In this Microsoft certified course, students will build on existing analytics experience and will learn to implement and manage a data analytics environment, query and transform data, implement and manage data models, and explore and visualize data.
In this Microsoft-certified course, students will build on their existing analytics experience and learn to implement and manage a data analytics environment, query and transform data, implement and manage data models, and explore and visualize data. In this course, students will use Microsoft Purview, Azure Synapse Analytics, and Power BI to build analytics solutions.
This training is a comprehensive preparation for the DP-500: Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI exam to earn the Microsoft Certified: Azure Enterprise Data Analyst Associate certification.
Training plan:
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: Understanding 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 PowerPoint.
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 pipeline activities.
Module 9: Introduction to Azure Synapse Analytics
Identify the business problems that Azure Synapse Analytics addresses.
Describe the 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 the 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 factss, dimensionss, 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 the fundamentals of the the fundamentals of the Power BI model.
Determine when to develop an import model.
Determine when to develop a direct query model.
Determine when to develop a composite model.
Choose an appropriate Power BI model framework.
Module 14: Understandinging 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 the the the best practices for implementing Power BI dataflows.
Create and consume Power BI dataflows.
Module 16: Create Power BI model relationships
Understand how model relationshipsss 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 a a a performance analyzer.
Troubleshoot DAX performance using DAX Studio.
Optimize a data model using the the the 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 an an an 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 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 direct query connections.
Use the the the Power BI service lineage view.
Use an an an XMLA endpoint to connect datasets.
Prerequisites:
Candidates for this certification should have advanced Power BI skills, including managing data repositories and data processing in the cloud and on-premises, along with using Power Query and Data Analysis Expressions (DAX). They should also be proficient in consuming data from Azure Synapse Analytics and have experience querying relational databases, analyzing data using Transact-SQL (T-SQL), and visualizing data.
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