Databricks Certified Data Engineer - Associate

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.