Answer: No

## Analysis of the Proposed Solution The goal requires three key components: 1. **Ingest incremental data** from Azure Data Lake Storage staging zone 2. **Transform the data by executing an R script** 3. **Insert transformed data** into Azure Synapse Analytics data warehouse ### Why the Solution Does NOT Meet the Goal **Mapping Data Flows in Azure Data Factory do not support R script execution.** Mapping Data Flows use Spark under the hood and support transformations using: - Built-in data transformation functions - Expression language - Custom transformations using Java or Scala - SQL-like operations **Key Limitations:** - **No R Language Support**: Mapping Data Flows cannot execute R code, which is a core requirement of the transformation step - **Limited Custom Scripting**: While Mapping Data Flows support custom transformations, they are limited to Java/Scala and cannot run external R scripts - **R Runtime Dependency**: R scripts require specific runtime environments and packages that are not available in Mapping Data Flows ### Alternative Approaches That Would Work To meet all requirements, you would need to: 1. **Use Azure Databricks**: Execute R notebooks in Azure Databricks, which provides full R runtime support 2. **Use Custom Activities**: Implement Azure Batch or Azure Functions with R support 3. **Stored Procedures**: Use Synapse Analytics stored procedures with R integration (if available in your environment) ### Conclusion The proposed solution fails because **Mapping Data Flows cannot execute R scripts**, which is a mandatory requirement for the data transformation step. While Azure Data Factory can handle the data ingestion and loading aspects, it cannot perform the R-based transformation specified in the requirements.

Author: LeetQuiz Editorial Team