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