DP-700 Implement and Manage an Analytics Solution Practice Question
A large enterprise wants to implement a 'Hub and Spoke' data architecture in Microsoft Fabric. They need to share a validated 'Gold' Lakehouse from a central workspace with multiple departmental workspaces without duplicating the underlying storage in OneLake. What is the most effective method to achieve this?
⚠ Common exam trap
Candidates often suggest 'Mirroring' or 'Dataflows', which involve unnecessary data movement or duplication, missing that 'Shortcuts' are the specific feature designed for virtualized data access.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use OneLake shortcuts in departmental Lakehouses.
OneLake shortcuts allow for the virtualization of data across different workspaces and items without moving or copying the data. This supports a 'single source of truth' architecture where data is managed centrally but consumed locally. Using shortcuts minimizes storage costs, reduces data latency, and simplifies the management of data lineage across the entire Fabric tenant.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a Data Pipeline to copy Delta tables daily.
Why it's wrong here
Copying data between workspaces creates redundant storage and introduces synchronization delays, which contradicts the goal of avoiding data duplication. This approach increases costs and makes it difficult to ensure that all departmental workspaces are looking at the most current version of the data. It also adds unnecessary complexity to the overall data orchestration and maintenance.
- ✓
Use OneLake shortcuts in departmental Lakehouses.
Why this is correct
Shortcuts enable users to reference data stored in a central Lakehouse directly within their departmental workspaces. This provides a unified view of the data while keeping the physical storage in the central 'Hub.' It is the optimal solution for a Hub and Spoke architecture as it ensures consistency and eliminates the overhead of managing multiple data copies.
- ✗
Export the central Lakehouse as a template and re-import it.
Why it's wrong here
Exporting and re-importing a Lakehouse only replicates the structure and metadata, but it does not maintain a link to the original data or share the existing content. Each departmental workspace would still need its own data load process to populate the Lakehouse. This method fails to address the requirement for a shared, validated data source without duplication.
- ✗
Configure cross-workspace Power BI DirectQuery models.
Why it's wrong here
While cross-workspace Power BI models allow for report-level sharing, they do not address the need for departmental data engineers to access the raw Lakehouse tables for further processing. This approach limits the departmental users to visualization tasks only, whereas shortcuts provide full access to the underlying data for diverse analytical needs including Spark and SQL.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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