Data Transformation MCQs

By: Prof. Dr. Fazal Rehman Shamil | Last updated: August 6, 2024

Question: What is data transformation?

A) Deleting irrelevant data from the dataset
B) Converting data into a suitable format for analysis
C) Aggregating data from multiple sources into a unified format
D) Generating new data from existing datasets
Answer: B) Converting data into a suitable format for analysis

Question: Which of the following is a common data transformation task?

A) Adding noise to the dataset
B) Deleting missing values
C) Removing duplicate records
D) Ignoring outlier detection
Answer: C) Removing duplicate records

Question: Why is data transformation necessary in data mining?

A) To increase the size of the dataset
B) To reduce the dimensionality of the dataset
C) To convert data into a format suitable for analysis
D) To introduce noise into the dataset
Answer: C) To convert data into a format suitable for analysis

Question: Which technique involves scaling numerical data to a standard range?

A) Data normalization
B) Data discretization
C) Data encoding
D) Data imputation
Answer: A) Data normalization

Question: What does data discretization involve?

A) Converting continuous data values into categorical values
B) Converting categorical data values into continuous values
C) Adding noise to the dataset
D) Removing missing values from the dataset
Answer: A) Converting continuous data values into categorical values

Question: Which of the following is a method of data discretization?

A) Mean normalization
B) Min-max scaling
C) Binning
D) Z-score normalization
Answer: C) Binning

Question: How does data transformation help in handling skewed data distributions?

A) By removing outliers
B) By normalizing the data
C) By adding noise to the dataset
D) By ignoring missing values
Answer: B) By normalizing the data

Question: What is the purpose of feature scaling in data transformation?

A) To remove duplicate records
B) To convert data into a standard format
C) To handle missing values
D) To introduce variability into the dataset
Answer: B) To convert data into a standard format

Question: Which technique involves transforming categorical variables into numerical representations?

A) Data normalization
B) Data encoding
C) Data discretization
D) Data imputation
Answer: B) Data encoding

Question: What is the primary goal of data transformation in data mining?

A) To introduce noise into the dataset
B) To increase the dimensionality of the dataset
C) To enhance the quality of data visualization
D) To prepare data for analysis
Answer: D) To prepare data for analysis

Question: Which technique involves applying mathematical transformations to data to make it more suitable for analysis?

A) Data aggregation
B) Data standardization
C) Data normalization
D) Data enrichment
Answer: C) Data normalization

Question: What does logarithmic transformation aim to achieve?

A) To convert data into a linear format
B) To handle missing values in the dataset
C) To reduce the skewness of data distributions
D) To introduce variability into the dataset
Answer: C) To reduce the skewness of data distributions

Question: Which of the following is a technique for handling outliers during data transformation?

A) Data imputation
B) Data masking
C) Data validation
D) Data trimming
Answer: D) Data trimming

Question: How does feature engineering relate to data transformation?

A) Feature engineering is a subset of data transformation techniques
B) Feature engineering involves creating new features from existing data
C) Feature engineering focuses solely on data cleaning
D) Feature engineering removes outliers from the dataset
Answer: B) Feature engineering involves creating new features from existing data

Question: Which approach involves transforming data into a format that improves the performance of machine learning algorithms?

A) Data standardization
B) Data normalization
C) Feature scaling
D) All of the above
Answer: D) All of the above

More Next Data Mining MCQs

1. Repeated Data Mining MCQs
2. Classification in Data mining MCQs
3. Clustering in Data mining MCQs
4. Data Analysis and Experimental Design MCQs
5. Basics of Data Science MCQs
6. Big Data MCQs
7. Caret Data Science MCQs 
8. Binary and Count Outcomes MCQs
9. CLI and Git Workflow

1. Data Preprocessing MCQs
   • Data Cleaning MCQs
   • Data Integration MCQs
   • Data Transformation MCQs
   • Data Reduction MCQs
2. Data Warehousing and OLAP MCQs
   • Data Warehouse Architecture MCQs
   • Online Analytical Processing (OLAP) MCQs
   • Data Cube Technology MCQs
3. Association Rule Learning MCQs
   • Apriori Algorithm MCQs
   • Eclat Algorithm MCQs
   • FP-Growth Algorithm MCQs
   • Market Basket Analysis MCQs
4. Classification
   • Decision Trees MCQs
   • Naive Bayes MCQs
   • k-Nearest Neighbors (k-NN) MCQs
   • Support Vector Machines (SVM) MCQs
   • Neural Networks MCQs
5. Clustering
   • k-Means Clustering MCQs
   • Hierarchical Clustering MCQs
   • DBSCAN MCQs
   • Mean Shift MCQs
6. Regression MCQs
7. Anomaly Detection MCQs
8. Text Mining and Natural Language Processing (NLP) MCQs
   • Text Preprocessing MCQs
   • Sentiment Analysis MCQs
9. Web Mining MCQs
   • Web Content Mining MCQs
   • Web Structure Mining MCQs
   • Web Usage Mining MCQs
10. Sequential Pattern Mining MCQs
11. Time Series Analysis MCQs

Data Mining Algorithms and Techniques MCQs

13. Frequent Itemset Mining MCQs
    • Apriori Algorithm MCQs
    • FP-Growth Algorithm MCQs
    • Eclat Algorithm MCQs
14. Dimensionality Reduction MCQs
15. Ensemble Methods MCQs
16. Data Mining Tools and Software MCQs
    • WEKA MCQs
    • RapidMiner MCQs
    • KNIME MCQs
    • Orange MCQs
17. Python  Programming for Data Mining MCQs (Pandas, NumPy, Scikit-Learn)
18. R Programming for Data Mining(dplyr, ggplot2, caret) MCQs
19. SQL Programming for Data Mining for Data Mining MCQs
20. Big Data Technologies MCQs
    • Hadoop and MapReduce  MCQs
    • Spark MCQs
    • NoSQL Databases (e.g., MongoDB, Cassandra) MCQs