Microsoft Fabric Analytics Engineer Associate Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Plan, implement, and manage a solution for data analytics (12.5% of the exam)

Plan a data analytics environment

• Identify requirements for a solution, including components, features, performance, and capacity stock-keeping units (SKUs)
• Recommend settings in the Fabric admin portal
• Choose a data gateway type
• Create a custom Power BI report theme

Summary: This section emphasizes setting a strong foundation for the analytics environment. You will learn how to assess the requirements of a data solution, from performance needs to capacity considerations, ensuring that every component is optimized to work together seamlessly. This includes identifying appropriate tools, evaluating features, and aligning technical decisions with business goals.

Equally important is configuring the environment to match organizational standards. You will work with the Fabric admin portal for secure and efficient settings, select data gateway types to enable proper connectivity, and create Power BI report themes that establish visual consistency. By mastering these essentials, you gain the ability to build a trustworthy and scalable environment for enterprise analytics.

Implement and manage a data analytics environment

• Implement workspace and item-level access controls for Fabric items
• Implement data sharing for workspaces, warehouses, and lakehouses
• Manage sensitivity labels in semantic models and lakehouses
• Configure Fabric-enabled workspace settings
• Manage Fabric capacity and configure capacity settings

Summary: This section focuses on the governance and management aspects of a data analytics setup. You will implement control at the workspace and item level, ensuring that data access remains secure while allowing for proper collaboration. With capabilities like sensitivity labels and workspace configuration, you will learn to enforce compliance and maintain data integrity across solutions.

Beyond access management, the section explores the operational aspects of capacity in Fabric. You will gain practical knowledge on monitoring and allocating capacity resources to meet demand, tuning capacity settings to optimize performance during peak workloads. Ultimately, this content highlights how governance and performance management come together to support both security and scalability.

Manage the analytics development lifecycle

• Implement version control for a workspace
• Create and manage a Power BI Desktop project (.pbip)
• Plan and implement deployment solutions
• Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models
• Deploy and manage semantic models by using the XMLA endpoint
• Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models

Summary: This section is about mastering the processes that ensure quality and continuity in analytics development. By applying version control and structured deployment strategies, you ensure that changes are tracked accurately, preserving organizational knowledge and reducing risks during updates. Managing Power BI Desktop projects and reusable files further facilitates repeatability across teams and projects, ensuring consistency in deliverables.

Another focus is analyzing dependencies before deployment. Evaluating downstream objects like dataflows or warehouses helps identify impacts of changes, enabling proactive planning. With XMLA endpoint management, semantic models also become more flexible and robust. Together, these practices ensure that analytics solutions can evolve smoothly while maintaining alignment with broader business processes.

Domain 2: Prepare and serve data (42.5% of the exam)

Create objects in a lakehouse or warehouse

• Ingest data by using a data pipeline, dataflow, or notebook
• Create and manage shortcuts
• Implement file partitioning for analytics workloads in a lakehouse
• Create views, functions, and stored procedures
• Enrich data by adding new columns or tables

Summary: This section centers on creating structures that allow efficient data ingestion and management. You will use data pipelines, dataflows, and notebooks to bring data into lakehouses or warehouses, gaining expertise in choosing the right method for various scenarios. As part of this, the creation of shortcuts and partitions ensures that large-scale workloads remain optimized for query performance and storage efficiency.

Beyond ingestion, attention is given to extending and enriching data models. By creating views, functions, procedures, and new tables, you enhance usability for analytics while maintaining flexibility for future scaling. These foundational skills empower you to structure the data layer in a way that drives both performance and usability.

Copy data

• Choose an appropriate method for copying data from a Fabric data source to a lakehouse or warehouse
• Copy data by using a data pipeline, dataflow, or notebook
• Implement Fast Copy when using dataflows
• Add stored procedures, notebooks, and dataflows to a data pipeline
• Schedule data pipelines
• Schedule dataflows and notebooks

Summary: This section focuses on moving data efficiently within Fabric environments. You will develop skills for identifying the best approach, depending on source and destination needs, while mastering tools like dataflows, pipelines, and notebooks for data movement. The concept of Fast Copy allows for handling large volumes of data at speed, a key consideration for enterprise-scale systems.

Automation stands out as another critical element. By scheduling dataflows, pipelines, and notebooks, you can ensure that processes run on time and with consistency. This not only optimizes operations for recurring tasks but also helps maintain up-to-date datasets that support timely business analysis.

Transform data

• Implement a data cleansing process
• Implement a star schema for a lakehouse or warehouse, including Type 1 and Type 2 slowly changing dimensions
• Implement bridge tables for a lakehouse or a warehouse
• Denormalize data
• Aggregate or de-aggregate data
• Merge or join data
• Identify and resolve duplicate data, missing data, or null values
• Convert data types by using SQL or PySpark
• Filter data

Summary: In this section, you learn to refine raw data into business-ready forms. This involves implementing common transformations, such as cleansing, handling duplicates or nulls, and converting data types to align with system requirements. Creating star schemas and slowly changing dimensions enables better modeling practices that directly support business intelligence and reporting.

Advanced design approaches are also emphasized. You will build bridge tables, denormalize data where useful, and apply aggregate or join operations that reshape the structure of datasets. By mastering these methods, you empower organizations to gain clean, structured, and reliable data that fuels accurate insights and reporting.

Optimize performance

• Identify and resolve data loading performance bottlenecks in dataflows, notebooks, and SQL queries
• Implement performance improvements in dataflows, notebooks, and SQL queries
• Identify and resolve issues with the structure or size of Delta table files (including v-order and optimized writes)

Summary: This section ensures you can recognize and address areas of inefficiency in data operations. By analyzing performance issues across dataflows, notebooks, and queries, you strengthen the ability to maintain smooth and reliable workloads. Understanding where bottlenecks arise allows you to apply remedies quickly and efficiently.

Performance optimization goes further, covering structural improvements to underlying files and datasets. Adjustments to Delta files, such as v-order and optimized writes, can significantly reduce query times and improve scalability as datasets grow. These skills make you capable of maintaining systems that remain responsive and efficient even under enterprise-scale workloads.

Domain 3: Implement and manage semantic models (22.5% of the exam)

Design and build semantic models

• Choose a storage mode, including Direct Lake
• Identify use cases for DAX Studio and Tabular Editor 2
• 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 strings, and field parameters
• Design and build a large format dataset
• Design and build composite models that include aggregations
• Implement dynamic row-level security and object-level security
• Validate row-level security and object-level security

Summary: This section covers how semantic models are structured to serve analytics needs. You will learn to design models using star schemas, relationships, and appropriate storage modes while leveraging tools like DAX Studio and Tabular Editor 2 for validation and optimization. Mastery of DAX functions is central, as it enables complex calculations across large datasets.

Security is also highlighted as a vital capability, including implementing row-level and object-level security, and validating those implementations. By complementing strong design standards with robust governance, these models ensure scalability and compliance without sacrificing usability.

Optimize enterprise-scale semantic models

• Implement performance improvements in queries and report visuals
• Improve DAX performance by using DAX Studio
• Optimize a semantic model by using Tabular Editor 2
• Implement incremental refresh

Summary: This section emphasizes optimization strategies that keep semantic models performing at their best. You will focus on refining query performance and streamlining report visuals to ensure responsive reporting, even with large-scale enterprise datasets. Using DAX Studio and Tabular Editor 2, you sharpen both efficiency and maintainability across solutions.

Incremental refresh is another powerful technique highlighted here, enabling efficient updates to large datasets without overburdening resources. Together, these practices allow semantic models to serve as enterprise-class solutions, supporting analytical workloads with high performance and low latency.

Domain 4: Explore and analyze data (22.5% of the exam)

Perform exploratory analytics

• Implement descriptive and diagnostic analytics
• Integrate prescriptive and predictive analytics into a visual or report
• Profile data

Summary: This section shows how to explore and analyze data to generate meaningful insights. You will use techniques to perform descriptive and diagnostic analysis, helping stakeholders understand both what has happened and why. Profiling data provides a broader perspective on quality and patterns, forming the basis for deeper exploration.

You will also integrate predictive and prescriptive analytics into reports, combining technical modeling with visual storytelling. These practices empower organizations to go beyond understanding trends, enabling forward-looking insights that support smarter and faster decision-making.

Query data by using SQL

• Query a lakehouse in Fabric by using SQL queries or the visual query editor
• Query a warehouse in Fabric by using SQL queries or the visual query editor
• Connect to and query datasets by using the XMLA endpoint

Summary: This section emphasizes applying SQL skills to work directly with Fabric resources. Whether querying lakehouses or warehouses through SQL or the visual editor, you gain the flexibility to meet diverse business needs and user preferences. These querying skills allow you to unlock structured insights quickly and efficiently.

The ability to query datasets connected through XMLA endpoints further extends your reach. This expertise ensures smooth integration into enterprise environments that require robust connections across multiple data sources. By mastering these techniques, you bring clear and actionable insights directly into the analytics workflow.