1. What is the primary goal of Big Data analytics?
- To store data in a compact format.
- To generate insights from small datasets.
- To process and analyze large volumes of structured and unstructured data.
- To visualize data in 3D.
AI for Big Data Analytics: Machine Learning and Data Processing MCQs
Explore key concepts in data mining, predictive analytics and AI-driven insights. Ideal for students and data science professionals.
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2. Which of the following is a key feature of machine learning?
- The ability to automatically improve with experience.
- The ability to create visual representations of data.
- The ability to make decisions based on pre-programmed rules.
- The ability to interpret data as images.
3. What type of data processing involves transforming raw data into meaningful insights?
- Data cleansing
- Data visualization
- Data engineering
- Data analysis
4. Which algorithm is commonly used for supervised learning in machine learning?
- K-means clustering
- Decision Trees
- Apriori Algorithm
- Naive Bayes
5. What is Big Data?
- Large volumes of data that cannot be processed by traditional data processing tools.
- Data stored in small databases.
- Structured data stored in a relational database.
- Data that is processed by manual methods.
6. Which of the following is a machine learning technique used for unsupervised learning?
- Linear Regression
- K-means clustering
- Logistic Regression
- Random Forest
7. What does a decision tree model represent in machine learning?
- A flowchart of decisions and their possible consequences.
- A collection of unsorted data points.
- A method for clustering data.
- A graph of relationships between different classes.
8. Which of these techniques is used for handling missing data in a dataset?
- Imputation
- Clustering
- Normalization
- Classification
9. What is feature selection in the context of Big Data analytics?
- The process of scaling data for analysis.
- The process of storing data in a smaller format.
- The process of cleaning data by removing missing values.
- The process of selecting a subset of relevant features from a large dataset.
10. Which of the following is a major challenge in Big Data analytics?
- Lack of storage capacity
- Inconsistent data formats and structures
- Limited computational power
- Availability of small datasets
11. Which algorithm is used to make predictions based on historical data in machine learning?
- Regression
- Clustering
- Classification
- Association
12. What is the purpose of cross-validation in machine learning?
- To increase the size of the dataset.
- To evaluate the performance of a model on different subsets of data.
- To optimize the storage of data.
- To reduce the dimensionality of data.
13. Which of the following is a technique used for dimensionality reduction in machine learning?
- Principal Component Analysis (PCA)
- Decision Trees
- Random Forests
- K-Nearest Neighbors
14. What is the primary goal of clustering in Big Data analytics?
- To group similar data points together.
- To predict future trends in data.
- To transform data into visual representations.
- To store data in structured formats.
15. Which type of machine learning algorithm is used for classification problems?
- Linear Regression
- K-Nearest Neighbors
- K-means clustering
- Decision Trees
16. What is Big Data analytics primarily used for in business?
- To create small datasets for easy analysis.
- To make sense of large amounts of unstructured data and generate insights.
- To optimize database performance.
- To summarize data using basic statistics.
17. Which of the following is an example of a supervised learning algorithm?
- K-means clustering
- Random Forest
- DBSCAN
- Apriori Algorithm
18. What is the main advantage of using a Random Forest model in machine learning?
- It helps in regression and classification problems by combining multiple decision trees.
- It performs well on small datasets.
- It is used primarily for clustering problems.
- It is faster than decision trees for training.
19. What is the purpose of the 'k' in k-means clustering?
- It defines the number of clusters to divide the dataset into.
- It is used to scale the features of the data.
- It defines the number of nearest neighbors to use.
- It is used to evaluate model accuracy.
20. What is the purpose of using ensemble methods like bagging and boosting?
- To simplify the data storage process.
- To reduce the computational complexity of models.
- To preprocess data before analysis.
- To combine the predictions of multiple models to improve accuracy.
21. What does the term "Big Data" primarily refer to in the context of analytics?
- Data that is too large or complex for traditional data-processing techniques to handle.
- Data stored in a compressed file format.
- Data that can be processed on a personal computer.
- Data that is available in real-time.
22. Which of the following is a popular framework for processing large-scale data in Big Data analytics?
- TensorFlow
- Apache Spark
- NLTK
- OpenCV
23. Which of the following describes the process of data normalization?
- Scaling data to a specific range to ensure it is comparable.
- Converting categorical data into numerical values.
- Reducing the dimensionality of the data.
- Splitting data into training and test sets.
24. What is a neural network used for in machine learning?
- To optimize machine learning algorithms.
- To store data in a database.
- To model complex relationships and make predictions based on data.
- To process unstructured data only.
25. What is the purpose of dimensionality reduction in machine learning?
- To reduce the number of input features in a dataset while retaining important information.
- To increase the size of a dataset.
- To remove noise from a dataset.
- To group similar data points together.
26. What does the term 'bias' refer to in machine learning?
- A measure of a model’s complexity.
- A method of improving model performance.
- A technique for preprocessing data.
- An error introduced by the model’s assumptions.
27. What is the purpose of a confusion matrix in machine learning?
- To evaluate the performance of a classification model.
- To calculate the training time of a model.
- To improve the accuracy of the dataset.
- To visualize the distribution of data points.
28. What is the primary advantage of using deep learning over traditional machine learning techniques in Big Data analytics?
- Deep learning models are simpler and faster.
- Deep learning can automatically extract features from large datasets without manual feature engineering.
- Deep learning is not suitable for unstructured data.
- Deep learning models require less data.
29. What does the term 'scalability' mean in the context of Big Data processing?
- The ability of a system to handle an increasing amount of work or data.
- The ability to store data in smaller units.
- The ability to visualize complex data.
- The ability to reduce the data processing time.
30. Which of the following is an example of an unsupervised learning technique in machine learning?
- Support vector machines
- Linear regression
- K-means clustering
- Decision trees