Data Integration MCQs

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

Question: 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
Answer: B) Aggregating data from multiple sources into a unified format

Question: Which of the following is a primary challenge in data integration?

A) Data normalization
B) Data discretization
C) Data inconsistency
D) Data visualization
Answer: C) Data inconsistency

Question: 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
Answer: B) Identifying and mapping attributes across datasets

Question: Which technique is used to resolve schema conflicts during data integration?

A) Data clustering
B) Data transformation
C) Data cleaning
D) Data mapping
Answer: D) Data mapping

Question: 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
Answer: B) To combine data from different sources while resolving conflicts

Question: 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
Answer: C) Data linking

Question: 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
Answer: B) To aggregate data from various sources into a central repository

Question: Which technique is used to detect and handle redundancy in integrated datasets?

A) Data deduplication
B) Data imputation
C) Data transformation
D) Data normalization
Answer: A) Data deduplication

Question: 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
Answer: C) It enables comprehensive analysis by combining diverse datasets

Question: Which technique involves resolving semantic heterogeneity in data integration?

A) Data normalization
B) Ontology mapping
C) Data anonymization
D) Data imputation
Answer: B) Ontology mapping

Question: What is meant by instance-level integration in data integration?

A) Integrating data at the attribute level
B) Integrating data across different instances or records
C) Integrating data using machine learning algorithms
D) Integrating data based on geographical location
Answer: B) Integrating data across different instances or records

Question: 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
Answer: C) ETL (Extract, Transform, Load) process

Question: What is meant by schema-level integration in data integration?

A) Integrating data based on data types
B) Integrating data at the attribute level
C) Integrating data based on schema conflicts
D) Integrating data based on data instances
Answer: B) Integrating data at the attribute level

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

A) Data partitioning
B) Data deduplication
C) Data fusion
D) Data transformation
Answer: C) Data fusion

Question: How does data integration support business intelligence (BI) applications?

A) By simplifying data analysis
B) By optimizing data storage
C) By automating data cleaning
D) By enhancing data visualization
Answer: A) By simplifying data analysis

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16. Data Mining Tools and Software MCQs
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