Microsoft Certified: Azure Enterprise Data Analyst Associate (DP500)

Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI (DP-500T00)

Module 1: Explore Azure data services for modern analytics

• Describe the Azure data ecosystem for analytics

Module 2: Understand concepts of data analytics

• Describe types of data analytics
• Understand the data analytics process

Module 3: Explore data analytics at scale

• Explore data job roles in analytics
• Understand tools for scaling analytics solutions

Module 4: Introduction to Microsoft Purview

• 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.

Module 5: Discover trusted data using Microsoft Purview

• Browse, search, and manage data catalog assets.
• Use data catalog assets with Power BI.
• Use Microsoft Purview in Azure Synapse

Module 6: Catalog data artifacts by using Microsoft Purview

• Describe asset classification in Microsoft Purview.

Module 7: Manage Power BI assets by using 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.

Module 8: Integrate Microsoft Purview and Azure Synapse Analytics

• 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.

Module 9: Introduction to Azure Synapse Analytics

• Identify the business problems that Azure Synapse Analytics addresses.
• Describe core capabilities of Azure Synapse Analytics.
• Determine when to use Azure Synapse Analytics.

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

• 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
• Module 11: Analyze data with Apache Spark in Azure Synapse Analytics
• 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.

Module 12: Analyze data in a relational data warehouse

• 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.

Module 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 an appropriate Power BI model framework.

Module 14: Understand scalability in Power BI

• Describe the importance of building scalable data models
• Implement Power BI data modeling best practices
• Use the Power BI large dataset storage format

Module 15: Create and manage scalable Power BI dataflows

• Describe Power BI dataflows and use cases.
• Describe best practices for implementing Power BI dataflows.
• Create and consume Power BI dataflows.

Module 16: Create Power BI model relationships

• Understand how model relationship work.
• Set up relationships.
• Use DAX relationship functions.
• Understand relationship evaluation.

Module 17: Use DAX time intelligence functions in Power BI Desktop models

• Define time intelligence.
• Use common DAX time intelligence functions.
• Create useful intelligence calculations.

Module 18: Create calculation groups

• Explore how calculation groups work.
• Maintain calculation groups in a model.
• Use calculation groups in a Power BI report.

Module 19: Enforce Power BI model security

• 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.

Module 20: Use tools to optimize Power BI performance

• Optimize queries using performance analyzer.
• Troubleshoot DAX performance using DAX Studio.
• Optimize a data model using Tabular Editor.

Module 21: Understand advanced data visualization concepts

• 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.

Module 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.

Module 23: Create paginated reports

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

Module 24: Provide governance in a Power BI environment

• 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

Module 25: Facilitate collaboration and sharing in Power BI

• 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

Module 26: Monitor and audit usage

• 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

Module 27: Provision Premium capacity in Power BI

• 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.

Module 28: Establish a data access infrastructure in Power BI

• 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.

Module 29: Broaden the reach of Power BI

• 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

Module 30: Automate Power BI administration

• 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

Module 31: Build reports using Power BI within Azure Synapse Analytics

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

Module 32: Design a Power BI application lifecycle management strategy

• Outline the application lifecycle process.
• Choose a source control strategy.
• Design a deployment strategy.

Module 33: Create and manage a Power BI deployment pipeline

• 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

Module 34: Create and manage Power BI assets

• Create specialized datasets.
• Create live and DirectQuery connections.
• Use Power BI service lineage view.
• Use XMLA endpoint to connect datasets.