Tag: Data Engineering

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Exam Prep Hub for DP-600: Implementing Analytics Solutions Using Microsoft Fabric

This is your one-stop hub with information for preparing for the DP-600: Implementing Analytics Solutions Using Microsoft Fabric certification exam. Upon successful completion of the exam, you earn the Fabric Analytics Engineer Associate certification.

This hub provides information directly here, links to a number of external resources, tips for preparing for the exam, practice tests, and section questions to help you prepare. Bookmark this page and use it as a guide to ensure that you are fully covering all relevant topics for the exam and using as many of the resources available as possible. We hope you find it convenient and helpful.

Why do the DP-600: Implementing Analytics Solutions Using Microsoft Fabric exam to gain the Fabric Analytics Engineer Associate certification?

Most likely, you already know why you want to earn this certification, but in case you are seeking information on its benefits, here are a few:
(1) there is a possibility for career advancement because Microsoft Fabric is a leading data platform used by companies of all sizes, all over the world, and is likely to become even more popular
(2) greater job opportunities due to the edge provided by the certification
(3) higher earnings potential,
(4) you will expand your knowledge about the Fabric platform by going beyond what you would normally do on the job and
(5) it will provide immediate credibility about your knowledge, and
(6) it may, and it should, provide you with greater confidence about your knowledge and skills.

Links to important DP-600 resources:

DP-600: Skills measured as of October 31, 2025:

Here you can learn in a structured manner by going through the topics of the exam one-by-one to ensure full coverage; click on each hyperlinked topic to go to more information about it:

Maintain a data analytics solution (25%-30%)

Implement security and governance

Implement workspace-level access controls

Implement item-level access controls

Implement row-level, column-level, object-level, and file-level access controls

Apply sensitivity labels to items

Endorse items

Maintain the analytics development lifecycle

Configure version control for a workspace

Create and manage a Power BI Desktop project (.pbip)

Create and configure development pipelines

Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models

Deploy and manage semantic models using XMLA endpoint

Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models

Prepare data (45%-50%)

Get Data

Create a data connection

Discover data by using OneLake catalog and Real-Time Hub

Ingest or access data as needed

Choose between a lakehouse, warehouse, or eventhouse

Implement OneLake Integration for eventhouse and semantic models

Transform Data

Create views, functions, and stored procedures

Enrich data by adding new columns and tables

Implement a star schema for a lakehouse or warehouse

Denormalize data

Aggregate data

Merge or join data

Identify and resolve duplicate data, missing data, or null values

Convert column data types

Filter data

Query and analyze data

Select, filter, and aggregate data by using the Visual Query Editor

Select, filter, and aggregate data using SQL

Select, filter, and aggregate data by using KQL

Select, filter, and aggregate data by using DAX

Implement and manage semantic models (25%-30%)

Design and build semantic models

Choose a storage mode

Choose a storage mode – additional information

Implement a star schema for a semantic model

Implement relationships, such as bridge tables and many-to-many relationships

Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions

Implement calculation groups, dynamic format strings, and field parameters

Identify use cases for and configure large semantic model storage format

Design and build composite models

Optimize enterprise-scale semantic models

Implement performance improvements in queries and report visuals

Improve DAX performance

Configure Direct Lake, including default fallback and refresh behavior

Choose between Direct Lake on OneLake and Direct Lake on SQL endpoints

Implement incremental refresh for semantic models

Practice Exams:

We have provided 2 practice exams with answers to help you prepare.

DP-600 Practice Exam 1 (60 questions with answer key)

DP-600 Practice Exam 2 (60 questions with answer key)

Good luck to you passing the DP-600: Implementing Analytics Solutions Using Microsoft Fabric certification exam and earning the Fabric Analytics Engineer Associate certification!

Analytics, BI Administration, Big Data, Business Intelligence (BI) Development, Data Analysis, Data Development, Data Governance, Data Integration, Data Integration (ETL), Data Modeling, Data Strategy, Data Visualization, Data Warehousing, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI, Power Query, Reporting, SQL

Implement Performance Improvements in Queries and Report Visuals

 
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 
        --> Implement performance improvements in queries and report visuals

Performance optimization is a critical skill for the Fabric Analytics Engineer. In enterprise-scale semantic models, poor query design, inefficient DAX, or overly complex visuals can significantly degrade report responsiveness and user experience. This exam section focuses on identifying performance bottlenecks and applying best practices to improve query execution, model efficiency, and report rendering.

1. Understand Where Performance Issues Occur

Performance problems typically fall into three layers:

a. Data & Storage Layer

  • Storage mode (Import, DirectQuery, Direct Lake, Composite)
  • Data source latency
  • Table size and cardinality
  • Partitioning and refresh strategies

b. Semantic Model & Query Layer

  • DAX calculation complexity
  • Relationships and filter propagation
  • Aggregation design
  • Use of calculation groups and measures

c. Report & Visual Layer

  • Number and type of visuals
  • Cross-filtering behavior
  • Visual-level queries
  • Use of slicers and filters

DP-600 questions often test your ability to identify the correct layer where optimization is needed.

2. Optimize Queries and Semantic Model Performance

a. Choose the Appropriate Storage Mode

  • Use Import for small-to-medium datasets requiring fast interactivity
  • Use Direct Lake for large OneLake Delta tables with high concurrency
  • Use Composite models to balance performance and real-time access
  • Avoid unnecessary DirectQuery when Import or Direct Lake is feasible

b. Reduce Data Volume

  • Remove unused columns and tables
  • Reduce column cardinality (e.g., avoid high-cardinality text columns)
  • Prefer surrogate keys over natural keys
  • Disable Auto Date/Time when not needed

c. Optimize Relationships

  • Use single-direction relationships by default
  • Avoid unnecessary bidirectional filters
  • Ensure relationships follow a star schema
  • Avoid many-to-many relationships unless required

d. Use Aggregations

  • Create aggregation tables to pre-summarize large fact tables
  • Enable query hits against aggregation tables before scanning detailed data
  • Especially valuable in composite models

3. Improve DAX Query Performance

a. Write Efficient DAX

  • Prefer measures over calculated columns
  • Use variables (VAR) to avoid repeated calculations
  • Minimize row context where possible
  • Avoid excessive iterators (SUMX, FILTER) over large tables

b. Use Filter Context Efficiently

  • Prefer CALCULATE with simple filters
  • Avoid complex nested FILTER expressions
  • Use KEEPFILTERS and REMOVEFILTERS intentionally

c. Avoid Expensive Patterns

  • Avoid EARLIER in favor of variables
  • Avoid dynamic table generation inside visuals
  • Minimize use of ALL when ALLSELECTED or scoped filters suffice

4. Optimize Report Visual Performance

a. Reduce Visual Complexity

  • Limit the number of visuals per page
  • Avoid visuals that generate multiple queries (e.g., complex custom visuals)
  • Use summary visuals instead of detailed tables where possible

b. Control Interactions

  • Disable unnecessary visual interactions
  • Avoid excessive cross-highlighting
  • Use report-level filters instead of visual-level filters when possible

c. Optimize Slicers

  • Avoid slicers on high-cardinality columns
  • Use dropdown slicers instead of list slicers
  • Limit the number of slicers on a page

d. Prefer Measures Over Visual Calculations

  • Avoid implicit measures created by dragging numeric columns
  • Define explicit measures in the semantic model
  • Reuse measures across visuals to improve cache efficiency

5. Use Performance Analysis Tools

a. Performance Analyzer

  • Identify slow visuals
  • Measure DAX query duration
  • Distinguish between query time and visual rendering time

b. Query Diagnostics (Power BI Desktop)

  • Analyze backend query behavior
  • Identify expensive DirectQuery or Direct Lake operations

c. DAX Studio (Advanced)

  • Analyze query plans
  • Measure storage engine vs formula engine time
  • Identify inefficient DAX patterns

(You won’t be tested on tool UI details, but knowing when and why to use them is exam-relevant.)

6. Common DP-600 Exam Scenarios

You may be asked to:

  • Identify why a report is slow and choose the best optimization
  • Identify the bottleneck layer (model, query, or visual)
  • Select the most appropriate storage mode for performance
  • Choose the least disruptive, most effective optimization
  • Improve a slow DAX measure
  • Reduce visual rendering time without changing the data source
  • Optimize performance for enterprise-scale models
  • Apply enterprise-scale best practices, not just quick fixes

Key Exam Takeaways

  • Always optimize the model first, visuals second
  • Star schema + clean relationships = better performance
  • Efficient DAX matters more than clever DAX
  • Fewer visuals and interactions = faster reports
  • Aggregations and Direct Lake are key enterprise-scale tools

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

1. A Power BI report built on a large semantic model is slow to respond. Performance Analyzer shows long DAX query times but minimal visual rendering time. Where should you focus first?

A. Reducing the number of visuals
B. Optimizing DAX measures and model design
C. Changing visual types
D. Disabling report interactions

✅ Correct Answer: B

Explanation:
If DAX query time is the bottleneck, the issue lies in measure logic, relationships, or model design, not visuals.

2. Which storage mode typically provides the best interactive performance for large Delta tables stored in OneLake?

A. Import
B. DirectQuery
C. Direct Lake
D. Live connection

✅ Correct Answer: C

Explanation:
Direct Lake queries Delta tables directly in OneLake, offering better performance than DirectQuery while avoiding full data import.

3. Which modeling change most directly improves query performance in enterprise-scale semantic models?

A. Using many-to-many relationships
B. Converting snowflake schemas to star schemas
C. Increasing column cardinality
D. Enabling bidirectional filtering

✅ Correct Answer: B

Explanation:
A star schema simplifies joins and filter propagation, improving both storage engine efficiency and DAX performance.

4. A measure uses multiple nested SUMX and FILTER functions over a large fact table. Which change is most likely to improve performance?

A. Replace the measure with a calculated column
B. Introduce DAX variables to reuse intermediate results
C. Add more visuals to cache results
D. Convert the table to DirectQuery

✅ Correct Answer: B

Explanation:
Using DAX variables (VAR) prevents repeated evaluation of expressions, significantly improving formula engine performance.

5. Which practice helps reduce memory usage and improve performance in Import mode models?

A. Keeping all columns for future use
B. Increasing the number of calculated columns
C. Removing unused columns and tables
D. Enabling Auto Date/Time for all tables

✅ Correct Answer: C

Explanation:
Removing unused columns reduces model size, memory consumption, and scan time, improving overall performance.

6. What is the primary benefit of using aggregation tables in composite models?

A. They eliminate the need for relationships
B. They allow queries to be answered without scanning detailed fact tables
C. They automatically optimize visuals
D. They replace Direct Lake storage

✅ Correct Answer: B

Explanation:
Aggregation tables allow Power BI to satisfy queries using pre-summarized Import data, avoiding expensive scans of large fact tables.

7. Which visual design choice is most likely to degrade report performance?

A. Using explicit measures
B. Limiting visuals per page
C. Using high-cardinality fields in slicers
D. Using report-level filters

✅ Correct Answer: C

Explanation:
Slicers on high-cardinality columns generate expensive queries and increase interaction overhead.

8. When optimizing report interactions, which action can improve performance without changing the data model?

A. Enabling all cross-highlighting
B. Disabling unnecessary visual interactions
C. Adding calculated tables
D. Switching to DirectQuery

✅ Correct Answer: B

Explanation:
Disabling unnecessary visual interactions reduces the number of queries triggered by user actions.

9. Which DAX practice is recommended for improving performance in enterprise semantic models?

A. Use implicit measures whenever possible
B. Prefer calculated columns over measures
C. Minimize row context and iterators on large tables
D. Use ALL() in every calculation

✅ Correct Answer: C

Explanation:
Iterators and row context are expensive on large tables. Minimizing their use improves formula engine efficiency.

10. Performance Analyzer shows fast query execution but slow visual rendering. What is the most likely cause?

A. Inefficient DAX measures
B. Poor relationship design
C. Too many or overly complex visuals
D. Incorrect storage mode

✅ Correct Answer: C

Explanation:
When rendering time is high but queries are fast, the issue is usually visual complexity, not the model or DAX.

Analytics, Business Intelligence, Business Intelligence (BI) Development, Data Analysis, Data Development, Data Integration, Data Modeling, Data Quality Assurance, Data Security, Data Strategy, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI, Power Query, Reporting, SQL

Design and Build Composite Models

 
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%) 
    --> Design and build semantic models 
        --> Design and Build Composite Models

What Is a Composite Model?

A composite model in Power BI and Microsoft Fabric combines data from multiple data sources and multiple storage modes in a single semantic model. Rather than importing all data into the model’s in-memory cache, composite models let you mix different query/storage patterns such as:

  • Import
  • DirectQuery
  • Direct Lake
  • Live connections

Composite models enable flexible design and optimized performance across diverse scenarios.

Why Composite Models Matter

Semantic models often need to support:

  • Large datasets that cannot be imported fully
  • Real-time or near-real-time requirements
  • Federation across disparate sources
  • Mix of highly dynamic and relatively static data

Composite models let you combine the benefits of in-memory performance with direct source access.

Core Concepts

Storage Modes in Composite Models

Storage ModeDescriptionTypical Use
ImportData is cached in the semantic model memoryFast performance for static or moderately sized data
DirectQueryQueries are pushed to the source at runtimeReal-time or large relational sources
Direct LakeQueries Delta tables in OneLakeLarge OneLake data with faster interactive access
Live ConnectionDelegates all query processing to an external modelShared enterprise semantic models

A composite model may include tables using different modes — for example, imported dimension tables and DirectQuery/Direct Lake fact tables.

Key Features of Composite Models

1. Table-Level Storage Modes

Every table in a composite model may use a different storage mode:

  • Dimensions may be imported
  • Fact tables may use DirectQuery or Direct Lake
  • Bridge or helper tables may be imported

This flexibility enables performance and freshness trade-offs.

2. Relationships Across Storage Modes

Relationships can span tables even if they use different storage modes, enabling:

  • Filtering between imported and DirectQuery tables
  • Cross-mode joins (handled intelligently by the engine)

Underlying engines push queries to the appropriate source (SQL, OneLake, Semantic layer), depending on where the data resides.

3. Aggregations and Hierarchies

You can define:

  • Aggregated tables (pre-summarized import tables)
  • Detail tables (DirectQuery or Direct Lake)

Power BI automatically uses aggregations when a visual’s query can be satisfied with summary data, enhancing performance.

4. Calculation Groups and Measures

Composite models work with complex semantic logic:

  • Calculation groups (standardized transformations)
  • DAX measures that span imported and DirectQuery tables

These models require careful modeling to ensure that context transitions behave predictably.

When to Use Composite Models

Composite models are ideal when:

A. Data Is Too Large to Import

  • Large fact tables (> hundreds of millions of rows)
  • Delta/OneLake data too big for full in-memory import
  • Use Direct Lake for these, while importing dimensions

B. Real-Time Data Is Required

  • Operational reporting
  • Systems with high update frequency
  • Use DirectQuery to relational sources

C. Multiple Data Sources Must Be Combined

  • Relational databases
  • OneLake & Delta
  • Cloud services (e.g., Synapse, SQL DB, Spark)
  • On-prem gateways

Composite models let you combine these seamlessly.

D. Different Performance vs Freshness Needs

  • Import for static master data
  • DirectQuery or Direct Lake for dynamic fact data

Composite vs Pure Models

AspectImport OnlyComposite
PerformanceVery fastDepends on source/query pattern
FreshnessScheduled refreshReal-time/near-real-time possible
Source diversityLimitedMultiple heterogeneous sources
Model complexitySimplerHigher

Query Execution and Optimization

Query Folding

  • DirectQuery and Power Query transformations rely on query folding to push logic back to the source
  • Query folding is essential for performance in composite models

Storage Mode Selection

Good modeling practices for composite models include:

  • Import small dimension tables
  • Direct Lake for large storage in OneLake
  • DirectQuery for real-time relational sources
  • Use aggregations to optimize performance

Modeling Considerations

1. Relationship Direction

  • Prefer single-direction relationships
  • Use bidirectional filtering only when required (careful with ambiguity)

2. Data Type Consistency

  • Ensure fields used in joins have matching data types
  • In composite models, mismatches can cause query fallbacks

3. Cardinality

  • High cardinality DirectQuery columns can slow queries
  • Use star schema patterns

4. Security

  • Row-level security crosses modes but must be carefully tested
  • Security logic must consider where filters are applied

Common Exam Scenarios

Exam questions may ask you to:

  • Choose between Import, DirectQuery, Direct Lake and composite
  • Assess performance vs freshness requirements
  • Determine query folding feasibility
  • Identify correct relationship patterns across modes

Example prompt:

“Your model combines a large OneLake dataset and a small dimension table. Users need current data daily but also fast filtering. Which storage and modeling approach is best?”

Correct exam choices often point to composite models using Direct Lake + imported dimensions.

Best Practices

  • Define a clear star schema even in composite models
  • Import dimension tables where reasonable
  • Use aggregations to improve performance for heavy visuals
  • Limit direct many-to-many relationships
  • Use calculation groups to apply analytics consistently
  • Test query performance across storage modes

Exam-Ready Summary/Tips

Composite models enable flexible and scalable semantic models by mixing storage modes:

  • Import – best performance for static or moderate data
  • DirectQuery – real-time access to source systems
  • Direct Lake – scalable querying of OneLake Delta data
  • Live Connection – federated or shared datasets

Design composite models to balance performance, freshness, and data volume, using strong schema design and query optimization.

For DP-600, always evaluate:

  • Data volume
  • Freshness requirements
  • Performance expectations
  • Source location (OneLake vs relational)

Composite models are frequently the correct answer when these requirements conflict.

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

1. What is the primary purpose of using a composite model in Microsoft Fabric?

A. To enable row-level security across workspaces
B. To combine multiple storage modes and data sources in one semantic model
C. To replace DirectQuery with Import mode
D. To enforce star schema design automatically

✅ Correct Answer: B

Explanation:
Composite models allow you to mix Import, DirectQuery, Direct Lake, and Live connections within a single semantic model, enabling flexible performance and data-freshness tradeoffs.

2. You are designing a semantic model with a very large fact table stored in OneLake and small dimension tables. Which storage mode combination is most appropriate?

A. Import all tables
B. DirectQuery for all tables
C. Direct Lake for the fact table and Import for dimension tables
D. Live connection for the fact table and Import for dimensions

✅ Correct Answer: C

Explanation:
Direct Lake is optimized for querying large Delta tables in OneLake, while importing small dimension tables improves performance for filtering and joins.

3. Which storage mode allows querying OneLake Delta tables without importing data into memory?

A. Import
B. DirectQuery
C. Direct Lake
D. Live Connection

✅ Correct Answer: C

Explanation:
Direct Lake queries Delta tables directly in OneLake, combining scalability with better interactive performance than traditional DirectQuery.

4. What happens when a DAX query in a composite model references both imported and DirectQuery tables?

A. The query fails
B. The data must be fully imported
C. The engine generates a hybrid query plan
D. All tables are treated as DirectQuery

✅ Correct Answer: C

Explanation:
Power BI’s engine generates a hybrid query plan, pushing operations to the source where possible and combining results with in-memory data.

5. Which scenario most strongly justifies using a composite model instead of Import mode only?

A. All data fits in memory and refreshes nightly
B. The dataset is static and small
C. Users require near-real-time data from a large relational source
D. The model contains only calculated tables

✅ Correct Answer: C

Explanation:
Composite models are ideal when real-time or near-real-time access is needed, especially for large datasets that are impractical to import.

6. In a composite model, which table type is typically best suited for Import mode?

A. High-volume transactional fact tables
B. Streaming event tables
C. Dimension tables with low cardinality
D. Tables requiring second-by-second freshness

✅ Correct Answer: C

Explanation:
Importing dimension tables improves query performance and reduces load on source systems due to their relatively small size and low volatility.

7. How do aggregation tables improve performance in composite models?

A. By replacing DirectQuery with Import
B. By pre-summarizing data to satisfy queries without scanning detail tables
C. By eliminating the need for relationships
D. By enabling bidirectional filtering automatically

✅ Correct Answer: B

Explanation:
Aggregations allow Power BI to answer queries using pre-summarized Import tables, avoiding expensive queries against large DirectQuery or Direct Lake fact tables.

8. Which modeling pattern is strongly recommended when designing composite models?

A. Snowflake schema
B. Flat tables
C. Star schema
D. Many-to-many relationships

✅ Correct Answer: C

Explanation:
A star schema simplifies relationships, improves performance, and reduces ambiguity—especially important in composite and cross-storage-mode models.

9. What is a potential risk of excessive bidirectional relationships in composite models?

A. Reduced data freshness
B. Increased memory consumption
C. Ambiguous filter paths and unpredictable query behavior
D. Loss of row-level security

✅ Correct Answer: C

Explanation:
Bidirectional relationships can introduce ambiguity, cause unexpected filtering, and negatively affect query performance—risks that are amplified in composite models.

10. Which feature allows a composite model to reuse an enterprise semantic model while extending it with additional data?

A. Direct Lake
B. Import mode
C. Live connection with local tables
D. Calculation groups

✅ Correct Answer: C

Explanation:
A live connection with local tables enables extending a shared enterprise semantic model by adding new tables and measures, forming a composite model.

Analytics, BI Administration, Business Intelligence, Data Analysis, Data Development, Data Modeling, Data Strategy, Data Visualization, Data Warehousing, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI

Identify Use Cases for and Configure Large Semantic Model Storage Format

 
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%) 
    --> Design and build semantic models 
        --> Identify use cases for and configure large semantic model storage format

Overview

As datasets grow in size and complexity, standard semantic model storage can become a limiting factor. Microsoft Fabric (via Power BI semantic models) provides a Large Semantic Model storage format designed to support very large datasets, higher cardinality columns, and more demanding analytical workloads.

For the DP-600 exam, you are expected to understand when to use large semantic models, what trade-offs they introduce, and how to configure them correctly.

What Is the Large Semantic Model Storage Format?

The Large semantic model option changes how data is stored and managed internally by the VertiPaq engine to support:

  • Larger data volumes (beyond typical in-memory limits)
  • Higher column cardinality
  • Improved scalability for enterprise workloads

This setting is especially relevant in Fabric Lakehouse and Warehouse-backed semantic models where data size can grow rapidly.

Key Characteristics

  • Designed for enterprise-scale models
  • Supports very large tables and partitions
  • Optimized for memory management, not raw speed
  • Works best with Import mode or Direct Lake
  • Requires Premium capacity or Fabric capacity

Common Use Cases

1. Very Large Fact Tables

Use large semantic models when:

  • Fact tables contain hundreds of millions or billions of rows
  • Historical data is retained for many years
  • Aggregations alone are not sufficient

2. High-Cardinality Columns

Ideal when models include:

  • Transaction IDs
  • GUIDs
  • Timestamps at high granularity
  • User or device identifiers

Standard storage can struggle with memory pressure in these scenarios.

3. Enterprise-Wide Shared Semantic Models

Useful for:

  • Centralized datasets reused across many reports
  • Models serving hundreds or thousands of users
  • Organization-wide KPIs and analytics

4. Complex Models with Many Tables

When your model includes:

  • Numerous dimension tables
  • Multiple fact tables
  • Complex relationships

Large storage format improves stability and scalability.

5. Direct Lake Models Over OneLake

In Microsoft Fabric:

  • Large semantic models pair well with Direct Lake
  • Enable querying massive Delta tables without full data import
  • Reduce duplication of data between OneLake and the model

When NOT to Use Large Semantic Models

Avoid using large semantic models when:

  • The dataset is small or moderate in size
  • Performance is more critical than scalability
  • The model is used by a limited number of users
  • You rely heavily on fast interactive slicing

For smaller models, standard storage often provides better query performance.

Performance Trade-Offs

AspectStandard StorageLarge Storage
Memory efficiencyModerateHigh
Query speedFasterSlightly slower
Max model sizeLimitedMuch larger
Cardinality toleranceLowerHigher
Enterprise scalabilityLimitedHigh

Exam Tip: Large semantic models favor scalability over speed.

How to Configure Large Semantic Model Storage Format

Prerequisites

  • Fabric capacity or Power BI Premium
  • Import or Direct Lake storage mode
  • Dataset ownership permissions

Configuration Steps

  1. Open Power BI Desktop
  2. Go to Model view
  3. Select the semantic model
  4. In Model properties, locate Large dataset storage
  5. Enable the option
  6. Publish the model to Fabric or Power BI Service

Once enabled, the setting cannot be reverted back to standard storage.

Important Configuration Considerations

  • Enable before model grows significantly
  • Combine with:
    • Partitioning
    • Aggregation tables
    • Proper star schema design
  • Monitor memory usage in capacity metrics
  • Plan refresh strategies carefully

Relationship to DP-600 Exam Topics

This section connects directly with:

  • Storage mode selection
  • Semantic model scalability
  • Direct Lake and OneLake integration
  • Enterprise model design decisions

Expect scenario-based questions asking you to choose the appropriate storage format based on:

  • Data volume
  • Cardinality
  • Performance requirements
  • Capacity constraints

Key Takeaways for the Exam

  • Large semantic models support very large, complex datasets
  • Use large semantic models for scale, not speed
  • Best for enterprise-scale analytics
  • Ideal for high-cardinality, high-volume, enterprise models
  • Trade performance for scalability
  • Require Premium or Fabric capacity
  • One-way configuration—so, plan ahead
  • Often paired/combined with Direct Lake

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

1. When should you enable the large semantic model storage format?

A. When the model is used by a small number of users
B. When the dataset contains very large fact tables and high-cardinality columns
C. When query performance must be maximized for small datasets
D. When using Import mode with small dimension tables

Correct Answer: B

Explanation:
Large semantic models are designed to handle very large datasets and high-cardinality columns. Small or simple models do not benefit and may experience reduced performance.

2. Which storage modes support large semantic model storage format?

A. DirectQuery only
B. Import and Direct Lake
C. Live connection only
D. All Power BI storage modes

Correct Answer: B

Explanation:
Large semantic model storage format is supported with Import and Direct Lake modes. It is not applicable to Live connections or DirectQuery-only scenarios.

3. What is a primary trade-off when using large semantic model storage format?

A. Increased query speed
B. Reduced memory usage with no downsides
C. Slightly slower query performance in exchange for scalability
D. Loss of DAX functionality

Correct Answer: C

Explanation:
Large semantic models favor scalability and memory efficiency over raw query speed, which can be slightly slower compared to standard storage.

4. Which scenario is the best candidate for a large semantic model?

A. A departmental sales report with 1 million rows
B. A personal Power BI report with static data
C. An enterprise model with billions of transaction records
D. A DirectQuery model against a SQL database

Correct Answer: C

Explanation:
Large semantic models are ideal for enterprise-scale datasets with very large row counts and complex analytics needs.

5. What happens after enabling large semantic model storage format?

A. It can be disabled at any time
B. The model automatically switches to DirectQuery
C. The setting cannot be reverted
D. Aggregation tables are created automatically

Correct Answer: C

Explanation:
Once enabled, large semantic model storage format cannot be turned off, making early planning important.

6. Which capacity requirement applies to large semantic models?

A. Power BI Free
B. Power BI Pro
C. Power BI Premium or Microsoft Fabric capacity
D. Any capacity type

Correct Answer: C

Explanation:
Large semantic models require Premium capacity or Fabric capacity due to their increased resource demands.

7. Why are high-cardinality columns a concern in standard semantic models?

A. They prevent relationships from being created
B. They increase memory usage and reduce compression efficiency
C. They disable aggregations
D. They are unsupported in Power BI

Correct Answer: B

Explanation:
High-cardinality columns reduce VertiPaq compression efficiency, increasing memory pressure—one reason to use large semantic model storage.

8. Which Fabric feature commonly pairs with large semantic models for massive datasets?

A. Power Query Dataflows
B. DirectQuery
C. Direct Lake over OneLake
D. Live connection to Excel

Correct Answer: C

Explanation:
Large semantic models pair well with Direct Lake, allowing efficient querying of large Delta tables stored in OneLake.

9. Which statement best describes large semantic model performance?

A. Always faster than standard storage
B. Optimized for small, interactive datasets
C. Optimized for scalability and memory efficiency
D. Not compatible with DAX calculations

Correct Answer: C

Explanation:
Large semantic models prioritize scalability and efficient memory management, not maximum query speed.

10. Which design practice should accompany large semantic models?

A. Flat denormalized tables only
B. Star schema, aggregations, and partitioning
C. Avoid relationships entirely
D. Disable incremental refresh

Correct Answer: B

Explanation:
Best practices such as star schema design, aggregation tables, and partitioning are critical for maintaining performance and manageability in large semantic models.

Analytics, Business Intelligence, Business Intelligence (BI) Development, Data Analysis, Data Development, Data Modeling, Data Quality Assurance, Data Visualization, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI, Power Query, Reporting

Implement Calculation Groups, Dynamic Format Strings, and Field Parameters

 
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%) 
    --> Design and build semantic models 
        --> Implement Calculation Groups, Dynamic Format Strings, 
            and Field Parameters

This topic evaluates your ability to design flexible, scalable, and user-friendly semantic models by reducing measure sprawl, improving report interactivity, and standardizing calculations. These techniques are especially important in enterprise-scale Fabric semantic models.

1. Calculation Groups

What Are Calculation Groups?

Calculation groups allow you to apply a single calculation logic to multiple measures without duplicating DAX. Instead of creating many similar measures (e.g., YTD Sales, YTD Profit, YTD Margin), you define the logic once and apply it dynamically.

Calculation groups are implemented in:

  • Power BI Desktop (Model view)
  • Tabular Editor (recommended for advanced scenarios)

Common Use Cases

  • Time intelligence (YTD, MTD, QTD, Prior Year)
  • Currency conversion
  • Scenario analysis (Actual vs Budget vs Forecast)
  • Mathematical transformations (e.g., % of total)

Key Concepts

  • Calculation Item: A single transformation (e.g., YTD)
  • SELECTEDMEASURE(): References the currently evaluated measure
  • Precedence: Controls evaluation order when multiple calculation groups exist

Example

CALCULATE(
    SELECTEDMEASURE(),
    DATESYTD('Date'[Date])
)

This calculation item applies YTD logic to any measure selected in a visual.

Exam Tips

  • Calculation groups reduce model complexity
  • They cannot be created in Power BI Service
  • Be aware of interaction with existing measures and time intelligence

2. Dynamic Format Strings

What Are Dynamic Format Strings?

Dynamic format strings allow measures to change their formatting automatically based on context — without creating multiple measures.

Instead of hardcoding formats (currency, percentage, decimal), the format responds dynamically to user selections or calculation logic.

Common Scenarios

  • Showing % for ratios and currency for amounts
  • Switching formats based on calculation group selection
  • Applying regional or currency formats dynamically

How They Work

Each measure has:

  • A value expression
  • A format string expression

The format string expression returns a text format, such as:

  • "$#,##0.00"
  • "0.00%"
  • "#,##0"

Example

SWITCH(
    TRUE(),
    ISINSCOPE('Metrics'[Margin]), "0.00%",
    "$#,##0.00"
)

Exam Tips

  • Dynamic format strings do not change the underlying value
  • They are essential when using calculation groups
  • They improve usability without increasing measure count

3. Field Parameters

What Are Field Parameters?

Field parameters allow report consumers to dynamically switch dimensions or measures in visuals using slicers — without duplicating visuals or pages.

They are created in:

  • Power BI Desktop (Modeling → New Parameter → Fields)

Types of Field Parameters

  • Measure parameters (e.g., Sales, Profit, Margin)
  • Dimension parameters (e.g., Country, Region, Product)
  • Mixed parameters (less common, but supported)

Common Use Cases

  • Letting users choose which metric to analyze
  • Switching between time granularity (Year, Quarter, Month)
  • Reducing report clutter while increasing flexibility

How They Work

Field parameters:

  • Generate a hidden table
  • Are used in slicers
  • Dynamically change the field used in visuals

Example

A single bar chart can switch between:

  • Sales Amount
  • Profit
  • Profit Margin

Based on the slicer selection.

Exam Tips

  • Field parameters are report-layer features, not DAX logic
  • They do not affect data storage or model size
  • Often paired with calculation groups for advanced analytics

4. How These Features Work Together

In real-world Fabric semantic models, these three features are often combined:

FeaturePurpose
Calculation GroupsApply reusable logic
Dynamic Format StringsEnsure correct formatting
Field ParametersEnable user-driven analysis

Example Scenario

A report allows users to:

  • Select a metric (field parameter)
  • Apply time intelligence (calculation group)
  • Automatically display correct formatting (dynamic format string)

This design is highly efficient, scalable, and exam-relevant.

Key Exam Takeaways

  • Calculation groups reduce measure duplication; Calculation groups = reuse logic
  • SELECTEDMEASURE() is central to calculation groups
  • Dynamic format strings affect display, not values; Dynamic format strings = display control
  • Field parameters increase report interactivity; Field parameters = user-driven interactivity
  • These features are commonly tested together

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

What is the primary benefit of using calculation groups in a semantic model?

A. They improve data refresh performance
B. They reduce the number of fact tables
C. They allow reusable calculations to be applied to multiple measures
D. They automatically optimize DAX queries

Correct Answer: C

Explanation:
Calculation groups let you define a calculation once (for example, YTD) and apply it to many measures using SELECTEDMEASURE(), reducing measure duplication and improving maintainability.

Question 2

Which DAX function is essential when defining a calculation item in a calculation group?

A. CALCULATE()
B. SELECTEDVALUE()
C. SELECTEDMEASURE()
D. VALUES()

Correct Answer: C

Explanation:
SELECTEDMEASURE() dynamically references the measure currently being evaluated, which is fundamental to how calculation groups work.

Question 3

Where can calculation groups be created?

A. Power BI Service only
B. Power BI Desktop Model view or Tabular Editor
C. Power Query Editor
D. SQL endpoint in Fabric

Correct Answer: B

Explanation:
Calculation groups are created in Power BI Desktop (Model view) or using external tools like Tabular Editor. They cannot be created in the Power BI Service.

Question 4

What happens if two calculation groups affect the same measure?

A. The measure fails to evaluate
B. The calculation group with the highest precedence is applied first
C. Both calculations are ignored
D. The calculation group created most recently is applied

Correct Answer: B

Explanation:
Calculation group precedence determines the order of evaluation when multiple calculation groups apply to the same measure.

Question 5

What is the purpose of dynamic format strings?

A. To change the data type of a column
B. To modify measure values at query time
C. To change how values are displayed based on context
D. To improve query performance

Correct Answer: C

Explanation:
Dynamic format strings control how a measure is displayed (currency, percentage, decimals) without changing the underlying numeric value.

Question 6

Which statement about dynamic format strings is TRUE?

A. They change the stored data in the model
B. They require Power Query transformations
C. They can be driven by calculation group selections
D. They only apply to calculated columns

Correct Answer: C

Explanation:
Dynamic format strings are often used alongside calculation groups to ensure values are formatted correctly depending on the applied calculation.

Question 7

What problem do field parameters primarily solve?

A. Reducing model size
B. Improving data refresh speed
C. Allowing users to switch fields in visuals dynamically
D. Enforcing row-level security

Correct Answer: C

Explanation:
Field parameters enable report consumers to dynamically change measures or dimensions in visuals using slicers, improving report flexibility.

Question 8

When you create a field parameter in Power BI Desktop, what is generated automatically?

A. A calculated column
B. A hidden parameter table
C. A new measure
D. A new semantic model

Correct Answer: B

Explanation:
Power BI creates a hidden table that contains the selectable fields used by the field parameter slicer.

Question 9

Which feature is considered a report-layer feature rather than a modeling or DAX feature?

A. Calculation groups
B. Dynamic format strings
C. Field parameters
D. Measures using iterators

Correct Answer: C

Explanation:
Field parameters are primarily a report authoring feature that affects visuals and slicers, not the underlying model logic.

Question 10

Which combination provides the most scalable and flexible semantic model design?

A. Calculated columns and filters
B. Multiple duplicated measures
C. Calculation groups, dynamic format strings, and field parameters
D. Import mode and DirectQuery

Correct Answer: C

Explanation:
Using calculation groups for reusable logic, dynamic format strings for display control, and field parameters for interactivity creates scalable, maintainable, and user-friendly semantic models.

Analytics, Business Intelligence, Business Intelligence (BI) Development, Data Analysis, Data Development, Data Modeling, Data Warehousing, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI, Reporting, SQL

Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions

 
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%) 
    --> Design and build semantic models 
        --> Write calculations that use DAX variables and functions, such as 
            iterators, table filtering, windowing, and information functions

Why This Topic Matters for DP-600

DAX (Data Analysis Expressions) is the core language used to define business logic in Power BI and Fabric semantic models. The DP-600 exam emphasizes not just basic aggregation, but the ability to:

  • Write readable, efficient, and maintainable measures
  • Control filter context and row context
  • Use advanced DAX patterns for real-world analytics

Understanding variables, iterators, table filtering, windowing, and information functions is essential for building performant and correct semantic models.

Using DAX Variables (VAR)

What Are DAX Variables?

DAX variables allow you to:

  • Store intermediate results
  • Avoid repeating calculations
  • Improve readability and performance

Syntax

VAR VariableName = Expression
RETURN FinalExpression

Example

Total Sales (High Value) =
VAR Threshold = 100000
VAR TotalSales = SUM(FactSales[SalesAmount])
RETURN
IF(TotalSales > Threshold, TotalSales, BLANK())

Benefits of Variables

  • Evaluated once per filter context
  • Improve performance
  • Make complex logic easier to debug

Exam Tip:
Expect questions asking why variables are preferred over repeated expressions.

Iterator Functions

What Are Iterators?

Iterators evaluate an expression row by row over a table, then aggregate the results.

Common Iterators

FunctionPurpose
SUMXRow-by-row sum
AVERAGEXRow-by-row average
COUNTXRow-by-row count
MINX / MAXXRow-by-row min/max

Example

Total Line Sales =
SUMX(
    FactSales,
    FactSales[Quantity] * FactSales[UnitPrice]
)

Key Concept

  • Iterators create row context
  • Often combined with CALCULATE and FILTER

Table Filtering Functions

FILTER

Returns a table filtered by a condition.

High Value Sales =
CALCULATE(
    SUM(FactSales[SalesAmount]),
    FILTER(
        FactSales,
        FactSales[SalesAmount] > 1000
    )
)

Related Functions

FunctionPurpose
FILTERRow-level filtering
ALLRemove filters
ALLEXCEPTRemove filters except specified columns
VALUESDistinct values in current context

Exam Tip:
Understand how FILTER interacts with CALCULATE and filter context.

Windowing Functions

Windowing functions enable calculations over ordered sets of rows, often used for time intelligence and ranking.

Common Windowing Functions

FunctionUse Case
RANKXRanking
OFFSETRelative row positioning
INDEXRetrieve rows by position
WINDOWDefine dynamic row windows

Example: Ranking

Sales Rank =
RANKX(
    ALL(DimProduct),
    [Total Sales],
    ,
    DESC
)

Example Use Cases

  • Running totals
  • Moving averages
  • Period-over-period comparisons

Exam Note:
Windowing functions are increasingly emphasized in modern DAX patterns.

Information Functions

Information functions return metadata or context information rather than numeric aggregations.

Common Information Functions

FunctionPurpose
ISFILTEREDDetects column filtering
HASONEVALUEChecks if a single value exists
SELECTEDVALUEReturns value if single selection
ISBLANKChecks for blank results

Example

Selected Year =
IF(
    HASONEVALUE(DimDate[Year]),
    SELECTEDVALUE(DimDate[Year]),
    "Multiple Years"
)

Use Cases

  • Dynamic titles
  • Conditional logic in measures
  • Debugging filter context

Combining These Concepts

Real-world DAX often combines multiple techniques:

Average Monthly Sales =
VAR MonthlySales =
    SUMX(
        VALUES(DimDate[Month]),
        [Total Sales]
    )
RETURN
AVERAGEX(
    VALUES(DimDate[Month]),
    MonthlySales
)

This example uses:

  • Variables
  • Iterators
  • Table functions
  • Filter context awareness

Performance Considerations

  • Prefer variables over repeated expressions
  • Minimize complex iterators over large fact tables
  • Use star schemas to simplify DAX
  • Avoid unnecessary row context when simple aggregation works

Common Exam Scenarios

You may be asked to:

  • Identify the correct use of SUM vs SUMX
  • Choose when to use FILTER vs CALCULATE
  • Interpret the effect of variables on evaluation
  • Diagnose incorrect ranking or aggregation results

Correct answers typically emphasize:

  • Clear filter context
  • Efficient evaluation
  • Readable and maintainable DAX

Best Practices Summary

  • Use VAR / RETURN for complex logic
  • Use iterators only when needed
  • Control filter context explicitly
  • Leverage information functions for conditional logic
  • Test measures under multiple filter scenarios

Quick Exam Tips

  • VAR / RETURN = clarity + performance
  • SUMX ≠ SUM (row-by-row vs column aggregation)
  • CALCULATE = filter context control
  • RANKX / WINDOW = ordered analytics
  • SELECTEDVALUE = safe single-selection logic

Summary

Advanced DAX calculations are foundational to effective semantic models in Microsoft Fabric:

  • Variables improve clarity and performance
  • Iterators enable row-level logic
  • Table filtering controls context precisely
  • Windowing functions support advanced analytics
  • Information functions make models dynamic and robust

Mastering these patterns is essential for both real-world analytics and DP-600 exam success.

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

1. What is the primary benefit of using DAX variables (VAR)?

A. They change row context to filter context
B. They improve readability and reduce repeated calculations
C. They enable bidirectional filtering
D. They create calculated columns dynamically

Correct Answer: B

Explanation:
Variables store intermediate results that are evaluated once per filter context, improving performance and readability.

2. Which function should you use to perform row-by-row calculations before aggregation?

A. SUM
B. CALCULATE
C. SUMX
D. VALUES

Correct Answer: C

Explanation:
SUMX is an iterator that evaluates an expression row by row before summing the results.

3. Which statement best describes the FILTER function?

A. It modifies filter context without returning a table
B. It returns a table filtered by a logical expression
C. It aggregates values across rows
D. It converts row context into filter context

Correct Answer: B

Explanation:
FILTER returns a table and is commonly used inside CALCULATE to apply row-level conditions.

4. What happens when CALCULATE is used in a measure?

A. It creates a new row context
B. It permanently changes relationships
C. It modifies the filter context
D. It evaluates expressions only once

Correct Answer: C

Explanation:
CALCULATE evaluates an expression under a modified filter context and is central to most advanced DAX logic.

5. Which function is most appropriate for ranking values in a table?

A. COUNTX
B. WINDOW
C. RANKX
D. OFFSET

Correct Answer: C

Explanation:
RANKX assigns a ranking to each row based on an expression evaluated over a table.

6. What is a common use case for windowing functions such as OFFSET or WINDOW?

A. Creating relationships
B. Detecting blank values
C. Calculating running totals or moving averages
D. Removing duplicate rows

Correct Answer: C

Explanation:
Windowing functions operate over ordered sets of rows, making them ideal for time-based analytics.

7. Which information function returns a value only when exactly one value is selected?

A. HASONEVALUE
B. ISFILTERED
C. SELECTEDVALUE
D. VALUES

Correct Answer: C

Explanation:
SELECTEDVALUE returns the value when a single value exists in context; otherwise, it returns blank or a default.

8. When should you prefer SUM over SUMX?

A. When calculating expressions row by row
B. When multiplying columns
C. When aggregating a single numeric column
D. When filter context must be modified

Correct Answer: C

Explanation:
SUM is more efficient when simply adding values from one column without row-level logic.

9. Why can excessive use of iterators negatively impact performance?

A. They ignore filter context
B. They force bidirectional filtering
C. They evaluate expressions row by row
D. They prevent column compression

Correct Answer: C

Explanation:
Iterators process each row individually, which can be expensive on large fact tables.

10. Which combination of DAX concepts is commonly used to build advanced, maintainable measures?

A. Variables and relationships
B. Iterators and calculated columns
C. Variables, CALCULATE, and table functions
D. Information functions and bidirectional filters

Correct Answer: C

Explanation:
Advanced DAX patterns typically combine variables, CALCULATE, and table functions for clarity and performance.

Analytics, BI Administration, Business Intelligence, Business Intelligence (BI) Development, Data Analysis, Data Development, Data Integration, Data Integration (ETL), Data Modeling, Data Quality Assurance, Data Strategy, Data Visualization, Data Warehousing, Data Wrangling, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI, Power Query, Reporting, SQL

Implement Relationships, Such as Bridge Tables and Many-to-Many Relationships

 
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%) 
    --> Design and build semantic models 
        --> Implement Relationships, Such as Bridge Tables 
            and Many-to-Many Relationships

Why Relationships Matter in Semantic Models

In Microsoft Fabric and Power BI semantic models, relationships define how tables interact and how filters propagate across data. Well-designed relationships are critical for:

  • Accurate aggregations
  • Predictable filtering behavior
  • Correct DAX calculations
  • Optimal query performance

While one-to-many relationships are preferred, real-world data often requires handling many-to-many relationships using techniques such as bridge tables.

Common Relationship Types in Semantic Models

1. One-to-Many (Preferred)

  • One dimension row relates to many fact rows
  • Most common and performant relationship
  • Typical in star schemas

Example:

  • DimCustomer → FactSales

2. Many-to-Many

  • Multiple rows in one table relate to multiple rows in another
  • More complex filtering behavior
  • Can negatively impact performance if not modeled correctly

Example:

  • Customers associated with multiple regions
  • Products assigned to multiple categories

Understanding Many-to-Many Relationships

Native Many-to-Many Relationships

Power BI supports direct many-to-many relationships, but these should be used carefully.

Characteristics:

  • Cardinality: Many-to-many
  • Filters propagate ambiguously
  • DAX becomes harder to reason about

Exam Tip:
Direct many-to-many relationships are supported but not always recommended for complex models.

Bridge Tables (Best Practice)

A bridge table (also called a factless fact table) resolves many-to-many relationships by introducing an intermediate table.

What Is a Bridge Table?

A table that:

  • Contains keys from two related entities
  • Has no numeric measures
  • Enables controlled filtering paths

Example Scenario

Business case:
Products can belong to multiple categories.

Tables:

  • DimProduct (ProductID, Name)
  • DimCategory (CategoryID, CategoryName)
  • BridgeProductCategory (ProductID, CategoryID)

Relationships:

  • DimProduct → BridgeProductCategory (one-to-many)
  • DimCategory → BridgeProductCategory (one-to-many)

This converts a many-to-many relationship into two one-to-many relationships.

Benefits of Using Bridge Tables

BenefitDescription
Predictable filteringClear filter paths
Better DAX controlEasier to write and debug measures
Improved performanceAvoids ambiguous joins
ScalabilityHandles complex relationships cleanly

Filter Direction Considerations

Single vs Bidirectional Filters

  • Single direction (recommended):
    Filters flow from dimension → bridge → fact
  • Bidirectional:
    Can simplify some scenarios but increases ambiguity

Exam Guidance:

  • Use single-direction filters by default
  • Enable bidirectional filtering only when required and understood

Many-to-Many and DAX Implications

When working with many-to-many relationships:

  • Measures may return unexpected results
  • DISTINCTCOUNT is commonly required
  • Explicit filtering using DAX functions may be necessary

Common DAX patterns:

  • CALCULATE
  • TREATAS
  • CROSSFILTER (advanced)

Relationship Best Practices for DP-600

  • Favor star schemas with one-to-many relationships
  • Use bridge tables instead of direct many-to-many when possible
  • Avoid unnecessary bidirectional filters
  • Validate relationship cardinality and direction
  • Test measures under different filtering scenarios

Common Exam Scenarios

You may see questions like:

  • “How do you model a relationship where products belong to multiple categories?”
  • “What is the purpose of a bridge table?”
  • “What are the risks of many-to-many relationships?”

Correct answers typically emphasize:

  • Bridge tables
  • Controlled filter propagation
  • Avoiding ambiguous relationships

Star Schema vs Many-to-Many Models

FeatureStar SchemaMany-to-Many
ComplexityLowHigher
PerformanceBetterLower
DAX simplicityHighLower
Use casesMost analyticsSpecialized scenarios

Summary

Implementing relationships correctly is foundational to building reliable semantic models in Microsoft Fabric:

  • One-to-many relationships are preferred
  • Many-to-many relationships should be handled carefully
  • Bridge tables provide a scalable, exam-recommended solution
  • Clear relationships lead to accurate analytics and simpler DAX

Exam Tip

If a question involves multiple entities relating to each other, or many-to-many relationships, the most likely answer usually includes using a “bridge table”.

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

1. Which relationship type is generally preferred in Power BI semantic models?

A. Many-to-many
B. One-to-one
C. One-to-many
D. Bidirectional many-to-many

Correct Answer: C

Explanation:
One-to-many relationships provide predictable filter propagation, better performance, and simpler DAX calculations.

2. What is the primary purpose of a bridge table?

A. Store aggregated metrics
B. Normalize dimension attributes
C. Resolve many-to-many relationships
D. Improve data refresh performance

Correct Answer: C

Explanation:
Bridge tables convert many-to-many relationships into two one-to-many relationships, improving model clarity and control.

3. Which characteristic best describes a bridge table?

A. Contains numeric measures
B. Stores transactional data
C. Contains keys from related tables only
D. Is always filtered bidirectionally

Correct Answer: C

Explanation:
Bridge tables typically contain only keys (foreign keys) and no measures, enabling relationship resolution.

4. What is a common risk of using native many-to-many relationships directly?

A. They cannot be refreshed
B. They cause data duplication
C. They create ambiguous filter propagation
D. They are unsupported in Fabric

Correct Answer: C

Explanation:
Native many-to-many relationships can result in ambiguous filtering and unpredictable aggregation results.

5. In a bridge table scenario, how are relationships typically defined?

A. Many-to-many on both sides
B. One-to-one from both dimensions
C. One-to-many from each dimension to the bridge
D. Bidirectional many-to-one

Correct Answer: C

Explanation:
Each dimension connects to the bridge table using a one-to-many relationship.

6. When should bidirectional filtering be enabled?

A. Always, for simplicity
B. Only when necessary and well-understood
C. Only on fact tables
D. Never in semantic models

Correct Answer: B

Explanation:
Bidirectional filters can be useful but introduce complexity and ambiguity if misused.

7. Which scenario is best handled using a bridge table?

A. A customer has one address
B. A sale belongs to one product
C. A product belongs to multiple categories
D. A date table relates to a fact table

Correct Answer: C

Explanation:
Products belonging to multiple categories is a classic many-to-many scenario requiring a bridge table.

8. How does a properly designed bridge table affect DAX measures?

A. Makes measures harder to write
B. Requires custom SQL logic
C. Enables predictable filter behavior
D. Eliminates the need for CALCULATE

Correct Answer: C

Explanation:
Bridge tables create clear filter paths, making DAX behavior more predictable and reliable.

9. Which DAX function is commonly used to handle complex many-to-many filtering scenarios?

A. SUMX
B. RELATED
C. TREATAS
D. LOOKUPVALUE

Correct Answer: C

Explanation:
TREATAS is often used to apply filters across tables that are not directly related.

10. For DP-600 exam questions involving many-to-many relationships, which solution is typically preferred?

A. Direct many-to-many relationships
B. Denormalized fact tables
C. Bridge tables with one-to-many relationships
D. Duplicate dimension tables

Correct Answer: C

Explanation:
The exam emphasizes scalable, maintainable modeling practices — bridge tables are the recommended solution.

Analytics, Business Intelligence, Business Intelligence (BI) Development, Data Analysis, Data Development, Data Governance, Data Integration, Data Integration (ETL), Data Modeling, Data Quality Assurance, Data Security, Data Strategy, Data Visualization, Data Warehousing, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI, Power Query, Python, Reporting, SQL

Implement a Star Schema for a Semantic Model

 
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 
    --> Design and build semantic models 
        --> Implement a Star Schema for a Semantic Model

What Is a Star Schema?

A star schema is a logical data modeling pattern optimized for analytics and reporting. It organizes data into:

  • Fact tables: Contain numeric measurements (metrics) of business processes
  • Dimension tables: Contain descriptive attributes used for slicing, grouping, and filtering

The schema resembles a star: a central fact table with multiple dimensions radiating outward.

Why Use a Star Schema for Semantic Models?

Star schemas are widely used in Power BI semantic models (Tabular models) because they:

  • Improve query performance: Simplified joins and clear relationships enable efficient engine processing
  • Simplify reporting: Easy for report authors to understand and navigate
  • Support fast aggregations: Summary measures are computed more efficiently
  • Integrate with DAX naturally: Reduces complexity of measures

In DP-600 scenarios where performance and reusability matter, star schemas are often the best design choice.

Semantic Models and Star Schema

Semantic models define business logic that sits on top of data. Star schemas support semantic models by:

  • Providing clean dimensional context (e.g., Product, Region, Time)
  • Ensuring facts are centrally located for aggregations
  • Reducing the number of relationships and cycles
  • Enabling measures to be defined once and reused across visuals

Semantic models typically import star schema tables into Power BI, Direct Lake, or DirectQuery contexts.

Elements of a Star Schema

Fact Tables

A fact table stores measurable, numeric data about business events.

Examples:

  • Sales
  • Orders
  • Transactions
  • Inventory movements

Characteristics:

  • Contains foreign keys referring to dimensions
  • Contains numeric measures (e.g., quantity, revenue)

Dimension Tables

Dimension tables store contextual attributes that describe facts.

Examples:

  • Customer (name, segment, region)
  • Product (category, brand)
  • Date (calendar attributes)
  • Store or location

Characteristics:

  • Typically smaller than fact tables
  • Used to filter and group measures

Building a Star Schema for a Semantic Model

1. Identify the Grain of the Fact Table

The grain defines the level of detail in the fact table — for example:

  • One row per sales transaction per customer per day

Understand the grain before building dimensions.

2. Design Dimension Tables

Dimensions should be:

  • Descriptive
  • De-duplicated
  • Hierarchical where relevant (e.g., Country > State > City)

Example:

DimProductDimCustomerDimDate
ProductIDCustomerIDDateKey
NameNameYear
CategorySegmentQuarter
BrandRegionMonth

3. Define Relationships

Semantic models should have clear relationships:

  • Fact → Dimension: one-to-many
  • No ambiguous cycles
  • Avoid overly complex circular relationships

In a star schema:

  • Fact table joins to each dimension
  • Dimensions do not join to each other directly

4. Import into Semantic Model

In Power BI Desktop or Fabric:

  • Load fact and dimension tables
  • Validate relationships
  • Ensure correct cardinality
  • Mark the Date dimension as a Date table if appropriate

Benefits in Semantic Modeling

BenefitDescription
PerformanceSimplified relationships yield faster queries
UsabilityModel is intuitive for report authors
MaintenanceEasier to document and manage
DAX SimplicityMeasures use clear filter paths

DAX and Star Schema

Star schemas make DAX measures more predictable:

Example measure:

Total Sales = SUM(FactSales[SalesAmount])

With a proper star schema:

  • Filtering by dimension (e.g., DimCustomer[Region] = “West”) automatically propagates to the fact table
  • DAX measure logic is clean and consistent

Star Schema vs Snowflake Schema

FeatureStar SchemaSnowflake Schema
ComplexitySimpleMore complex
Query performanceTypically betterSlightly slower
Modeling effortLowerHigher
NormalizationLowHigh

For analytical workloads (like in Fabric and Power BI), star schemas are generally preferred.

When to Apply a Star Schema

Use star schema design when:

  • You are building semantic models for BI/reporting
  • Data is sourced from multiple systems
  • You need to support slicing and dicing by multiple dimensions
  • Performance and maintainability are priorities

Semantic models built on star schemas work well with:

  • Import mode
  • Direct Lake with dimensional context
  • Composite models

Common Exam Scenarios

You might encounter questions like:

  • “Which table should be the fact in this model?”
  • “Why should dimensions be separated from fact tables?”
  • “How does a star schema improve performance in a semantic model?”

Key answers will focus on:

  • Simplified relationships
  • Better DAX performance
  • Intuitive filtering and slicing

Best Practices for Semantic Star Schemas

  • Explicitly define date tables and mark them as such
  • Avoid many-to-many relationships where possible
  • Keep dimensions denormalized (flattened)
  • Ensure fact tables have surrogate keys linking to dimensions
  • Validate cardinality and relationship directions

Exam Tip

If a question emphasizes performance, simplicity, clear filtering behavior, and ease of reporting, a star schema is likely the correct design choice / optimal answer.

Summary

Implementing a star schema for a semantic model is a proven best practice in analytics:

  • Central fact table
  • Descriptive dimensions
  • One-to-many relationships
  • Optimized for DAX and interactive reporting

This approach supports Fabric’s goal of providing fast, flexible, and scalable analytics.

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

1. What is the primary purpose of a star schema in a semantic model?

A. To normalize data to reduce storage
B. To optimize transactional workloads
C. To simplify analytics and improve query performance
D. To enforce row-level security

Correct Answer: C

Explanation:
Star schemas are designed specifically for analytics. They simplify relationships and improve query performance by organizing data into fact and dimension tables.

2. In a star schema, what type of data is typically stored in a fact table?

A. Descriptive attributes such as names and categories
B. Hierarchical lookup values
C. Numeric measures related to business processes
D. User-defined calculated columns

Correct Answer: C

Explanation:
Fact tables store measurable, numeric values such as revenue, quantity, or counts, which are analyzed across dimensions.

3. Which relationship type is most common between fact and dimension tables in a star schema?

A. One-to-one
B. One-to-many
C. Many-to-many
D. Bidirectional many-to-many

Correct Answer: B

Explanation:
Each dimension record (e.g., a customer) can relate to many fact records (e.g., multiple sales), making one-to-many relationships standard.

4. Why are star schemas preferred over snowflake schemas in Power BI semantic models?

A. Snowflake schemas require more storage
B. Star schemas improve DAX performance and model usability
C. Snowflake schemas are not supported in Fabric
D. Star schemas eliminate the need for relationships

Correct Answer: B

Explanation:
Star schemas reduce relationship complexity, making DAX calculations simpler and improving query performance.

5. Which table should typically contain a DateKey column in a star schema?

A. Dimension tables only
B. Fact tables only
C. Both fact and dimension tables
D. Neither table type

Correct Answer: C

Explanation:
The fact table uses DateKey as a foreign key, while the Date dimension uses it as a primary key.

6. What is the “grain” of a fact table?

A. The number of rows in the table
B. The level of detail represented by each row
C. The number of dimensions connected
D. The data type of numeric columns

Correct Answer: B

Explanation:
Grain defines what a single row represents (e.g., one sale per customer per day).

7. Which modeling practice helps ensure optimal performance in a semantic model?

A. Creating relationships between dimension tables
B. Using many-to-many relationships by default
C. Keeping dimensions denormalized
D. Storing text attributes in the fact table

Correct Answer: C

Explanation:
Denormalized (flattened) dimension tables reduce joins and improve query performance in analytic models.

8. What happens when a dimension is used to filter a report in a properly designed star schema?

A. The filter applies only to the dimension table
B. The filter automatically propagates to the fact table
C. The filter is ignored by measures
D. The filter causes a many-to-many relationship

Correct Answer: B

Explanation:
Filters flow from dimension tables to the fact table through one-to-many relationships.

9. Which scenario is best suited for a star schema in a semantic model?

A. Real-time transactional processing
B. Log ingestion with high write frequency
C. Interactive reporting with slicing and aggregation
D. Application-level CRUD operations

Correct Answer: C

Explanation:
Star schemas are optimized for analytical queries involving aggregation, filtering, and slicing.

10. What is a common modeling mistake when implementing a star schema?

A. Using surrogate keys
B. Creating direct relationships between dimension tables
C. Marking a date table as a date table
D. Defining one-to-many relationships

Correct Answer: B

Explanation:
Dimensions should not typically relate to each other directly in a star schema, as this introduces unnecessary complexity.

Analytics, BI Administration, Business Intelligence, Business Intelligence (BI) Development, Data Analysis, Data Development, Data Governance, Data Integration, Data Integration (ETL), Data Modeling, Data Strategy, Data Visualization, Data Warehousing, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI, Power Query, Reporting

Choose a storage mode – additional information

 
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 
    --> Design and build semantic models 
        --> Choose a storage mode
This is supplemental information to what is included in the "Choose a storage mode" post.

DP-600 Cheat Sheet: Choosing a Storage Mode in Microsoft Fabric

Storage Mode Decision Matrix

Requirement / ScenarioImportDirectQueryDirect LakeComposite
Best query performance✅ Excellent❌ Depends on source✅ Excellent✅ Very good
Near real-time data❌ No✅ Yes✅ Yes✅ Yes
Large datasets (TB-scale)❌ Limited✅ Yes✅ Yes✅ Yes
Minimal refresh overhead❌ Requires refresh✅ No refresh✅ No refresh⚠ Partial
Uses OneLake Delta tables❌ Not required❌ Not required✅ Required✅ Optional
Full DAX & modeling features✅ Full support⚠ Limited⚠ Limited✅ Full
Calculated tables supported✅ Yes❌ No❌ No✅ Yes (Import tables only)
Lowest data duplication❌ High✅ None✅ None⚠ Mixed
Simple to manage✅ Yes⚠ Depends on source⚠ Fabric-specific❌ More complex

When to Choose Each Storage Mode

✅ Import Mode — Choose when:

  • Dataset fits comfortably in memory
  • You need complex DAX, calculated tables, or calculated columns
  • Performance is the top priority
  • Data freshness can be managed via scheduled refresh

Exam clue words: fastest, complex calculations, small to medium data

✅ DirectQuery — Choose when:

  • Data must always be current
  • Source system is highly optimized (SQL, Synapse, etc.)
  • Data volume is very large
  • You want zero data duplication

Exam clue words: real-time, source system, no refresh

✅ Direct Lake — Choose when:

  • Data is stored as Delta tables in OneLake
  • Dataset is large and frequently updated
  • You want Import-like performance without refresh
  • You’re working fully within Fabric

Exam clue words: OneLake, Delta, no refresh, Fabric-optimized

✅ Composite Model — Choose when:

  • You need flexibility across different tables
  • Fact tables are large and live (Direct Lake / DirectQuery)
  • Dimension tables are small and stable (Import)
  • You want performance and modeling flexibility

Exam clue words: hybrid, mix storage modes, dimension vs fact

Fast Exam Inclusion/Elimination Tips

  • Calculated tables required? → Import or Composite
  • OneLake + Delta tables? → Direct Lake
  • Real-time + external source? → DirectQuery
  • Best balance of flexibility and scale? → Composite

One-Sentence Exam Rule

If it’s in OneLake and too big to refresh, Direct Lake is usually the right answer.

Analytics, Business Intelligence, Data Analysis, Data Integration, Data Integration (ETL), Data Modeling, Data Strategy, Data Visualization, Data Warehousing, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI, Power Query, Reporting

Choose a Storage Mode

 
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%) 
    --> Design and build semantic models 
        --> Choose a storage mode

What Is Storage Mode?

In Microsoft Fabric, storage mode determines how a semantic model accesses and processes data. It affects performance, freshness, compute behavior, and model capabilities. Choosing the right storage mode is critical when designing semantic models for analytics and reporting.

A semantic model (Power BI dataset) can use different storage modes for its tables — and when multiple modes coexist, the model is called a composite model. DEV Community

Common Storage Modes

There are three primary storage modes you should know for the exam:

1. Import Mode

  • Stores data inside the semantic model in memory (VertiPaq) after a refresh. DEV Community
  • Offers fast query performance since data is cached locally.
  • Requires scheduled or manual refresh to update data from the source.
  • Supports the full range of modeling features (e.g., calculated tables, complex DAX).

When to use Import Mode:

  • Data fits in memory and doesn’t need real-time freshness.
  • You need complex calculations or modeling features requiring data in memory.
  • You want high performance for interactive analytics.

Pros:

  • Very fast interactive queries
  • Full DAX and modeling capabilities

Cons:

  • Must schedule refreshes
  • Data freshness depends on refresh cadence

2. DirectQuery Mode

  • Semantic model does not store data locally — queries are sent to the underlying source (SQL, warehouse, etc.) at query time. DEV Community
  • Ensures real-time or near-real-time data because no import refresh is needed.

When to use DirectQuery:

  • Source data changes frequently and must always show the latest results.
  • Data volumes are too large to import fully.

Pros:

  • Real-time access to source data
  • No refresh cycles required

Cons:

  • Performance depends heavily on source system
  • Some modeling features may be limited compared with Import

3. Direct Lake Mode

A newer, Fabric-specific storage mode designed to combine performance and freshness:

  • Reads Delta tables directly from OneLake and loads necessary column data into memory. Microsoft Learn
  • Avoids full data copy, eliminating the long import refresh cycle.
  • Uses the VertiPaq engine for fast aggregations and interactions (similar to import).
  • Offers low-latency synch with source changes without heavy refresh workloads.
  • Supports real-time insights while minimizing data movement. Microsoft Learn

When to use Direct Lake:

  • Working with extremely large datasets that would be costly or impractical to import entirely.
  • Needing relatively fresh data without long refresh cycles typical of Import mode.
  • Integrating tightly with delta-based assets such as Fabric lakehouses and warehouses. Microsoft Learn

Pros:

  • Fast querying with fresher data than import
  • No heavy refresh cycles
  • Leverages OneLake integration and existing delta tables

Cons:

  • Some modeling features (like calculated tables) are limited or not supported in Direct Lake tables (those tables must be switched to Import if needed). Microsoft Fabric Community
  • May fall back to DirectQuery in certain conditions (e.g., tables requiring SQL endpoint security). Microsoft Learn

Composite Models

A semantic model may include a mix of storage modes — for example, some tables in Direct Lake and others in Import. This is called a composite model. DEV Community

Typical use cases for composite models:

  • Import frequently used dimension tables (to support calculated tables)
  • Use Direct Lake for large fact tables stored in OneLake
  • Balance performance with modeling flexibility

Choosing the Right Storage Mode — Key Factors

When deciding on a storage mode for your semantic model, consider:

1. Data Freshness Requirements

  • Real-time data? → DirectQuery or Direct Lake
  • Static or periodic data? → Import

2. Dataset Size

  • Large volumes (multi-TB) without capacity for full import? → Direct Lake
  • Manageable size within memory? → Import

3. Modeling Features Needed

  • Complex measures, calculated tables, custom hierarchies? → Import (or mix)

4. Performance Needs

  • High interactive performance with good freshness? → Direct Lake
  • Ultimate speed with full caching? → Import

5. Source Capabilities

  • Some sources may not support DirectQuery efficiently — understand source performance.

Practical Examples

  • Import Mode: Small/medium enterprise data warehouse reporting that runs daily refreshes.
  • DirectQuery: Regulatory reporting where every query must reflect the latest operational data in a SQL system.
  • Direct Lake: Analytics on massive delta datasets stored in OneLake, where import is impractical but freshness and performance are both essential. Microsoft Learn

Exam Tips

  • Know what each mode does (Import vs DirectQuery vs Direct Lake).
  • Understand trade offs between performance, freshness, and modeling capability.
  • Recognize Direct Lake as a Fabric-optimized hybrid mode ideal for delta lake data.
  • Be prepared to choose the mode based on scenario requirements like latency, size, and features.

Summary

Storage ModeData LocationRefreshPerformanceBest Use Case
ImportIn model memoryScheduledVery fastSmaller datasets needing complex logic
DirectQuerySourceReal-timeSource-dependentReal-time needs
Direct LakeOneLake delta filesNear real-timeFast, scalableLarge datasets in OneLake Microsoft Learn

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

1. Which storage mode stores data fully in memory within the semantic model?

A. DirectQuery
B. Direct Lake
C. Import
D. Composite

Correct Answer: C. Import

Explanation:
Import mode loads data into the VertiPaq in-memory engine inside the semantic model, providing the fastest query performance but requiring refreshes.

2. Which storage mode provides real-time access to data by querying the source system at query time?

A. Import
B. DirectQuery
C. Direct Lake
D. Cached

Correct Answer: B. DirectQuery

Explanation:
DirectQuery does not store data locally. Each query is sent directly to the source system, ensuring real-time or near-real-time results.

3. What is a key advantage of Direct Lake compared to Import mode?

A. Supports more DAX functions
B. Requires no OneLake integration
C. Avoids full data refresh while maintaining high performance
D. Works only with SQL Server

Correct Answer: C. Avoids full data refresh while maintaining high performance

Explanation:
Direct Lake reads Delta tables directly from OneLake, avoiding large import refreshes while still using the VertiPaq engine for fast analytics.

4. Which scenario is best suited for Import mode?

A. A dataset requiring real-time updates every second
B. A small to medium dataset with complex DAX calculations
C. A multi-terabyte lakehouse fact table
D. Streaming event data

Correct Answer: B. A small to medium dataset with complex DAX calculations

Explanation:
Import mode supports the full range of modeling features and offers excellent performance for datasets that fit comfortably in memory.

5. Which storage mode is specifically optimized for Delta tables stored in OneLake?

A. Import
B. DirectQuery
C. Direct Lake
D. Hybrid

Correct Answer: C. Direct Lake

Explanation:
Direct Lake is a Fabric-optimized storage mode designed to work directly with Delta tables in OneLake.

6. A semantic model includes some tables in Import mode and others in Direct Lake mode. What is this called?

A. Hybrid model
B. Incremental model
C. Composite model
D. Federated model

Correct Answer: C. Composite model

Explanation:
A composite model uses multiple storage modes within the same semantic model, allowing flexibility between performance and freshness.

7. Which limitation applies to Direct Lake tables?

A. They cannot be refreshed
B. They do not support relationships
C. Calculated tables are not supported directly
D. They cannot be queried using DAX

Correct Answer: C. Calculated tables are not supported directly

Explanation:
Calculated tables require Import mode. Direct Lake tables must be switched to Import if calculated tables are needed.

8. What primarily determines query performance when using DirectQuery mode?

A. The VertiPaq engine
B. The refresh schedule
C. The source system’s performance
D. OneLake caching

Correct Answer: C. The source system’s performance

Explanation:
In DirectQuery mode, queries are executed against the source system, so performance depends on source optimization and capacity.

9. Which storage mode minimizes data duplication while still offering high query performance?

A. Import
B. DirectQuery
C. Direct Lake
D. Cached Import

Correct Answer: C. Direct Lake

Explanation:
Direct Lake avoids copying data into the model while still leveraging in-memory query acceleration, minimizing duplication and refresh overhead.

10. You need near real-time analytics on a very large dataset stored in OneLake without long refresh times. Which storage mode should you choose?

A. Import
B. DirectQuery
C. Direct Lake
D. Snapshot

Correct Answer: C. Direct Lake

Explanation:
Direct Lake is ideal for large OneLake datasets where full import refreshes are impractical but fast, fresh analytics are required.