This post is a part of the DP-600: Implementing Analytics Solutions Using Microsoft Fabric Exam Prep Hub; and this topic falls under these sections: 
Prepare data 
    --> Get data 
        --> Implement OneLake Integration for Eventhouse and Semantic Models

Microsoft Fabric is designed around the principle of OneLake as a single, unified data foundation. For the DP-600 exam, the topic “Implement OneLake integration for Eventhouse and semantic models” focuses on how both streaming data and analytical models can integrate with OneLake to enable reuse, governance, and multi-workload analytics.

This topic frequently appears in architecture and scenario-based questions, not as a pure feature checklist.

Why OneLake Integration Is Important

OneLake integration enables:

• A single copy of data to support multiple analytics workloads
• Reduced data duplication and ingestion complexity
• Consistent governance and security
• Seamless movement between real-time, batch, and BI analytics

For the exam, this is about understanding how data flows across Fabric experiences, not just where it lives.

OneLake Integration for Eventhouse

Eventhouse Recap

An Eventhouse is optimized for:

• Real-time and near-real-time analytics
• Streaming and telemetry data
• High-ingestion rates
• Querying with KQL (Kusto Query Language)

By default, Eventhouse is focused on real-time querying—but many solutions require more.

How Eventhouse Integrates with OneLake

When OneLake integration is implemented for an Eventhouse:

• Streaming data ingested into the Eventhouse is persisted in OneLake
• The same data becomes available for:
  • Lakehouses (Spark / SQL)
  • Warehouses (T-SQL reporting)
  • Notebooks
  • Semantic models
• Real-time and historical analytics can coexist

This allows streaming data to participate in downstream analytics without re-ingestion.

Exam Signals for Eventhouse + OneLake

Look for phrases like:

• Persist streaming data
• Reuse event data
• Combine real-time and batch analytics
• Avoid duplicate ingestion pipelines

These strongly indicate OneLake integration for Eventhouse.

OneLake Integration for Semantic Models

Semantic Models Recap

A semantic model (Power BI dataset) defines:

• Business-friendly tables and relationships
• Measures and calculations (DAX)
• Security rules (RLS, OLS)
• A curated layer for reporting and analysis

Semantic models do not store raw data themselves—they rely on underlying data sources.

How Semantic Models Integrate with OneLake

Semantic models integrate with OneLake when their data source is:

• A Lakehouse
• A Warehouse
• Eventhouse data persisted to OneLake

In these cases:

• Data physically resides in OneLake
• The semantic model acts as a logical abstraction
• Multiple reports can reuse the same curated model

This supports the Fabric design pattern of shared semantic models over shared data.

Import vs DirectQuery (Exam-Relevant)

Semantic models can connect to OneLake-backed data using:

• Import mode – best performance, scheduled refresh
• DirectQuery – near-real-time access, source-dependent performance

DP-600 often tests your ability to choose the appropriate mode based on:

• Data freshness requirements
• Dataset size
• Performance expectations

Eventhouse + OneLake + Semantic Models (End-to-End View)

A common DP-600 architecture looks like this:

1. Streaming data is ingested into an Eventhouse
2. Event data is persisted to OneLake
3. Data is accessed by:
   • Lakehouse (for transformations)
   • Warehouse (for BI-friendly schemas)
4. A shared semantic model is built on top
5. Multiple Power BI reports reuse the model

This architecture supports real-time insights and historical analysis from the same data.

Governance and Security Benefits

OneLake integration ensures:

• Centralized security and permissions
• Sensitivity labels applied consistently
• Reduced risk of shadow datasets
• Clear lineage across streaming, batch, and BI layers

Exam questions often frame this as a governance or compliance requirement.

Common Exam Scenarios

You may be asked to:

• Enable downstream analytics from streaming data
• Avoid duplicating event ingestion
• Support real-time dashboards and historical reports
• Reuse a semantic model across teams
• Align streaming analytics with enterprise BI

Always identify:

• Where the data is persisted
• Who needs access
• How fresh the data must be
• Which query language is required

Best Practices (DP-600 Focus)

• Use Eventhouse for real-time ingestion and KQL analytics
• Enable OneLake integration for reuse and persistence
• Build shared semantic models on OneLake-backed data
• Avoid multiple ingestion paths for the same data
• Let OneLake act as the single source of truth

Key Takeaway
For the DP-600 exam, implementing OneLake integration for Eventhouse and semantic models is about enabling streaming data to flow seamlessly into governed, reusable analytical solutions. Eventhouse delivers real-time insights, OneLake provides a unified storage layer, and semantic models expose trusted, business-ready analytics—all without unnecessary duplication.

Practice Questions:

Here are 10 questions to test and help solidify your learning and knowledge. As you review these and other questions in your preparation, make sure to …

• Identifying and understand why an option is correct (or incorrect) — not just which one
• Look for and understand the usage scenario of keywords in exam questions to guide you
• Expect scenario-based questions rather than direct definitions

And also keep in mind …

• When you see streaming data + reuse + BI or ML, think:
  Eventhouse → OneLake → Lakehouse/Warehouse → Semantic model

1. What is the primary benefit of integrating an Eventhouse with OneLake?

A. Faster Power BI rendering
B. Ability to query event data using DAX
C. Persistence and reuse of streaming data across Fabric workloads
D. Elimination of real-time ingestion

Correct Answer: C

Explanation:
OneLake integration allows streaming data ingested into an Eventhouse to be persisted and reused by Lakehouses, Warehouses, notebooks, and semantic models—without re-ingestion.

2. Which query language is used for real-time analytics directly in an Eventhouse?

A. T-SQL
B. Spark SQL
C. DAX
D. KQL

Correct Answer: D

Explanation:
Eventhouses are built on KQL (Kusto Query Language), which is optimized for querying streaming and time-series data.

3. A team wants to combine real-time event data with historical batch data in Power BI. What is the BEST approach?

A. Build separate semantic models for each data source
B. Persist event data to OneLake and build a semantic model on top
C. Use DirectQuery to the Eventhouse only
D. Export event data to Excel

Correct Answer: B

Explanation:
Persisting event data to OneLake allows it to be combined with historical data and exposed through a single semantic model.

4. How do semantic models integrate with OneLake in Microsoft Fabric?

A. Semantic models store data directly in OneLake
B. Semantic models replace OneLake storage
C. Semantic models reference OneLake-backed sources such as Lakehouses and Warehouses
D. Semantic models only support streaming data

Correct Answer: C

Explanation:
Semantic models do not store raw data; they reference OneLake-backed sources like Lakehouses, Warehouses, or persisted Eventhouse data.

5. Which scenario MOST strongly indicates the need for OneLake integration for Eventhouse?

A. Ad hoc SQL reporting on static data
B. Monthly batch ETL processing
C. Reusing streaming data for BI, ML, and historical analysis
D. Creating a single real-time dashboard

Correct Answer: C

Explanation:
OneLake integration is most valuable when streaming data must be reused across multiple analytics workloads beyond real-time querying.

6. Which storage principle best describes the benefit of OneLake integration?

A. Multiple copies for better performance
B. One copy of data, many analytics experiences
C. Schema-on-read only
D. Real-time only storage

Correct Answer: B

Explanation:
Microsoft Fabric promotes the principle of storing one copy of data in OneLake and enabling multiple analytics experiences on top of it.

7. Which connectivity mode should be chosen for a semantic model when near-real-time access to event data is required?

A. Import
B. Cached mode
C. DirectQuery
D. Snapshot mode

Correct Answer: C

Explanation:
DirectQuery enables near-real-time access to the underlying data, making it suitable when freshness is critical.

8. What governance advantage does OneLake integration provide?

A. Automatic deletion of sensitive data
B. Centralized security and sensitivity labeling
C. Removal of workspace permissions
D. Unlimited data access

Correct Answer: B

Explanation:
OneLake integration supports centralized governance, including consistent permissions and sensitivity labels across streaming and batch data.

9. Which end-to-end architecture BEST supports both real-time dashboards and historical reporting?

A. Eventhouse only
B. Lakehouse only
C. Eventhouse with OneLake integration and a shared semantic model
D. Warehouse without ingestion

Correct Answer: C

Explanation:
This architecture enables real-time ingestion via Eventhouse, persistence in OneLake, and curated reporting through a shared semantic model.

10. On the DP-600 exam, which phrase is MOST likely to indicate the need for OneLake integration for Eventhouse?

A. “SQL-only reporting solution”
B. “Single-user analysis”
C. “Avoid duplicating streaming ingestion pipelines”
D. “Static reference data”

Correct Answer: C

Explanation:
Avoiding duplication and enabling reuse of streaming data across analytics workloads is a key signal for OneLake integration.