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Answer: Define data quality rules using AWS Glue DataBrew by creating a new project, selecting the patient records dataset, and specifying rules to identify and resolve data inconsistencies related to patient diagnoses and treatment plans.
Option C is the correct answer. To ensure the data quality of the patient 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 patient diagnoses and treatment plans, you can maintain the integrity of the patient records. Manually inspecting each patient 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 healthcare provider. The data includes patient records with information about patient diagnoses and treatment plans. You have been tasked with ensuring the data quality of the patient records dataset. Describe the steps you would take to run data quality checks on the patient records dataset and explain how you would define data quality rules to identify and resolve data inconsistencies related to patient diagnoses and treatment plans.
A
Run data quality checks by manually inspecting each patient record and identifying inconsistencies in diagnoses and treatment plans.
B
Use AWS Glue to run data quality checks by writing custom scripts that identify inconsistencies in diagnoses and treatment plans based on specific patterns.
C
Define data quality rules using AWS Glue DataBrew by creating a new project, selecting the patient records dataset, and specifying rules to identify and resolve data inconsistencies related to patient diagnoses and treatment plans.
D
Ignore data quality checks and assume the diagnoses and treatment plans are consistent.