Overview
Level up with the new Microsoft Certified: Azure Enterprise Data Analyst Associate certification.
This Microsoft Azure Enterprise Data Analyst course covers methods and practices for performing advanced data analytics at scale. 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 course, students will use Microsoft Purview, Azure Synapse Analytics, and Power BI to build analytics solutions.
Skills Covered
- Implement and manage a data analytics environment
- Query and transform data
- Implement and manage data models
- Explore and visualize data
Who Should Attend
Candidates for this course should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions. Specifically, candidates 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 should have experience querying relational databases, analyzing data by using Transact-SQL (T-SQL), and visualizing data.
Course Curriculum
Prerequisites
Before attending this course, it is recommended that students have:
- A foundational knowledge of core data concepts and how they’re implemented using Azure data services. For more information see Azure Data Fundamentals.
- Experience designing and building scalable data models, cleaning and transforming data, and enabling advanced analytic capabilities that provide meaningful business value using Microsoft Power BI. For more information see Power BI Data Analyst
Download Course Syllabus
Course Modules
Understand analytics solutions in the Azure data ecosystem. Explore the architecture of a scalable analytics solution to meet business needs.
Learning objectives
After completing this module, you will be able to:
- Describe the Azure data ecosystem for analytics
Prerequisites
- You should be familiar with basic data concepts and terminology.
Explore key concepts of data analytics, including types of analytics, data, and storage. Explore the analytics process and tools used to discover insights.
Learning objectives
After completing this module, you will be able to:
- Describe types of data analytics
- Understand the data analytics process
Prerequisites
- You should be familiar with basic data concepts and terminology.
Describe data analytics at scale and understand the roles of a data team. Learn about the responsibilities of an enterprise data analyst and what tools are available to build scalable solutions.
Learning objectives
After completing this module, you will be able to:
- Explore data job roles in analytics
- Understand tools for scaling analytics solutions
Prerequisites
- You should be familiar with basic data concepts and terminology.
In this module, you’ll evaluate whether Microsoft Purview is the right choice for your data discovery and governance needs.
Learning objectives
By the end of this module, you’ll be able to:
- 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.
Prerequisites
- Knowledge of Microsoft Azure accounts and services
- Knowledge of various data sources such as SQL Server, Cosmos DB, etc.
- Knowledge of the concepts around data governance
Use Microsoft Purview Studio to discover trusted organizational assets for reporting.
Learning objectives
After completing this module, you’ll be able to:
- Browse, search, and manage data catalog assets.
- Use data catalog assets with Power BI.
- Use Microsoft Purview in Azure Synapse Studio.
Prerequisites
- Experience using the Azure data ecosystem.
Register, scan, catalog, and view data assets and their relevant details in Microsoft Purview.
Learning objectives
By the end of this module, you’ll be able to:
- Describe asset classification in Microsoft Purview.
Prerequisites
- Experience using the Azure data ecosystem.
Improve data governance and asset discovery using Power BI and Microsoft Purview integration.
Learning objectives
By the end of this module, you’ll be able to:
- 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.
Prerequisites
- Familiarity with the Azure data ecosystem.
Learn how to integrate Microsoft Purview with Azure Synapse Analytics to improve data discoverability and lineage tracking.
Learning objectives
After completing this module, you’ll be able to:
- 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.
Prerequisites
Before starting this module, you should be familiar with both Azure Synapse Analytics and Microsoft Purview. Consider completing the following modules before starting this one:
Learn about the features and capabilities of Azure Synapse Analytics – a cloud-based platform for big data processing and analysis.
Learning objectives
In this module, you’ll learn how to:
- Identify the business problems that Azure Synapse Analytics addresses.
- Describe core capabilities of Azure Synapse Analytics.
- Determine when to use Azure Synapse Analytics.
Prerequisites
Before completing this module, you should have the following prerequisite knowledge and experience:
- Familiarity with cloud computing concepts and Microsoft Azure.
- Familiarity with fundamental data concepts.
With Azure Synapse serverless SQL pool, you can leverage your SQL skills to explore and analyze data in files, without the need to load the data into a relational database.
Learning objectives
After the completion of this module, you will be able to:
- 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
Prerequisites
Consider completing the Explore data analytics in Azure and Get started querying with Transact-SQL learning paths before starting this module. You will need knowledge of:
- Analytical data workloads in Microsoft Azure
- Querying data with Transact-SQL
Apache Spark is a core technology for large-scale data analytics. Learn how to use Spark in Azure Synapse Analytics to analyze and visualize data in a data lake.
Learning objectives
After completing this module, you will be able to:
- 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.
Prerequisites
If you are not already familiar with Azure Synapse Analytics, consider completing the Introduction to Azure Synapse Analytics module before starting this module.
Relational data warehouses are a core element of most enterprise Business Intelligence (BI) solutions, and are used as the basis for data models, reports, and analysis.
Learning objectives
In this module, you’ll learn how to:
- 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.
Prerequisites
Before taking this module, you should have:
- An understanding of data fundamentals.
- Experience of querying data with Transact-SQL.
Describe model frameworks, their benefits and limitations, and features to help optimize your Power BI data models.
Learning objectives
By the end of this module, you’ll be able to:
- 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.
Prerequisites
- Experience developing Power BI data models, reports, and dashboards.
Scalable data models enable enterprise-scale analytics in Power BI. Implement data modeling best practices, use large dataset storage format, and practice building a star schema to design analytics solutions that can scale.
Learning objectives
By the end of this module, you’ll be able to:
- Describe the importance of building scalable data models
- Implement Power BI data modeling best practices
- Use the Power BI large dataset storage format
Prerequisites
Consider completing the Model data in Power BI learning path. You will need knowledge of:
- Power BI data model design including star schema design basics
Create Power BI transformation logic for reuse across your organization with Power BI dataflows. Learn how to combine Power BI dataflows with Power BI Premium for scalable ETL, and practice creating and consuming dataflows.
Learning objectives
By the end of this module, you’ll be able to:
- Describe Power BI dataflows and use cases.
- Describe best practices for implementing Power BI dataflows.
- Create and consume Power BI dataflows.
Prerequisites
- You’ll need knowledge of Power BI data model design including star schema design basics.
- Consider completing the Model data in Power BI learning path.
Power BI model relationships form the basis of a tabular model. Define Power BI model relationships, set up relationships, recognize DAX relationship functions, and describe relationship evaluation.
Learning objectives
By the end of this module, you’ll be able to:
- Understand how model relationship work.
- Set up relationships.
- Use DAX relationship functions.
- Understand relationship evaluation.
Prerequisites
- Experience developing Power BI data models by using Power BI Desktop.
By the end of this module, you’ll learn the meaning of time intelligence and how to add time intelligence DAX calculations to your model.
Learning objectives
By the end of this module, you’ll be able to:
- Define time intelligence.
- Use common DAX time intelligence functions.
- Create useful intelligence calculations.
Prerequisites
You should have experience creating Microsoft Power BI Desktop models and designing Power BI report layouts. You should also understand how to create Data Analysis Expressions (DAX) measures and how to work with iterator functions and filter context.
In this module you’ll learn what calculation groups are, explore key features and usage scenarios, and learn to create calculation groups.
Learning objectives
After completing this module, you will be able to:
- Explore how calculation groups work.
- Maintain calculation groups in a model.
- Use calculation groups in a Power BI report.
Prerequisites
- Experience creating reports using Power BI desktop.
- Basic understanding of Tabular Editor 2.
- Proficient with Data Analysis Expressions (DAX) in tabular models, at a basic level.
Enforce model security in Power BI using row-level security and object-level security.
Learning objectives
By the end of this module, you’ll be able to:
- 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.
Prerequisites
- Experience developing Power BI data models by using Power BI Desktop.
Use tools to develop, manage, and optimize Power BI data model and DAX query performance.
Learning objectives
After completing this module, you’ll be able to:
- Optimize queries using performance analyzer.
- Troubleshoot DAX performance using DAX Studio.
- Optimize a data model using Tabular Editor.
Prerequisites
- Experience designing and building Power BI data models.
Create cohesive, inclusive, and efficient Power BI reports to effectively communicate results.
Learning objectives
In this module, you’ll learn how to:
- 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.
Prerequisites
- Ability to visualize data and create reports in Power BI Desktop.
- Understanding of reports in the Power BI Service.
Describe real-time analytics in Power BI using automatic page refresh, real-time dashboards, and auto-refresh in paginated reports.
Learning objectives
By the end of this module, you’ll be able to:
- Describe Power BI real-time analytics.
- Set up automatic page refresh.
- Create real-time dashboards.
- Set up auto-refresh paginated reports.
Prerequisites
- Experience developing Power BI data models and creating reports and dashboards, and familiarity with DirectQuery storage mode.
Paginated reports allow report developers to create Power BI artifacts that have tightly controlled rendering requirements. Paginated reports are ideal for creating sales invoices, receipts, purchase orders, and tabular data. This module will teach you how to create reports, add parameters, and work with tables and charts in paginated reports.
Learning objectives
In this module, you will:
- Get data.
- Create a paginated report.
- Work with charts and tables on the report.
- Publish the report.
Prerequisites
Power BI paginated reports are a feature of Power BI Premium
Power BI governance is a set of rules, regulations, and policies that define and ensure the effective, controlled, and valuable operation of a BI environment. In this module, you’ll learn the fundamental components and practices necessary to govern a Power BI tenant.
Learning objectives
In this module, you will:
- 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
Prerequisites
Familiarity with Power BI and governance practices in a BI environment.
You’ve created dashboards and reports. Perhaps you want to collaborate on them with your coworkers. Or maybe you’re ready to distribute them more widely. What’s the best way to collaborate and share them? In this module, we compare your options.
Learning objectives
In this module, you will:
- 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
Prerequisites
We recommend completing the Dashboard in a Day learning path before beginning this module.
Usage metrics help you understand the impact of your dashboards and reports. When you run either dashboard usage metrics or report usage metrics, you discover how those dashboards and reports are being used throughout your organization, who’s using them, and for what purpose. Knowing who is taking what action on which item in your Power BI tenant can be critical in helping your organization fulfill its requirements, like meeting regulatory compliance and records management. This module outlines what is available in usage metrics reports and audit logs.
Learning objectives
In this module, you will:
- 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
Prerequisites
We recommend completing Dashboard in a Day before beginning this module.
Power BI Premium is a dedicated, capacity-based offering. Learn about the differences between Power BI Pro and Power BI Premium, and how Power BI Premium manages capacity resources. Featured tools you can use with Power BI premium are also covered.
Learning objectives
By the end of this module, you’ll be able to:
- 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.
Prerequisites
We recommend that you complete the Admin in a Day, part 1 learning path before beginning this module.
Working with on-premises data sources requires configuring a gateway between Power BI and the on-premises data source. This module examines how to work with gateways and SQL Server Analysis Services (SSAS) data sources that are used either for scheduled refresh or for live connections.
Learning objectives
By the end of this module, you’ll be able to:
- 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.
Prerequisites
- Familiarity with Power BI and governance practices in a BI environment.
- We recommend that you complete the Admin in a Day, part 1 learning path before beginning this module.
You can broaden the reach of Power BI by sharing your reports beyond your Power BI environment. You can publish reports to the public internet, embed reports in Microsoft Teams or in PowerApps, and place BI reports in a SharePoint online web part. There’s also a special version of Power BI service called Microsoft Power BI Embedded (PBIE) which allows application developers to embed fully interactive reports into their applications without having to build their own data visualizations and controls from scratch.
Learning objectives
By the end of this module, you’ll be able to:
- 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
Prerequisites
- Familiarity with Power BI and governance practices in a BI environment.
- We recommend completing the Dashboard in a Day learning path before beginning this module.
Cmdlets are functions written in PowerShell script language that execute commands in the Windows PowerShell environment. Running these cmdlets will allow you to interact with your Power BI Platform without having to go through the admin portal in a web browser. Combine these cmdlets with other PowerShell functions to write complex scripts that can optimize your workflow.
Learning objectives
By the end of this module, you’ll be able to:
- 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
Prerequisites
- Familiarity with Power BI and governance practices in a BI environment.
- We recommend completing the Dashboard in a Day learning path before beginning this module.
In this module, you will learn how you can build Power BI reports from within Azure Synapse Analytics.
Learning objectives
In this module, you will:
- Describe the Power BI and Synapse workspace integration
- Understand Power BI data sources
- Describe optimization options
- Visualize data with serverless SQL pools
Prerequisites
- It is recommended that students understand how Azure Synapse Analytics and Power BI works.
The use of OneDrive, Git repositories, and Power BI deployment pipelines allows us to follow application lifecycle management techniques. This reduces administrative overhead and provides continuity in the development process.
Learning objectives
Upon completion of this module, you should be able to:
- Outline the application lifecycle process.
- Choose a source control strategy.
- Design a deployment strategy.
Prerequisites
Experience with Power BI service. Awareness of application lifecycle management.
Deployment pipelines enable creators to develop and test Power BI content in the Power BI service before the content is made available for consumption by users. It offers creators improved productivity, faster delivery of content updates, and reduced manual work and errors. The tool is designed as a pipeline with three stages: development, test, and production.
Learning objectives
By the end of this module, you’ll be able to:
- 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
Prerequisites
You will be able to access and use the deployment pipelines feature if the following conditions are met:
- You have one of the following licenses:
- You’re a Power BI Pro user, and you belong to an organization that has Premium capacity.
- Premium Per User (PPU).
- You’re an admin of a new workspace experience.
Creating shared data assets for your analytics environment provides structure and consistency. Maintaining those assets is as important, and XMLA endpoint provides additional administrative capabilities.
Learning objectives
Upon completion of this module, you should be able to:
- Create specialized datasets.
- Create live and DirectQuery connections.
- Use Power BI service lineage view.
- Use XMLA endpoint to connect datasets.
Prerequisites
Intermediate experience with Power BI Desktop application and service.
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Training Options
- ILT: Instructor-Led Training
- VILT: Virtual Instructor-Led Training
Exam & Certification
Microsoft Certified: Azure Enterprise Data Analyst Associate
Are you ready to prove your skills at finding, collating, modeling, and analyzing data? Take your skills to the next level with Azure and Power BI. Get ready for Exam DP-500 (beta), and earn your Azure Enterprise Data Analyst Associate certification. Validate that you have the skills and experience to support your career, your team, and your organization and to help ensure that their business decisions and goals are based on a complete, informed view of the data.
Training & Certification Guide
Candidates for the Azure Enterprise Data Analyst Associate certification should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions.
Responsibilities for this role include performing advanced data analytics at scale, such as cleaning and transforming data, designing and building enterprise data models, incorporating advanced analytics capabilities, integrating with IT infrastructure, and applying development lifecycle practices. These professionals help collect enterprise-level requirements for data analytics solutions that include Azure and Microsoft Power BI. They also advise on data governance and configuration settings for Power BI administration, monitor data usage, and optimize performance of the data analytics solutions.
Azure enterprise data analysts collaborate with other roles, such as solution architects, data engineers, data scientists, AI engineers, database administrators, and Power BI data analysts.
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 should have experience querying relational databases, analyzing data by using Transact-SQL (T-SQL), and visualizing data.
Candidates for this exam 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 should have experience querying relational databases, analyzing data by using Transact-SQL (T-SQL), and visualizing data.
This exam measures your ability to accomplish the following technical tasks: implement and manage a data analytics environment; query and transform data; implement and manage data models; and explore and visualize data.
Skills measured
- Implement and manage a data analytics environment (25–30%)
- Query and transform data (20–25%)
- Implement and manage data models (25–30%)
- Explore and visualize data (20–25%)
Frequently Asked Questions
Unlock opportunities with enterprise-scale analytics solutions by using Azure and Power BI
Are you ready to prove your skills at finding, collating, modeling, and analyzing data? Take your skills to the next level with Azure and Power BI. Validate that you have the skills and experience to support your career, your team, and your organization and to help ensure that their business decisions and goals are based on a complete, informed view of the data.
Earning a Microsoft Certification is globally recognized and industry-endorsed evidence of mastering real world skills. It shows you demonstrate proficiency in keeping pace with technology. It’s a career move that yields many positive results.
Getting a Microsoft Certification is also a great way to break into the tech industry. A Microsoft Certification immediately confers a level of authority and expertise, especially helpful for someone new to the industry.
The number of questions on a certification exam is subject to change as Microsoft make updates to ensure it aligns with current changes in the technology and job role. Most Microsoft Certification exams typically contain between 40-60 questions; and around 60-140 minutes.
Starting June 30 2021, all newly earned role-based and specialty certifications will be valid for one year from the date the certification was earned.
To stay up to date, IT pros are constantly learning and adding skills. The IDC study concluded that Microsoft Learning Partners such as Trainocate Malaysia which won the 2021 Microsoft Learning Partner award are well positioned to help organizations achieve their business and learning goals. The IT leaders who were surveyed found the most value from a Learning Partner that provides:
- An end-to-end solution which starts with identifying skill gaps, simplifies the learning experience, and finishes by evaluating how well the Learning Partner met the organization goals.
- Scale, flexibility, and speed to train teams of any size, in any location, amid changing circumstances.
- Value-added services, such as hands-on labs, classroom training, and custom content that help the skills development program succeed.
- High-quality content and delivery, meaning accurate, relevant courseware, top-notch instructors, and a path to certification, if needed.
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Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.
DP-203T00: Data Engineering on Microsoft Azure
In this course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution.
DP-300T00: Administering Relational Databases on Microsoft Azure
This DP-300T00: Administering Relational Databases on Microsoft Azure course provides students with the knowledge and skills to administer a SQL Server database infrastructure for cloud, on-premises and hybrid relational databases and who work with the Microsoft PaaS relational database offerings.
DP-420T00: Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB
This course teaches developers how to create application using the SQL API and SDK for Azure Cosmos DB. Students will learn how to write efficient queries, create indexing policies, manage and provisioned resources, and perform common operations with the SDK.
