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 ‘Data Mining’ primarily concerned with? A) Data validation B) Extracting patterns from data C) Model evaluation D) Extracting patterns from data 2 / 45 2) What does the term ‘gradient descent’ refer to? A) Data cleaning B) Optimization algorithm C) Model evaluation D) Optimization algorithm 3 / 45 3) What is the primary focus of ‘Time Series Forecasting’? A) Feature selection B) Data cleaning C) Predict future values D) Predict future values 4 / 45 4) What does ‘KNN’ stand for in machine learning? A) K-Nearest Neighbors B) K-Means C) Linear Regression D) K-Nearest Neighbors 5 / 45 5) What is the main advantage of using ‘XGBoost’? A) High performance and speed B) High performance and speed C) Low accuracy D) Simpler models 6 / 45 6) Which of the following is a technique for ‘Feature Selection’? A) Data cleaning B) Data normalization C) Recursive Feature Elimination D) Recursive Feature Elimination 7 / 45 7) What does ‘Dimensionality Curse’ refer to? A) Overfitting B) Increased complexity C) Difficulty in visualization D) Difficulty in visualization 8 / 45 8) What is the aim of ‘Text Mining’? A) Extract information from text B) Extract information from text C) Clean data D) Feature selection 9 / 45 9) What is the main benefit of ‘k-fold cross-validation’? A) More reliable model performance B) Model interpretability C) Data profiling D) More reliable model performance 10 / 45 10) What is the purpose of the confusion matrix? A) Evaluate classification performance B) Measure regression accuracy C) Assess clustering D) Evaluate classification performance 11 / 45 11) What is the significance of ‘time series forecasting’? A) Analyze static data B) Predict future values C) Predict future values D) Feature selection 12 / 45 12) Which of the following is a characteristic of ‘Deep Learning’? A) Multiple layers B) Shallow networks C) Supervised learning D) Multiple layers 13 / 45 13) What does ‘T-test’ compare in statistics? A) Variances B) Means of two groups C) Means of two groups D) Distributions 14 / 45 14) What is the primary goal of ‘A/B testing’? A) Clean data B) Compare two variations C) Compare two variations D) Model validation 15 / 45 15) What does ‘data pipeline’ refer to in data engineering? A) Data cleaning B) Series of data processing steps C) Data visualization D) Series of data processing steps 16 / 45 16) Which of the following best describes ‘recurrent neural networks’ (RNN)? A) Handle sequential data B) Predict categorical outcomes C) Analyze static data D) Handle sequential data 17 / 45 17) What does the term ‘loss function’ refer to in machine learning? A) Measure of model performance B) Data augmentation C) Measure of model performance D) Data normalization 18 / 45 18) What does the term ‘feature vector’ refer to? A) Representation of an object in a multi-dimensional space B) Output of a model C) Representation of an object in a multi-dimensional space D) Single data point 19 / 45 19) Which of the following is a disadvantage of ‘Decision Trees’? A) Robust to noise B) Prone to overfitting C) Prone to overfitting D) Easy to interpret 20 / 45 20) 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 21 / 45 21) Which of the following is a supervised learning technique? A) Decision Trees B) DBSCAN C) K-Means D) Decision Trees 22 / 45 22) What does ‘Dimensionality Reduction’ achieve in machine learning? A) Simplifies datasets B) Simplifies datasets C) Improves accuracy D) Increases variance 23 / 45 23) What does ‘Time Series Analysis’ help to analyze? A) Temporal data trends B) Clean data C) Temporal data trends D) Feature selection 24 / 45 24) What is ‘tuning’ in the context of neural networks? A) Adjusting hyperparameters B) Data augmentation C) Feature selection D) Adjusting hyperparameters 25 / 45 25) What is the purpose of data visualization? A) Enhance understanding B) Store data C) Enhance understanding D) Clean data 26 / 45 26) What is the purpose of ‘Data Sampling’? A) Clean data B) Reduce dataset size C) Enhance model accuracy D) Reduce dataset size 27 / 45 27) What does ‘LSTM’ stand for in deep learning? A) Logistic Regression B) Long Short-Term Memory C) Long Short-Term Memory D) Linear Support Vector Machine 28 / 45 28) What does the term ‘confidence interval’ indicate? A) Range of values for an estimate B) Range of values for an estimate C) Outlier detection D) Variance in data 29 / 45 29) What does ‘Bayesian Inference’ allow us to update? A) Beliefs with new evidence B) Beliefs with new evidence C) Feature selection D) Data cleaning 30 / 45 30) Which of the following is a common clustering algorithm? A) Logistic Regression B) K-Means C) K-Means D) Linear Regression 31 / 45 31) What is the purpose of ‘Data Augmentation’ in deep learning? A) Clean data B) Increase training data C) Increase training data D) Reduce overfitting 32 / 45 32) What does ‘Data Leakage’ refer to? A) Using training data in testing B) Feature selection C) Using training data in testing D) Data cleaning 33 / 45 33) 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 34 / 45 34) Which type of learning is supervised learning? A) Unlabeled data B) Labeled data C) Labeled data D) Reinforcement 35 / 45 35) What is the purpose of ‘Feature Scaling’? A) Feature extraction B) Data cleaning C) Normalize feature values D) Normalize feature values 36 / 45 36) What does ‘Confusion Matrix’ represent in classification? A) Feature selection B) True vs. predicted classifications C) Data cleaning D) True vs. predicted classifications 37 / 45 37) Which of the following is a common data visualization tool? A) Tableau B) MySQL C) Python D) Tableau 38 / 45 38) Which algorithm is commonly used for natural language processing? A) RNN B) RNN C) Linear Regression D) K-Means 39 / 45 39) What is ‘Natural Language Processing’ primarily used for? A) Feature selection B) Data cleaning C) Understand human language D) Understand human language 40 / 45 40) What does the term ‘natural language generation’ refer to? A) Data visualization B) Analyzing text C) Creating human-like text from data D) Creating human-like text from data 41 / 45 41) What does the term ‘training loss’ refer to in neural networks? A) Error on validation data B) Error on training data C) Test error D) Error on training data 42 / 45 42) What does the term ‘precision’ refer to in classification tasks? A) True positive rate among predicted positives B) True positive rate among predicted positives C) Accuracy of predictions D) True positive rate among all 43 / 45 43) Which of the following is an example of a continuous variable? A) Age B) Temperature C) Temperature D) Number of students 44 / 45 44) Which of the following is NOT a characteristic of decision trees? A) Easy to interpret B) Overfitting prone C) Overfitting prone D) Non-linear relationships 45 / 45 45) What does ‘Bagging’ help to improve in ensemble methods? A) Reduces variance B) Increases bias C) Improves interpretability D) Reduces variance 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
