1 – Explore Azure data services for modern analytics

• Understand the Azure data ecosystem
• Explore modern analytics solution architecture

2 – Understand concepts of data analytics

• Understand data analytics types
• Explore the data analytics process
• Understand types of data and data storage

3 – Explore data analytics at scale

• Explore data team roles and responsibilities
• Review tasks and tools for data analysts
• Scale analytics with Azure Synapse Analytics and Power BI
• Strategies to scale analytics

4 – Introduction to Microsoft Purview

• What is Microsoft Purview?
• How Microsoft Purview works
• When to use Microsoft Purview

5 – Discover trusted data using Microsoft Purview

• Search for assets
• Browse assets
• Use assets with Power BI
• Integrate with Azure Synapse Analytics

6 – Catalog data artifacts by using Microsoft Purview

• Register and scan data
• Classify and label data
• Search the data catalog

7 – Manage Power BI assets by using Microsoft Purview

• Register and scan a Power BI tenant
• Search and browse Power BI assets
• View Power BI metadata and lineage

8 – Integrate Microsoft Purview and Azure Synapse Analytics

• Catalog Azure Synapse Analytics data assets in Microsoft Purview
• Connect Microsoft Purview to an Azure Synapse Analytics workspace
• Search a Purview catalog in Synapse Studio
• Track data lineage in pipelines

9 – Introduction to Azure Synapse Analytics

• What is Azure Synapse Analytics
• How Azure Synapse Analytics works
• When to use Azure Synapse Analytics

10 – Use Azure Synapse serverless SQL pool to query files in a data lake

• Understand Azure Synapse serverless SQL pool capabilities and use cases
• Query files using a serverless SQL pool
• Create external database objects

11 – Analyze data with Apache Spark in Azure Synapse Analytics

• Get to know Apache Spark
• Use Spark in Azure Synapse Analytics
• Analyze data with Spark
• Visualize data with Spark

12 – Analyze data in a relational data warehouse

• Design a data warehouse schema
• Create data warehouse tables
• Load data warehouse tables
• Query a data warehouse

13 – Choose a Power BI model framework

• Describe Power BI model fundamentals
• Determine when to develop an import model
• Determine when to develop a DirectQuery model
• Determine when to develop a composite model
• Choose a model framework

14 – Understand scalability in Power BI

• Describe the significance of scalable models
• Implement Power BI data modeling best practices
• Configure large datasets

15 – Create and manage scalable Power BI dataflows

• Define use cases for dataflows
• Create reusable assets
• Implement best practices

16 – Create Power BI model relationships

• Understand model relationships
• Set up relationships
• Use DAX relationship functions
• Understand relationship evaluation

17 – Use DAX time intelligence functions in Power BI Desktop models

• Use DAX time intelligence functions
• Additional time intelligence calculations

18 – Create calculation groups

• Understand calculation groups
• Explore calculation groups features and usage
• Create calculation groups in a model

19 – Enforce Power BI model security

• Restrict access to Power BI model data
• Restrict access to Power BI model objects
• Apply good modeling practices

20 – Use tools to optimize Power BI performance

• Use Performance analyzer
• Troubleshoot DAX performance by using DAX Studio
• Optimize a data model by using Best Practice Analyzer

21 – Understand advanced data visualization concepts

• Create and import a custom report theme
• Enable personalized visuals in a report
• Design and configure Power BI reports for accessibility
• Create custom visuals with R or Python
• Review report performance using Performance Analyzer

22 – Monitor data in real-time with Power BI

• Describe Power BI real-time analytics
• Set up automatic page refresh
• Create real-time dashboards
• Set-up auto-refresh paginated reports

23 – Create paginated reports

• Get data
• Create a paginated report
• Work with charts on the report
• Publish the report

24 – Provide governance in a Power BI environment

• Elements of data governance
• Configure tenant settings
• Deploy organizational visuals
• Manage embed codes
• Help and support settings

25 – Monitor and audit usage

• Usage metrics for dashboards and reports
• Usage metrics for dashboards and reports – new version
• Audit logs
• Activity log

26 – Broaden the reach of Power BI

• REST API custom development
• Provision a Power BI embedded capacity
• Dataflow introduction
• Dataflow explained
• Create a Dataflow
• Dataflow capabilities on Power BI Premium
• Template apps – install packages
• Template apps – installed entities
• Template app governance

27 – Build reports using Power BI within Azure Synapse Analytics

• Describe the Power BI and Synapse workspace integration
• Understand Power BI data sources
• Describe Power BI optimization options
• Visualize data with serverless SQL pools

28 – Design a Power BI application lifecycle management strategy

• Define application lifecycle management
• Recommend a source control strategy
• Design a deployment strategy

29 – Create and manage a Power BI deployment pipeline

• Understand the deployment process
• Create a deployment pipeline
• Assign a workspace
• Deploy content
• Work with deployment pipelines

30 – Create and manage Power BI assets

• Create reusable Power BI assets
• Explore Power BI assets using lineage view
• Manage a Power BI dataset using XMLA endpoint