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

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

About Microsoft Power BI  (DP-500)  Exam

Professionals planning to take the Azure Enterprise Data Analyst Associate exam are required to have subject matter expertise to design, create, and deploy enterprise-scale data analytics solutions. You will be required to assist in collecting enterprise-level requirements for data analytics solutions for Azure and Power BI. 

Knowledge Required

Candidates taking the AZ-500 exam are required to have skills including -

• Advanced Power BI skills like managing data repositories
• Data processing in the cloud and on-premises
• Using Power Query and Data Analysis Expressions (DAX)
• Proficiency in consuming data from Azure Synapse Analytics
• Experience querying relational databases
• Analyzing data by using Transact-SQL (T-SQL)
• Visualizing data.
  

Roles and Responsibilities

Candidates are required to perform roles that include -

• Performing advanced data analytics at scale like cleaning and transforming data
• Designing and building enterprise data models
• Incorporating advanced analytics capabilities
• Integrating with IT infrastructure
• Applying development lifecycle practices.
• Monitoring data usage
• Advice on data governance and configuration settings for Power BI administration
• Optimizing the performance of the data analytics solutions
  

Who should take the exam?

Candidates taking the Azure enterprise data analysts exam perform the roles including -

• Solution architects
• Data engineers
• Data scientists
• AI engineers
• Database administrators
• Power BI Data Analysts

Course Outline

The Microsoft DP-500 exam covers the following topics as per exam updates as of February 6, 2023 - 

Domain 1 - Implement and manage a data analytics environment (25–30%)

Govern and administer a data analytics environment

• manage Power BI assets by using Microsoft Purview
• identify data sources in Azure by using Microsoft 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

Domain 2 - 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

Domain 3 - 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

Domain 4 - 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