Data Integration

Merging data from multiple sources

The collection and integration of data from various sources for a comprehensive view of the data.

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Big Data Scientist - Data Integration Example Questions

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Question 1

What is data profiling in data integration?

A process of compressing data sets A process of archiving historical data sets A process of backing up and restoring data sets A process of examining data sets to determine their content, structure, and quality A process of merging data sets into a single source of truth A process of transforming data into a standard form

Correct Answer: A process of examining data sets to determine their content, structure, and quality

Data profiling is the process of analyzing data to determine its quality, completeness, and structure. It is done before integration to ensure the data meets the required standards.

Question 2

What is data quality?

The degree to which data meets the required standards for accuracy, completeness, consistency, and relevancy The cost of storing and managing data The type of data stored in a database The security measures in place to protect data The volume of data stored in a database The speed at which data can be processed

Correct Answer: The degree to which data meets the required standards for accuracy, completeness, consistency, and relevancy

Data quality refers to the degree to which data meets the required standards for accuracy, completeness, consistency, and relevancy. Poor data quality can lead to incorrect insights and decisions, and can cause problems in data integration and analytics.

Question 3

What is data cleansing?

The process of identifying and correcting or removing corrupt or inaccurate records from a dataset The process of importing data to a central location The process of encrypting sensitive data The process of analyzing data to find trends The process of backing up data for disaster recovery

Correct Answer: The process of identifying and correcting or removing corrupt or inaccurate records from a dataset

Data cleansing is the process of identifying and correcting or removing corrupt or inaccurate records from a dataset. This is an important step in data integration to ensure that the unified view of data is accurate and reliable.

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