Do not treat this objective as a recognition exercise. Practise explaining and applying 'Enhance productivity and code quality: Generate sample data and modernize legacy code' in a new scenario, then check your reasoning against the official source. Source context: Read in English Perform code-free transformation at scale with Azure Data Factory Module 10 Units Feedback Beginner Data Engineer Data Scientist Azure Data Factory In this module, you'll learn how to perform common data transformation and cleansing activities within Azure Data Factory without using code. Learning objectives Introduction Explain Data Factory transformation methods Describe Data Factory transformation types Use Data Factory mapping data flow Debug mapping data flow Use Data Factory wrangling data Use compute transformations within Data Factory Integrate SQL Server Integration Services packages within Data Factory Knowledge check Summary Add Prerequisites The student should be able to: Sign-in to the Azure portal Explain and create resource groups Describe Azure Data Factory core components Ingest data into Azure Data Factory using the Copy Activity Introduction min Explain Azure Data Factory transformation methods min Describe Azure Data Factory transformation types min Exercise - Author an Azure Data Factory mapping data flow min Debug mapping data flow min Exercise - Use Azure Data Factory wrangling data min Exercise - Use compute transformations within Azure Data Factory min Exercise - Integrate SQL server integration services packages within Azure Data Factory min Module assessment min Summary min Take the module assessment Module Assessment Results Assess your understanding of this module. Sign in and answer all questions correctly to earn a pass designation on your profile.
Objective
Enhance productivity and code quality: Generate sample data and modernize legacy code