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You are working on a data pipeline that processes data from a healthcare provider. The data includes patient records with sensitive information. 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 ensure the data is accurate and complete.
You are working on a data pipeline that processes data from a healthcare provider. The data includes patient records with sensitive information. 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 ensure the data is accurate and complete.
Explanation:
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 ensure the data is accurate and complete, 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.