30) What does “feature scaling” involve in data preprocessing? A) Reducing dimensionality B) Aggregating data features C) Scaling features to a specific range D) Selecting the most important features 31 / 45 31) What does “regularization” help prevent? A) Simplifies feature selection B) Reduces data quality C) Increases model complexity D) Prevents overfitting 32 / 45 32) What is a recommendation system? A) Cluster similar items B) Perform regression C) Predict user preferences D) Predict user preferences 33 / 45 33) Which algorithm is most suitable for dimensionality reduction? A) PCA B) K-means C) SVM D) Random Forest 34 / 45 34) What is the purpose of “data augmentation” in image processing? A) Reduces image complexity B) Reduces dimensionality C) Increases regularization strength D) Increases training data size artificially 35 / 45 35) What does the “learning rate” control in machine learning? A) Amount of regularization B) Step size for parameter updates C) Number of training epochs D) Number of features 36 / 45 36) What does “dimensionality reduction” aim to achieve? A) Increases the complexity of the model B) Normalizes feature scales C) Reduces the number of features D) Increases model training time 37 / 45 37) What is cross-entropy loss used for in machine learning? A) Measure classification performance B) Perform regression C) Measure classification performance D) Perform dimensionality reduction 38 / 45 38) What is a kernel trick in support vector machines (SVM)? A) Improve model speed B) Transform data into higher dimensions C) Adjust the bias D) Transform data into higher dimensions 39 / 45 39) What is feature engineering? A) Improve model performance B) Simplify model complexity C) Reduce data dimensionality D) Improve model performance 40 / 45 40) What is regularization in machine learning? A) PCA B) K-means C) SVM D) Random Forest 41 / 45 41) What does “underfitting” mean in machine learning? A) Too complex model B) Too simple model C) Too simple model D) High training error 42 / 45 42) What is the role of “data preprocessing”? A) Reduces training time B) Simplifies model structure C) Prepares data for analysis D) Increases data dimensionality 43 / 45 43) What does “one-hot encoding” accomplish in preprocessing? A) Normalizes numerical features B) Transforms categorical data to binary C) Reduces dimensionality D) Combines multiple features 44 / 45 44) 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 45 / 45 45) What is the use of the gradient descent algorithm in machine learning? A) Maximize accuracy B) Minimize loss function C) Minimize loss function D) Increase 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 13Share on WhatsApp6Share on LinkedIn4Share on YouTube9Share on Facebook Comments 0
