Describe the Azure data ecosystem for analytics
Describe types of data analytics
Understand the data analytics process
Explore data job roles in analytics
Understand tools for scaling analytics solutions
Evaluate whether Microsoft Purview is appropriate for data discovery and governance needs.
Describe how the features of Microsoft Purview work to provide data discovery and governance.
Browse, search, and manage data catalog assets.
Use data catalog assets with Power BI.
Use Microsoft Purview in Azure Synapse
Describe asset classification in Microsoft Purview.
Register and scan a Power BI tenant.
Use the search and browse functions to find data assets.
Describe the schema details and data lineage tracing of Power BI data assets.
Catalog Azure Synapse Analytics database assets in Microsoft Purview.
Configure Microsoft Purview integration in Azure Synapse Analytics.
Search the Microsoft Purview catalog from Synapse Studio.
Track data lineage in Azure Synapse Analytics pipelines activities.
Identify the business problems that Azure Synapse Analytics addresses.
Describe core capabilities of Azure Synapse Analytics.
Determine when to use Azure Synapse Analytics.
Identify capabilities and use cases for serverless SQL pools in Azure Synapse Analytics
Query CSV, JSON, and Parquet files using a serverless SQL pool
Create external database objects in a serverless SQL pool
Identify core features and capabilities of Apache Spark.
Configure a Spark pool in Azure Synapse Analytics.
Run code to load, analyze, and visualize data in a Spark notebook.
Design a schema for a relational data warehouse.
Create fact, dimension, and staging tables.
Use SQL to load data into data warehouse tables.
Use SQL to query relational data warehouse tables.
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 an appropriate Power BI model framework.
Describe the importance of building scalable data models
Implement Power BI data modeling best practices
Use the Power BI large dataset storage format
Describe Power BI dataflows and use cases.
Describe best practices for implementing Power BI dataflows.
Create and consume Power BI dataflows.
Understand how model relationship work.
Set up relationships.
Use DAX relationship functions.
Understand relationship evaluation.
Define time intelligence.
Use common DAX time intelligence functions.
Create useful intelligence calculations.
Explore how calculation groups work.
Maintain calculation groups in a model.
Use calculation groups in a Power BI report.
Restrict access to Power BI model data with RLS.
Restrict access to Power BI model objects with OLS.
Apply good development practices to enforce Power BI model security.
Optimize queries using performance analyzer.
Troubleshoot DAX performance using DAX Studio.
Optimize a data model using Tabular Editor.
Create and import a custom report theme.
Create custom visuals with R or Python.
Enable personalized visuals in a report.
Review report performance using Performance Analyzer.
Design and configure Power BI reports for accessibility.
Describe Power BI real-time analytics.
Set up automatic page refresh.
Create real-time dashboards.
Set up auto-refresh paginated reports.
Create a paginated report.
Work with charts and tables on the report.
Publish the report.
Define the key components of an effective BI governance model
Describe the key elements associated with data governance
Configure, deploy, and manage elements of a BI governance strategy
Set up BI help and support settings
Understand the differences between My workspace, workspaces, and apps
Describe new workspace capabilities and how they improve the user experience
Anticipate migration impact to Power BI users
Share, publish to the web, embed links and secure Power BI reports, dashboards, and content
Discover what usage metrics are available through the Power BI admin portal
Optimize use of usage metrics for dashboards and reports
Distinguish between audit logs and the activity logs
Describe the difference between Power BI Pro and Power BI Premium
Define dataset eviction
Explain how Power BI manages memory resources
List three external tools you can use with Power BI Premium.
Understand the difference between gateways, the various connectivity modes, and data refresh methods.
Describe the gateway network requirements, where to place the gateway in your network, and how to use clustering to ensure high availability.
Scale, monitor, and manage gateway performance and users.
Describe the various embedding scenarios that allow you to broaden the reach of Power BI
Understand the options for developers to customize Power BI solutions
Learn to provision and optimize Power BI embedded capacity and create and deploy dataflows
Build custom Power BI solutions template apps
Use REST APIs to automate common Power BI admin tasks
Apply Power BI Cmdlets for Windows PowerShell and PowerShell core
Use Power BI Cmdlets
Automate common Power BI admin tasks with scripting
Describe the Power BI and Synapse workspace integration
Understand Power BI data sources
Describe optimization options
Visualize data with serverless SQL pools
Outline the application lifecycle process.
Choose a source control strategy.
Design a deployment strategy.
Articulate the benefits of deployment pipelines
Create a deployment pipeline using Premium workspaces
Assign and deploy content to pipeline stages
Describe the purpose of deployment rules
Deploy content from one pipeline stage to another
Create specialized datasets.
Create live and DirectQuery connections.
Use Power BI service lineage view.
Use XMLA endpoint to connect datasets.