Answer: None of these changes will need to be made

## Explanation Delta Live Tables (DLT) is designed to be flexible and support existing pipeline architectures. Let's analyze each option: **A. None of these changes will need to be made** - **CORRECT** - DLT supports the medallion architecture (bronze, silver, gold layers) - DLT supports both Python and SQL for different parts of the pipeline - DLT supports streaming sources natively - DLT allows mixing Python and SQL in the same pipeline **B. The pipeline will need to stop using the medallion-based multi-hop architecture** - **INCORRECT** - DLT actually encourages and supports the medallion architecture - The bronze/silver/gold pattern is a recommended best practice in DLT **C. The pipeline will need to be written entirely in SQL** - **INCORRECT** - DLT supports both Python and SQL - You can mix Python and SQL in the same DLT pipeline - The data engineer can continue using Python for bronze/silver layers - The data analyst can continue using SQL for gold layer **D. The pipeline will need to use a batch source in place of a streaming source** - **INCORRECT** - DLT supports both streaming and batch processing - Streaming sources are fully supported in DLT - You can define streaming tables in DLT **E. The pipeline will need to be written entirely in Python** - **INCORRECT** - DLT supports both Python and SQL - The data analyst can continue using SQL for the gold layer **Key DLT Features:** 1. **Language Flexibility**: Supports both Python and SQL 2. **Architecture Support**: Maintains medallion architecture 3. **Processing Modes**: Supports both streaming and batch 4. **Incremental Processing**: Automatically handles incremental updates 5. **Data Quality**: Built-in expectations and data quality monitoring The existing pipeline structure is perfectly compatible with DLT without requiring any of the listed changes.

Author: Keng Suppaseth