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Guarantee your DP-500 exam success with examkiller's study guide. The DP-500 practice test questions are developed by experiences Microsoft Certification Professionals who working in todays prospering companies and Microsoft exam data center.

Exam Number: DP-500

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

Passing Score: 700 (Total Score: 1000)(Tips: You should pass 70% for each section of the exam (bar on the chart), or else you still faild the exam even your total score more than 700 )

Origin Provider: ExamKiller

Total Questions: 114 QAs

Type: Real Exam Questions

Guarantee: 100% Pass Guarantee

Demo: Click Here for Check Demo

Microsoft DP-500 Exam Objectives

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

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 (including the use of query folding)
• 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

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