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Answer: Implement data validation and data cleansing processes, monitor data quality, and enforce data access controls.
The best approach to ensure data integrity and prevent data corruption in a data pipeline involves a combination of implementing data validation and data cleansing processes to identify and rectify data quality issues, continuously monitoring data quality to proactively detect and address issues, and enforcing data access controls to prevent unauthorized access and modifications. This comprehensive approach adheres to compliance standards, is cost-effective by preventing costly data errors, and scalable to accommodate growing data volumes.
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As a Databricks Certified Data Engineer - Associate, you are tasked with designing a data pipeline in an Azure Databricks environment that ensures high data integrity and prevents data corruption. The solution must adhere to strict compliance standards, be cost-effective, and scalable. Considering these constraints, which of the following approaches BEST ensures data integrity and prevents data corruption in the pipeline? (Choose one option)
A
Implement data validation and data cleansing processes, monitor data quality, and enforce data access controls.
B
Disable data validation and data cleansing processes, monitor data quality, and enforce data access controls.
C
Implement data validation and data cleansing processes, ignore data quality, and enforce data access controls.
D
Implement data validation and data cleansing processes, monitor data quality, and disable data access controls.
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