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 both Python and SQL, so the mixed-language approach can continue - DLT supports the medallion architecture (bronze, silver, gold layers) - DLT supports streaming sources - No requirement to rewrite everything in one language **B. The pipeline will need to stop using the medallion-based multi-hop architecture** - **INCORRECT** - DLT actually encourages and supports the medallion architecture - You can define bronze (raw), silver (cleaned), and gold (aggregated) tables within 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 pipeline - The data engineer can continue using Python for bronze/silver, analyst can use SQL for gold **D. The pipeline will need to use a batch source in place of a streaming source** - **INCORRECT** - DLT supports both streaming and batch processing - You can use streaming sources with DLT - The `STREAMING` keyword can be used for streaming tables **E. The pipeline will need to be written entirely in Python** - **INCORRECT** - DLT supports both languages, no requirement to use only Python **Key Points:** - DLT is designed to work with existing Databricks workflows - It supports the medallion architecture - It supports both Python and SQL - It supports both streaming and batch processing - The main change would be adding DLT-specific keywords (like `LIVE`, `STREAMING`) if not already present, but this isn't listed in the options **Note:** The provided answer in the text states "Ans should be A" and includes additional context about potentially needing to add `STREAMING` or `LIVE` keywords, which confirms option A is correct.

Author: Keng Suppaseth