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Exam: Implementing Analytics Solutions Using Microsoft FabricPurpose of this documentThis study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.NoteThe bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam DP-600 questions.NoteMost questions cover features that are general availability (GA). The DP-600 exam may contain questions on Preview features if those features are commonly used.Skills measuredAudience profileAs a candidate for this exam, you should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions.Please follow these practice Test Topics:Your responsibilities for this DP-600 course role include transforming data into reusable analytics assets by using Microsoft Fabric components, such as:· Lakehouses· Data warehouses· Notebooks· Dataflows· Data pipelines· Semantic models· ReportsYou implement analytics best practices in Fabric, including version control and deployment.To implement solutions as a Fabric analytics engineer, you partner with other roles, such as:· Solution architects· Data engineers· Data scientists· AI engineers· Database administrators· Power BI data analystsIn addition to in-depth work with the Fabric platform, you need experience with:· Data modeling· Data transformation· Git-based source control· Exploratory analytics· Languages, including Structured Query Language (SQL), Data Analysis Expressions (DAX), and PySparkSkills at a glance· Plan, implement, and manage a solution for data analytics (10-15%)· Prepare and serve data (40-45%)· Implement and manage semantic models (20-25%)· Explore and analyze data (20-25%)Plan, implement, and manage a solution for data analytics (10-15%)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 themeImplement 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 capacityManage 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 modelsPrepare and serve data (40-45%)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 tablesCopy 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· Add stored procedures, notebooks, and dataflows to a data pipeline· Schedule data pipelines· Schedule dataflows and notebooksTransform 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 dataOptimize 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 Delta table file sizesImplement and manage semantic models (20-25%)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 securityOptimize 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 refreshExplore and analyze data (20-25%)Perform exploratory analytics· Implement descriptive and diagnostic analytics· Integrate prescriptive and predictive analytics into a visual or report· Profile data·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

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