Data Integration MCQs

1. What is data integration?

(A) Deleting irrelevant data from the dataset

(B) Aggregating data from multiple sources into a unified format

(C) Normalizing data values to a standard scale

(D) Applying statistical methods to analyze data

2. Which of the following is a primary challenge in data integration?

(A) Data normalization

(B) Data discretization

(C) Data inconsistency

(D) Data visualization

3. What does schema matching involve in data integration?

(A) Converting data into a common format

(B) Identifying and mapping attributes across datasets

(C) Handling missing values in the dataset

(D) Generating new data from existing datasets

4. Which technique is used to resolve schema conflicts during data integration?

(A) Data clustering

(B) Data transformation

(C) Data cleaning

(D) Data mapping

5. What is the purpose of data fusion in data integration?

(A) To delete redundant data records

(B) To combine data from different sources while resolving conflicts

(C) To normalize data values

(D) To anonymize sensitive data

6. Which approach involves combining data from multiple sources based on a common attribute?

(A) Data summarization

(B) Data aggregation

(C) Data linking

(D) Data merging

7. What is the role of data warehouses in data integration?

(A) To store raw, unprocessed data

(B) To aggregate data from various sources into a central repository

(C) To visualize data patterns

(D) To perform predictive analytics

8. Which technique is used to detect and handle redundancy in integrated datasets?

(A) Data deduplication

(B) Data imputation

(C) Data transformation

(D) Data normalization

9. Why is data integration important in data mining?

(A) It simplifies the data cleaning process

(B) It reduces the need for data analysis

(C) It enables comprehensive analysis by combining diverse datasets

(D) It automates data collection

10. Which technique involves resolving semantic heterogeneity in data integration?

(A) Data normalization

(B) Ontology mapping

(C) Data anonymization

(D) Data imputation

11. What is meant by instance-level integration in data integration?

(A) Integrating data at the attribute level

(B) Integrating data based on geographical location

(C) Integrating data using machine learning algorithms

(D) Integrating data across different instances or records

12. Which approach is used to integrate data by transforming and combining it into a unified format?

(A) Schema mapping

(B) Data cleaning

(C) ETL (Extract, Transform, Load) process

(D) Data linking

13. What is meant by schema-level integration in data integration?

(A) Integrating data based on data types

(B) Integrating data based on data instances

(C) Integrating data based on schema conflicts

(D) Integrating data at the attribute level

14. Which technique involves merging data from multiple sources to create a single, comprehensive dataset?

(A) Data fusion

(B) Data deduplication

(C) Data partitioning

(D) Data transformation

15. How does data integration support business intelligence (BI) applications?

(A) By enhancing data visualization

(B) By optimizing data storage

(C) By automating data cleaning

(D) By simplifying data analysis