Ensuring data quality and consistency is a cornerstone of building reliable Azure Databricks-based data pipelines. Which approach offers an automated and scalable solution for testing data quality and consistency across your datasets?

Real Exam

Writing custom validation scripts in Databricks notebooks that run as part of the pipeline execution, outputting data quality metrics

21.4%

Relying on Azure Data Factory‘s data flow debug features to validate data quality without additional testing in Databricks

14.3%

Manual review of random data samples before and after processing in Databricks

4.8%

Utilizing a third-party tool specifically designed for data quality testing, integrated with Azure Databricks via APIs

59.5%

Databricks Certified Data Engineer - Professional