Answer: Both teams would use the same source of truth for their work

## Explanation The correct answer is **B. Both teams would use the same source of truth for their work**. ### Why this is correct: 1. **Core Lakehouse Benefit**: A data lakehouse architecture combines the best aspects of data lakes and data warehouses, providing a unified platform where both data engineering and data analysis teams can work with the same data. 2. **Eliminates Data Silos**: The primary issue described is that teams are working with different data sources or versions of data, leading to inconsistent reports. A lakehouse provides a single source of truth where: - Data engineering teams can ingest, transform, and prepare data - Data analysis teams can directly query the same prepared data - Both teams work with identical data sets 3. **Addresses the Root Cause**: The problem stems from "siloed nature of their organization's data engineering and data analysis architectures." A lakehouse architecture specifically breaks down these silos by providing a unified platform. ### Why other options are incorrect: - **A**: Autoscaling is a technical capability, not a solution to data consistency issues between teams. - **C**: Organizational restructuring is not a technical feature of a lakehouse architecture. - **D**: While collaboration may improve, real-time collaboration is not the primary benefit that addresses data consistency issues. - **E**: Faster response to ad-hoc requests is a secondary benefit, not the core solution to inconsistent reporting. ### Key Takeaway: The lakehouse architecture's fundamental advantage in this scenario is providing a unified data platform that serves as a single source of truth, eliminating discrepancies between teams working with different data sources or versions.

Author: Keng Suppaseth