Data Science and AI: Key Concepts and Tools MCQ Test
Questions: 30
Questions
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1. What is the primary goal of data science?
- a) To manipulate data for financial profit
- b) To store large datasets
- c) To create complex mathematical models only
- d) To convert raw data into valuable insights and predictions
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2. Which of the following is a common tool used for data visualization?
- a) Jupyter Notebook
- b) Tableau
- c) TensorFlow
- d) Hadoop
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3. What does the term 'Big Data' refer to?
- a) Large volumes of data that are too complex for traditional processing tools
- b) Small sets of structured data
- c) Data that only businesses can access
- d) Only unstructured data
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4. Which of the following is an essential skill for data scientists?
- a) Graphic design
- b) Data cleaning and preprocessing
- c) Writing and editing documents
- d) Social media management
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5. Which AI technique is primarily used in supervised learning?
- a) Neural Networks
- b) Deep Learning
- c) K-means Clustering
- d) Decision Trees
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6. Which of the following is an example of unstructured data?
- a) Customer age data
- b) Audio recordings
- c) Data from a database
- d) Excel spreadsheets
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7. What is a feature in a dataset?
- a) The label or target variable
- b) A row in the dataset
- c) A column that holds measurable characteristics
- d) A group of algorithms
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8. Which of the following is an example of supervised learning?
- a) K-means clustering
- b) Linear regression
- c) Principal Component Analysis
- d) Reinforcement learning
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9. Which of the following algorithms is used for classification tasks in machine learning?
- a) K-means clustering
- b) Support Vector Machines
- c) Linear Regression
- d) K-Nearest Neighbors
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10. What is the purpose of normalization in data preprocessing?
- a) To scale features so they have a standard range
- b) To remove irrelevant data
- c) To categorize data into groups
- d) To delete duplicate entries
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11. What type of machine learning problem does the 'K-means clustering' algorithm solve?
- a) Regression
- b) Classification
- c) Unsupervised learning (clustering)
- d) Reinforcement learning
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12. What is the purpose of the confusion matrix in machine learning?
- a) To track the performance of a machine learning model
- b) To visualize the model’s loss function
- c) To test model predictions with multiple metrics
- d) To analyze model errors in classification tasks
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13. Which of the following tools is used for data wrangling and cleaning?
- a) Scikit-learn
- b) Pandas
- c) Matplotlib
- d) TensorFlow
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14. What does 'feature engineering' refer to in machine learning?
- a) Selecting and transforming raw data into meaningful input features for models
- b) The process of selecting the appropriate machine learning algorithm
- c) Cleaning the data by removing null values
- d) Reducing the number of features for simpler models
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15. Which of the following is a key benefit of using big data tools like Hadoop?
- a) It simplifies data analysis by only allowing structured data
- b) It speeds up the process of writing and editing code
- c) It allows for processing of very large datasets across multiple servers
- d) It creates automated content for websites
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16. What is the main function of natural language processing (NLP) in AI?
- a) To classify images based on visual features
- b) To process and analyze human language data
- c) To create recommendation systems
- d) To predict stock market trends
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17. Which technique is used to improve the generalization of a machine learning model?
- a) Data augmentation
- b) Deleting features
- c) Using only a small sample of data
- d) Simplifying the algorithm
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18. Which of the following libraries is commonly used for building machine learning models in Python?
- a) Pandas
- b) NumPy
- c) Scikit-learn
- d) Flask
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19. What is the purpose of a loss function in machine learning?
- a) To measure how well the model’s predictions align with the actual data
- b) To select the best features for training
- c) To perform feature scaling
- d) To visualize data points in 3D
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20. Which of the following is an example of reinforcement learning?
- a) A robot learning to play a game by receiving rewards for correct actions
- b) Clustering similar images together
- c) Predicting housing prices based on features
- d) Sorting products in a warehouse
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21. What does the term 'dimensionality reduction' refer to in data science?
- a) Reducing the amount of noise in the dataset
- b) Reducing the number of input features while preserving data information
- c) Increasing the size of the dataset
- d) Removing outliers from the dataset
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22. What is the purpose of the "train-test split" in machine learning?
- a) To divide the data into training and testing sets to evaluate model performance
- b) To reduce the size of the dataset
- c) To create labels for unstructured data
- d) To increase the amount of data available for analysis
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23. What does a 'decision tree' algorithm do in machine learning?
- a) It organizes data into hierarchical structures for classification or regression tasks
- b) It creates a random set of data points
- c) It stores large amounts of data in a structured format
- d) It builds models for text analysis
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24. Which of the following is a common evaluation metric for classification models?
- a) Mean squared error
- b) Accuracy
- c) Precision
- d) All of the above
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25. What does the term "hyperparameter tuning" refer to?
- a) Adjusting the features of the dataset
- b) Selecting the right machine learning model
- c) Fine-tuning model parameters to improve performance
- d) Increasing the size of the training data
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26. Which of the following is an example of unsupervised learning?
- a) K-means clustering
- b) Linear regression
- c) Random forests
- d) Naive Bayes
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27. What is the primary purpose of cross-validation in machine learning?
- a) To make predictions faster
- b) To divide data into training and validation sets to evaluate model performance
- c) To remove duplicate data
- d) To automate feature engineering
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28. In the context of deep learning, what does the term 'neural network' refer to?
- a) A model inspired by the human brain to recognize patterns
- b) A group of algorithms designed to sort large datasets
- c) A system for storing and accessing data
- d) A technique for dimensionality reduction
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29. In deep learning, what is the role of an activation function?
- a) To ensure that the output is scaled to a specific range
- b) To introduce non-linearity in the neural network
- c) To prevent overfitting
- d) To monitor the model's training progress
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30. In the context of machine learning, what is overfitting?
- a) When a model is too simple to make accurate predictions
- b) When a model performs well on the training data but poorly on new data
- c) When a model doesn't fit the training data at all
- d) When the model is too general to provide any insights
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