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 primary purpose of ‘Data Augmentation’? A) Clean data B) Increase dataset size C) Increase dataset size D) Improve accuracy 2 / 45 2) What does ‘Backpropagation’ optimize in neural networks? A) Data cleaning B) Weights in the model C) Weights in the model D) Feature selection 3 / 45 3) What does ‘data augmentation’ involve in machine learning? A) Creating new training samples B) Cleaning data C) Reducing data size D) Creating new training samples 4 / 45 4) What is the purpose of ‘Data Augmentation’ in deep learning? A) Clean data B) Increase training data C) Increase training data D) Reduce overfitting 5 / 45 5) What is the primary purpose of data visualization? A) Store data B) Normalize data C) Communicate insights D) Communicate insights 6 / 45 6) What is ‘Transfer Learning’ primarily used for? A) Feature selection B) Standardize data C) Improve model training with limited data D) Improve model training with limited data 7 / 45 7) What is the purpose of the ROC curve in machine learning? A) Evaluate classification performance B) Assess noise C) Measure accuracy D) Evaluate classification performance 8 / 45 8) What does ‘big data’ refer to? A) Large and complex datasets B) Small datasets C) Simple data D) Large and complex datasets 9 / 45 9) 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 10 / 45 10) What is the difference between precision and recall? A) Precision is always higher than recall B) Precision measures true positives over predicted positives C) Recall measures true positives over actual positives D) Precision measures true positives over predicted positives 11 / 45 11) What is the main purpose of data preprocessing? A) Increase noise B) Improve model performance C) Improve model performance D) Reduce training time 12 / 45 12) What is the primary role of a data scientist? A) Manage IT projects B) Analyze data C) Develop software D) Analyze data 13 / 45 13) Which of the following is NOT a type of clustering algorithm? A) Linear Regression B) Hierarchical Clustering C) K-Means D) Linear Regression 14 / 45 14) What does ‘hyperparameter optimization’ aim to achieve? A) Improve model performance B) Reduce noise C) Increase complexity D) Improve model performance 15 / 45 15) What does ‘data reconciliation’ refer to? A) Data aggregation B) Ensuring data consistency across sources C) Ensuring data consistency across sources D) Data cleaning 16 / 45 16) What is the purpose of ‘LDA’ (Linear Discriminant Analysis)? A) Dimensionality reduction B) Data cleaning C) Feature selection D) Dimensionality reduction 17 / 45 17) What does ‘Transfer Learning’ involve? A) Applying knowledge from one model to another B) Feature selection C) Data cleaning D) Applying knowledge from one model to another 18 / 45 18) What does ‘Dropout’ do in neural networks? A) Improve accuracy B) Prevent overfitting C) Prevent overfitting D) Increase complexity 19 / 45 19) Which of the following is a key advantage of using ‘LSTM’? A) Capture long-term dependencies B) Analyze static data C) Capture long-term dependencies D) Reduce complexity 20 / 45 20) Which of the following techniques is used for feature scaling? A) K-Means B) Decision Trees C) Standardization D) Standardization 21 / 45 21) What does ‘SVM’ stand for in machine learning? A) Support Vector Machine B) Standard Variance Model C) Support Vector Machine D) Statistical Variance Model 22 / 45 22) What does ‘Bayesian Statistics’ focus on? A) Prior knowledge incorporation B) Prior knowledge incorporation C) Random sampling D) Hypothesis testing 23 / 45 23) What does ‘Data Imbalance’ refer to? A) Unequal class distribution B) Feature extraction C) Unequal class distribution D) Data cleaning 24 / 45 24) What is ‘Dimensionality Reduction’ mainly used for in data science? A) Reduce feature space B) Increase complexity C) Reduce feature space D) Improve data quality 25 / 45 25) What is ‘Feature Engineering’ primarily concerned with? A) Creating new features B) Data cleaning C) Creating new features D) Model evaluation 26 / 45 26) Which of the following is NOT a metric used in regression analysis? A) R-squared B) Root Mean Squared Error C) R-squared D) Mean Absolute Error 27 / 45 27) Which of the following is a common data visualization tool? A) Tableau B) MySQL C) Python D) Tableau 28 / 45 28) What does ‘support vector’ refer to in SVM? A) Outliers B) Data points that define the margin C) Data points that define the margin D) All data points 29 / 45 29) Which type of learning is supervised learning? A) Unlabeled data B) Labeled data C) Labeled data D) Reinforcement 30 / 45 30) Which of the following algorithms is suitable for multi-class classification? A) K-Means B) Softmax Regression C) Softmax Regression D) Linear Regression 31 / 45 31) Which of the following is an example of ‘Supervised Learning’? A) Data cleaning B) Clustering C) Predicting house prices D) Predicting house prices 32 / 45 32) What is ‘XGBoost’ primarily known for? A) K-Means clustering B) Gradient boosting C) Gradient boosting D) Support Vector Machines 33 / 45 33) What is the goal of dimensionality reduction? A) Decrease accuracy B) Increase noise C) Simplify models D) Simplify models 34 / 45 34) What is the primary goal of ‘clustering algorithms’? A) Group similar data points B) Predict outcomes C) Validate models D) Group similar data points 35 / 45 35) What is the primary purpose of ‘data exploration’? A) Discover patterns and insights B) Discover patterns and insights C) Train algorithms D) Validate models 36 / 45 36) What is ‘Bagging’ primarily used for in machine learning? A) Increase bias B) Reduce variance C) Reduce variance D) Improve interpretability 37 / 45 37) Which of the following is a common use of ‘Neural Networks’? A) Data normalization B) Image recognition C) Image recognition D) Data cleaning 38 / 45 38) In machine learning, what is the term ‘label’? A) The input variable B) Unused variable C) The output variable D) The output variable 39 / 45 39) What is ‘Data Cleaning’ primarily focused on? A) Data normalization B) Improve data quality C) Feature selection D) Improve data quality 40 / 45 40) What does ‘PCA’ stand for in data analysis? A) Principal Component Analysis B) Partial Component Analysis C) Principal Component Analysis D) Primary Component Analysis 41 / 45 41) What is ‘Bootstrapping’ used for in statistics? A) Estimate sampling distribution B) Feature selection C) Estimate sampling distribution D) Clean data 42 / 45 42) What does ‘label encoding’ refer to in data preprocessing? A) Normalizing data B) Converting categorical data into numeric values C) Converting categorical data into numeric values D) Data cleaning 43 / 45 43) Which of the following is NOT a technique for dimensionality reduction? A) PCA B) K-Means C) t-SNE D) K-Means 44 / 45 44) What is the role of ‘Transfer Learning’ in machine learning? A) Apply knowledge from one domain to another B) Apply knowledge from one domain to another C) Data cleaning D) Feature extraction 45 / 45 45) What does the term ‘data aggregation’ refer to? A) Combining data for summary statistics B) Data cleaning C) Data profiling D) Combining data for summary statistics 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
