Building Cloud Native Apps with Azure

• 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%)

• 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

• 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

• 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

• 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

• 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 notebooks

• 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

• 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 sizes

• 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

• 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

• Implement descriptive and diagnostic analytics

• Integrate prescriptive and predictive analytics into a visual or report

• Profile data

• 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

2 Days

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Microsoft Fabric

Microsoft Fabric