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Azure Enterprise Data Analyst Associate

About this course

Enroll in Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI (DP 500) training course from Microsoft.
In this course you will learn about methods and practices for performing advanced data analytics at scale

Instructor-Led Virtual Live Class

Exam DP-500: Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI

Description

DP-500 exam (Enterprise Data Analyst) covers a broad range of skills and tools – starting with Power BI advanced features and concepts, all the way to Azure Synapse Analytics and Microsoft Purview.

Requirements

Some experience with Power BI and/or Azure

Who this course is for:

  • A Power BI developer who wants to further increase their skills
  • An IT specialist (solution architect, database administrator, data scientist) of an organization where Power BI is being used
  • Anyone looking for additional ways to improve their understanding of Power BI and illustrate their knowledge by earning this new Microsoft certification

Course Outline

Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI 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%)

 

Implement and manage a data analytics environment (25–30%)

  • Manage Power BI assets by using Azure Purview
  • Identify data sources in Azure by using Azure Purview
  • Recommend settings in the Power BI admin portal
  • Recommend a monitoring and auditing solution for a data analytics environment, including
  • Identify requirements for a solution, including features, performance, and licensing strategy
  • Configure and manage Power BI capacity
  • Recommend and configure an on-premises gateway in Power BI
  • Recommend and configure a Power BI tenant or workspace to integrate with Azure Data Lake

Storage Gen2

  • Integrate an existing Power BI workspace into Azure Synapse Analytics

Manage the analytics development lifecycle

  • Commit code and artifacts to a source control repository in Azure Synapse Analytics
  • Recommend a deployment strategy for Power BI assets
  • Recommend a source control strategy for Power BI assets
  • Implement and manage deployment pipelines in Power BI
  • Perform impact analysis of downstream dependencies from dataflows and datasets
  • Recommend automation solutions for the analytics development lifecycle, including Power BI
  • Deploy and manage datasets by using the XMLA endpoint
  • Create reusable assets, including Power BI templates, Power BI data source (.pbids) files, and shared datasets

 

Query and transform data (20–25%)

  • Identify an appropriate Azure Synapse pool when analyzing data

Recommend appropriate file types for querying serverless SQL pools

  • Query relational data sources in dedicated or serverless SQL pools, including querying partitioned data sources
  • Use a machine learning PREDICT function in a query
  • Identify data loading performance bottlenecks in Power Query or data sources
  • Implement performance improvements in Power Query and data sources
  • Create and manage scalable Power BI dataflows
  • Identify and manage privacy settings on data sources
  • Create queries, functions, and parameters by using the Power Query Advanced Editor
  • Query advanced data sources, including JSON, Parquet, APIs, and Azure Machine Learning models

Implement and manage data models (25–30%)

  • Choose when to use DirectQuery for Power BI datasets
  • Choose when to use external tools, including DAX Studio and Tabular Editor 2
  • Create calculation groups
  • Write calculations that use DAX variables and functions, for example handling blanks or errors, creating virtual relationships, and working with iterators
  • Design and build a large format dataset
  • Design and build composite models, including aggregations
  • Design and implement enterprise-scale row-level security and object-level security
  • Identify and implement performance improvements in queries and report visuals
  • Troubleshoot DAX performance by using DAX Studio
  • Optimize a data model by using Tabular Editor 2
  • Analyze data model efficiency by using VertiPaq Analyzer
  • Implement incremental refresh
  • Optimize a data model by using denormalization

Explore and visualize data (20–25%)

  • Explore data by using native visuals in Spark notebooks
  • Explore and visualize data by using the Azure Synapse SQL results pane
  • Create and import a custom report theme
  • Create R or Python visuals in Power BI
  • Connect to and query datasets by using the XMLA endpoint
  • Design and configure Power BI reports for accessibility
  • Enable personalized visuals in a report
  • Configure automatic page refresh
  • Create and distribute paginated reports in Power BI Report Builder

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