admin Oct, Tue, 2024 EXAM DATA SCIENCE 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! DATA SCIENCE Test your DATA SCIENCE 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 role of ‘Random Forest’ in machine learning? A) Linear regression B) Ensemble method C) Single tree model D) Ensemble method 2 / 45 2) What is the role of ‘k-fold cross-validation’ in model evaluation? A) Evaluate model on different subsets of data B) Evaluate model on different subsets of data C) Scale features D) Clean data 3 / 45 3) Which of the following describes the term ‘underfitting’? A) High accuracy B) Model too simple C) Model too complex D) Model too simple 4 / 45 4) Which of the following techniques is used for ‘data imputation’? A) Mean/mode substitution B) K-Means clustering C) Mean/mode substitution D) Decision Trees 5 / 45 5) What is the primary goal of reinforcement learning? A) Classify data B) Maximize cumulative reward C) Minimize errors D) Maximize cumulative reward 6 / 45 6) What does ‘Clustering’ aim to achieve in data analysis? A) Feature extraction B) Group similar data points C) Linear regression D) Group similar data points 7 / 45 7) What is ‘Grid Search’ commonly used for in machine learning? A) Data cleaning B) Hyperparameter tuning C) Hyperparameter tuning D) Feature selection 8 / 45 8) Which of the following is a disadvantage of ‘Neural Networks’? A) Requires large datasets B) Requires large datasets C) High accuracy D) Easy to interpret 9 / 45 9) What is the significance of ‘data visualization’ in data science? A) Data cleaning B) Make data insights accessible and understandable C) Make data insights accessible and understandable D) Data profiling 10 / 45 10) What does the term ‘data-driven decision making’ refer to? A) Consensus decision B) Gut feeling C) Making decisions based on data analysis D) Making decisions based on data analysis 11 / 45 11) What is the main purpose of ‘Outlier Detection’? A) Improve accuracy B) Identify anomalies C) Enhance interpretability D) Identify anomalies 12 / 45 12) What does the term ‘trainable parameters’ refer to in machine learning? A) Data points B) Weights in a model C) Weights in a model D) Features 13 / 45 13) What does ‘ROC Curve’ represent in classification models? A) Feature importance B) True positive rate vs. false positive rate C) True positive rate vs. false positive rate D) Model accuracy 14 / 45 14) What does ‘Outlier Detection’ help identify? A) Unusual data points B) Feature selection C) Data normalization D) Unusual data points 15 / 45 15) What does the term ‘ROC curve’ represent in classification? A) Model accuracy B) Trade-off between sensitivity and specificity C) Trade-off between sensitivity and specificity D) Predictive power 16 / 45 16) Which type of learning is supervised learning? A) Unlabeled data B) Labeled data C) Labeled data D) Reinforcement 17 / 45 17) What does ‘Dropout’ do in neural networks? A) Improve accuracy B) Prevent overfitting C) Prevent overfitting D) Increase complexity 18 / 45 18) Which of the following is a benefit of using deep learning? A) Automatic feature extraction B) Simplicity C) Automatic feature extraction D) Less data required 19 / 45 19) What is the purpose of ‘Cross-Validation’? A) Assess model generalization B) Assess model generalization C) Improve accuracy D) Data cleaning 20 / 45 20) What does ‘data reconciliation’ refer to? A) Data aggregation B) Ensuring data consistency across sources C) Ensuring data consistency across sources D) Data cleaning 21 / 45 21) What is the primary purpose of exploratory data analysis (EDA)? A) Uncover patterns B) Build models C) Uncover patterns D) Clean data 22 / 45 22) What is the purpose of ‘Confusion Matrix’ in model evaluation? A) Evaluate model performance B) Evaluate model performance C) Data cleaning D) Feature extraction 23 / 45 23) What does the term ‘over-sampling’ refer to in data processing? A) Increasing the number of minority class samples B) Increasing the number of minority class samples C) Decreasing majority samples D) Ignoring outliers 24 / 45 24) What is ‘Bagging’ primarily used for in machine learning? A) Increase bias B) Reduce variance C) Reduce variance D) Improve interpretability 25 / 45 25) What is ‘Bagging’ primarily used to improve? A) Model stability B) Model stability C) Data validation D) Model interpretability 26 / 45 26) Which of the following is NOT a technique for dimensionality reduction? A) PCA B) K-Means C) t-SNE D) K-Means 27 / 45 27) What is the aim of ‘Predictive Analytics’? A) Data cleaning B) Feature selection C) Forecast future outcomes D) Forecast future outcomes 28 / 45 28) What is ‘A/B Testing’ used for? A) Data cleaning B) Compare two versions C) Feature selection D) Compare two versions 29 / 45 29) What does ‘Variance Inflation Factor (VIF)’ assess in regression? A) Multicollinearity detection B) Outlier detection C) Multicollinearity detection D) Feature importance 30 / 45 30) What does the term ‘bias-variance tradeoff’ refer to? A) Increase complexity B) Balance between error types C) Data normalization D) Balance between error types 31 / 45 31) What is the role of ‘ensemble methods’ in machine learning? A) Optimize hyperparameters B) Combine predictions from multiple models C) Fit a single model D) Combine predictions from multiple models 32 / 45 32) Which of the following is a non-parametric model? A) Logistic Regression B) Decision Trees C) Decision Trees D) Linear Regression 33 / 45 33) Which of the following is NOT a type of ‘Bias’ in machine learning? A) Sample bias B) Underfitting C) Overfitting D) Overfitting 34 / 45 34) Which of the following techniques is used for ‘Text Preprocessing’? A) Tokenization B) Tokenization C) Feature selection D) Data normalization 35 / 45 35) What does ‘Bagging’ help to improve in ensemble methods? A) Reduces variance B) Increases bias C) Improves interpretability D) Reduces variance 36 / 45 36) What does the term ‘semi-supervised learning’ refer to? A) Only using labeled data B) Using no data C) Combining labeled and unlabeled data D) Combining labeled and unlabeled data 37 / 45 37) What does ‘exploratory data analysis’ (EDA) focus on? A) Discover patterns and insights B) Data cleaning C) Discover patterns and insights D) Model validation 38 / 45 38) Which of the following is an example of a ‘non-linear’ model? A) Neural Networks B) K-Means clustering C) Linear Regression D) Neural Networks 39 / 45 39) What does ‘Feature Scaling’ aim to achieve? A) Normalize features B) Clean data C) Normalize features D) Feature extraction 40 / 45 40) Which metric is often used to evaluate ‘clustering’ algorithms? A) Silhouette Score B) Accuracy C) F1 Score D) Silhouette Score 41 / 45 41) What does ‘k-fold cross-validation’ help prevent? A) Overfitting B) Overfitting C) Underfitting D) Model complexity 42 / 45 42) What is ‘Exploratory Data Analysis’ primarily concerned with? A) Normalize data B) Discover patterns C) Discover patterns D) Validate assumptions 43 / 45 43) Which of the following is NOT a type of neural network? A) Convolutional Neural Network B) Recurrent Neural Network C) Linear Regression D) Linear Regression 44 / 45 44) Which of the following algorithms is used for regression tasks? A) Decision Trees B) Linear Regression C) Linear Regression D) K-Means 45 / 45 45) What is the purpose of using ‘regularization’ in regression models? A) Increase bias B) Prevent overfitting C) Decrease variance D) Prevent overfitting 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
