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Exam DP-500: Enterprise scale analytics solutions using Azure and Power BI

This certification is for highly experienced Power BI developers who are able to create Enterprise-scale data analytics solutions using Azure Synapse Analytics as a back-end. Candidates have responsibility for managing large datasets and reporting solutions in Power BI with appropriate Governance and controls.

Candidates likely have a Power BI Administrator role in their organisation, or at least are working closely with Power BI admins to drive adoption of the technology. This syllabus covers the architecture required for Enterprise self-service (e.g., Shared Datasets and Dataflows hosted on Power BI Premium). It also covers licencing and how to manage and monitor the Power BI estate using Power BI REST APIs to build a monitoring solution.

For Power BI administrators, this kind of solution provides oversight of what’s been developed, what content is being consumed and who it’s been shared with. The integration with Azure Purview is also covered which will allow users to scan and catalogue entire data estate to understand data lineage and how sensitive data assets are being controlled.

A core part of the syllabus is Azure Synapse Analytics – leveraging SQL serverless (where data is queried directly from Azure Data Lake Gen 2), exploring data using native visuals in spark notebooks and considering how Power BI integrates with Azure Synapse analytics. In the scenarios covered in this syllabus, Azure is used as the Enterprise tooling for Extract, Transform and Load (ETL) operations with version control/Application Lifecycle Management (ALCM) and deployment methodologies to ensure performance at scale.

Performance management and enterprise deployment methodologies are also reflected downstream in the Power BI artefacts – published versions of large, shared datasets are managed as Analysis Services models hosted in the Power BI service and accessed via the XMLA endpoint using External tools such as DAX Studio and Tabular editor. The performance of these datasets is managed by optimising the data model (considering star schema/denormalization), choosing the optimal architecture (e.g. the mix between Import mode and Direct Query for Composite models/aggregations), optimising DAX expressions and considering how to fine tune refresh performance using features such as Incremental Refresh. Artefacts are deployed using Deployment pipelines and Azure Dev Ops for version control/Application Lifecycle Management (ALCM) in Power BI.

Data visualisation is also a key aspect of an Enterprise level solution and the syllabus covers how to develop custom visualisations using R/Python and how to choose the most appropriate tooling to display the required insights and features. For example, the uses cases for Paginated Reports are considered as well as designing/configuring Power BI reports for accessibility, and enabling end user customisation through features such as Personalize Visuals.

The full syllabus is covered (along with some sample LDI questions!) in the Turkey Power BI User Group meetup session below!

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

Govern and administer a data analytics environment

  • 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 Power BI REST API and PowerShell cmdlets

 

Integrate an analytics platform into an existing IT infrastructure

  • 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 REST API and PowerShell cmdlets
  • 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%)

Query data by using Azure Synapse Analytics

  • 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

Ingest and transform data by using Power BI

  • 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%)

Design and build tabular models

  • 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

Optimize enterprise-scale data models

  • 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 Azure Synapse Analytics

  • Explore data by using native visuals in Spark notebooks
  • Explore and visualize data by using the Azure Synapse SQL results pane

Visualize data by using Power BI

  • 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