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Answer: Define data quality rules using AWS Glue DataBrew by creating a new project, selecting the employee records dataset, and specifying rules to identify and resolve data inconsistencies related to employee job positions.
Option C is the correct answer. To ensure the data quality of the employee records dataset, you should define data quality rules using AWS Glue DataBrew. By creating a new project, selecting the dataset, and specifying rules to identify and resolve data inconsistencies related to employee job positions, you can maintain the integrity of the employee records. Manually inspecting each employee record (Option A) is not efficient for large datasets. Writing custom scripts (Option B) can be time-consuming and may not cover all possible data quality issues. Ignoring data quality checks (Option D) is not recommended as it can lead to poor data quality and incorrect analysis.
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You are working on a data pipeline that processes data from a human resources department. The data includes employee records with information about employee details and job positions. You have been tasked with ensuring the data quality of the employee records dataset. Describe the steps you would take to run data quality checks on the employee records dataset and explain how you would define data quality rules to identify and resolve data inconsistencies related to employee job positions.
A
Run data quality checks by manually inspecting each employee record and identifying inconsistencies in job positions.
B
Use AWS Glue to run data quality checks by writing custom scripts that identify inconsistencies in job positions based on specific patterns.
C
Define data quality rules using AWS Glue DataBrew by creating a new project, selecting the employee records dataset, and specifying rules to identify and resolve data inconsistencies related to employee job positions.
D
Ignore data quality checks and assume the job positions are consistent.