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Answer: Define data quality rules using AWS Glue DataBrew by creating a new project, selecting the vehicle maintenance records dataset, and specifying rules to ensure the data is complete and consistent.
Option C is the correct answer. To ensure the data quality of the vehicle maintenance records dataset, you should define data quality rules using AWS Glue DataBrew. By creating a new project, selecting the dataset, and specifying rules to ensure the data is complete and consistent, you can maintain the integrity of the vehicle maintenance records. Manually inspecting each vehicle maintenance 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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Your team is working on a data pipeline that processes data from a transportation company. The data includes vehicle maintenance records with information about vehicle inspections and repairs. You have been tasked with ensuring the data quality of the vehicle maintenance records dataset. Describe the steps you would take to run data quality checks on the vehicle maintenance records dataset and explain how you would define data quality rules to ensure the data is complete and consistent.
A
Run data quality checks by manually inspecting each vehicle maintenance record and identifying any missing or inconsistent information.
B
Use AWS Glue to run data quality checks by writing custom scripts that identify missing or inconsistent information in the vehicle maintenance records.
C
Define data quality rules using AWS Glue DataBrew by creating a new project, selecting the vehicle maintenance records dataset, and specifying rules to ensure the data is complete and consistent.
D
Ignore data quality checks and assume the data is complete and consistent.
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