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: 
Implement and manage semantic models (25-30%) 
    --> Optimize enterprise-scale semantic models 
        --> Choose between Direct Lake on OneLake and Direct Lake on SQL endpoints

In Microsoft Fabric, Direct Lake is a high-performance semantic model storage mode that allows Power BI and Fabric semantic models to query data directly from OneLake without importing it into VertiPaq. When implementing Direct Lake, you must choose where the semantic model reads from, either:

• Direct Lake on OneLake
• Direct Lake on SQL endpoints

Understanding the differences, trade-offs, and use cases for each option is critical for optimizing enterprise-scale semantic models, and this topic appears explicitly in the DP-600 exam blueprint.

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Direct Lake on OneLake

What It Is

Direct Lake on OneLake connects the semantic model directly to Delta tables stored in OneLake, bypassing SQL engines entirely. Queries operate directly on Parquet/Delta files using the Fabric Direct Lake engine.

Key Characteristics

• Reads Delta tables directly from OneLake
• No dependency on a SQL query engine
• Near-Import performance with zero data duplication
• Minimal latency between data ingestion and reporting
• Requires supported Delta table structures and data types

Advantages

• Best performance for large-scale analytics
• Always reflects the latest data written to OneLake
• Eliminates Import refresh overhead
• Ideal for lakehouse-centric architectures

Limitations

• Some complex DAX patterns may cause fallback
• Requires schema compatibility with Direct Lake
• Less flexibility for SQL-based transformations

Typical Use Cases

• Enterprise lakehouse analytics
• High-volume fact tables
• Near-real-time reporting
• Fabric-native data pipelines

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Direct Lake on SQL Endpoints

What It Is

Direct Lake on SQL endpoints connects the semantic model to the SQL analytics endpoint of a Lakehouse or Warehouse, while still using Direct Lake storage mode behind the scenes.

Instead of reading files directly, the semantic model relies on the SQL endpoint to expose the data.

Key Characteristics

• Queries go through the SQL endpoint
• Still benefits from Direct Lake storage
• Enables SQL views and transformations
• Slightly higher latency than pure OneLake access

Advantages

• Supports SQL-based modeling (views, joins, calculated columns)
• Easier integration with existing SQL logic
• Familiar experience for SQL-first teams
• Useful when business logic is already defined in SQL

Limitations

• Additional query layer may impact performance
• Less efficient than direct file access
• SQL endpoint availability becomes a dependency

Typical Use Cases

• Organizations with strong SQL development practices
• Reuse of existing SQL views and transformations
• Gradual migration from Warehouse or SQL models
• Mixed BI and ad-hoc SQL workloads

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Key Comparison Summary

Aspect	Direct Lake on OneLake	Direct Lake on SQL Endpoint
Data access	Direct file access	Via SQL analytics endpoint
Performance	Highest	Slightly lower
SQL dependency	None	Required
Schema flexibility	Lower	Higher
Transformation style	Lakehouse / Spark	SQL-based
Ideal for	Scale & performance	SQL reuse & flexibility

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Choosing Between the Two (Exam-Focused Guidance)

On the DP-600 exam, questions typically focus on architectural intent and performance optimization:

Choose Direct Lake on OneLake when:

• Performance is the top priority
• Data is already modeled in Delta tables
• You want the simplest, most scalable architecture
• Near-real-time analytics are required

Choose Direct Lake on SQL endpoints when:

• You need SQL views or transformations
• Existing logic already exists in SQL
• Teams are more comfortable with SQL than Spark
• Some flexibility is preferred over maximum performance

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Exam Tip 💡

If a question emphasizes:

• Maximum performance, minimal latency, or scalability/large-scale analytics → Direct Lake on OneLake
• SQL views, SQL transformations, or SQL reuse → Direct Lake on SQL endpoints

Expect scenario-based questions where both options are technically valid, but only one best aligns with the business and performance requirements.

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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

Question 1

A company has Delta tables stored in OneLake and wants the lowest possible query latency for Power BI reports without using SQL views. Which option should they choose?

A. Import mode
B. DirectQuery on SQL endpoint
C. Direct Lake on SQL endpoint
D. Direct Lake on OneLake

Correct Answer: D

Explanation:
Direct Lake on OneLake reads Delta tables directly from OneLake without a SQL layer, delivering the best performance and lowest latency.

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Question 2

Which requirement would most strongly favor Direct Lake on SQL endpoints over Direct Lake on OneLake?

A. Maximum performance
B. Real-time data visibility
C. Use of SQL views for business logic
D. Minimal infrastructure dependencies

Correct Answer: C

Explanation:
Direct Lake on SQL endpoints allows semantic models to consume SQL views and transformations, making it ideal when business logic is defined in SQL.

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Question 3

What is a key architectural difference between Direct Lake on OneLake and Direct Lake on SQL endpoints?

A. Only OneLake supports Delta tables
B. SQL endpoints require data import
C. OneLake access bypasses the SQL engine
D. SQL endpoints cannot be used with semantic models

Correct Answer: C

Explanation:
Direct Lake on OneLake reads Delta files directly from storage, while SQL endpoints introduce an additional SQL query layer.

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Question 4

A Fabric semantic model uses Direct Lake on OneLake. Under which condition might it fallback to DirectQuery?

A. The model contains calculated columns
B. The dataset exceeds 1 TB
C. The Delta table schema is unsupported
D. The SQL endpoint is unavailable

Correct Answer: C

Explanation:
If the Delta table schema or data types are not supported by Direct Lake, Fabric automatically falls back to DirectQuery.

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Question 5

Which scenario is best suited for Direct Lake on SQL endpoints?

A. High-volume streaming telemetry
B. SQL-first team reusing existing warehouse views
C. Near-real-time dashboards on raw lake data
D. Large fact tables optimized for scan performance

Correct Answer: B

Explanation:
Direct Lake on SQL endpoints is ideal when teams rely on SQL views and want to reuse existing SQL logic.

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Question 6

Which statement about performance is most accurate?

A. SQL endpoints always outperform OneLake
B. OneLake always requires Import mode
C. Direct Lake on OneLake typically offers better performance
D. Direct Lake on SQL endpoints does not use Direct Lake

Correct Answer: C

Explanation:
Direct Lake on OneLake avoids the SQL layer, resulting in faster query execution in most scenarios.

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Question 7

A Power BI model must reflect new data immediately after ingestion into OneLake. Which option best supports this requirement?

A. Import mode
B. DirectQuery
C. Direct Lake on SQL endpoint
D. Direct Lake on OneLake

Correct Answer: D

Explanation:
Direct Lake on OneLake reads data directly from Delta tables and reflects changes immediately without refresh.

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Question 8

Which dependency exists when using Direct Lake on SQL endpoints that does not exist with Direct Lake on OneLake?

A. Delta Lake support
B. VertiPaq compression
C. SQL analytics endpoint availability
D. Semantic model compatibility

Correct Answer: C

Explanation:
Direct Lake on SQL endpoints depends on the SQL analytics endpoint being available, while OneLake access does not.

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Question 9

From a DP-600 exam perspective, which factor most often determines the correct choice between these two options?

A. Dataset size alone
B. Whether SQL transformations are required
C. Number of report users
D. Power BI license type

Correct Answer: B

Explanation:
Exam questions typically focus on whether SQL logic (views, joins, transformations) is needed, which drives the choice.

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Question 10

You are designing an enterprise semantic model focused on scalability and minimal complexity. The data is already curated as Delta tables. What is the best choice?

A. Import mode
B. DirectQuery on SQL endpoint
C. Direct Lake on SQL endpoint
D. Direct Lake on OneLake

Correct Answer: D

Explanation:
Direct Lake on OneLake offers the simplest architecture with the highest scalability and performance when Delta tables are already prepared.

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