[2023 Refreshed DP-500 Dumps] for Microsoft Certified Azure Enterprise Data Analyst Associate

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

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Launch your career Azure Enterprise Data Analyst Associate with authentic DP-500 Dumps

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About DP-500 Exam:

This test assesses your ability to complete the following technical tasks: construct and manage a data analytics environment; query and convert data; develop and maintain data models; and explore and display data.

Responsibilities for this position involve doing sophisticated data analytics at scale, such as cleaning and converting data, developing and constructing corporate data models, including advanced analytics capabilities, interfacing with IT infrastructure, and using development lifecycle techniques. These individuals assist collect enterprise-level requirements for data analytics solutions that include Azure and Power BI.

They also advise on data governance and configuration settings for Power BI administration, monitor data utilization, and improve the performance of the data analytics solutions. Azure enterprise data analysts interact with other positions, such as solution architects, data engineers, data scientists, AI engineers, database administrators, and Power BI data analysts.

Who Are the Ideal Candidates?

Applicants for the Azure Enterprise Data Analyst Associate certification need to demonstrate that they have subject matter knowledge in the areas of developing, implementing and delivering enterprise-scale data analytics solutions.

Candidates for this exam should have advanced knowledge of Power BI, including the ability to manage data repositories and perform data processing in the cloud and on-premises, as well as the ability to use Power Query and Data Analysis Expressions (DAX). They should also be skilled in consuming data from Azure Synapse Analytics and have expertise querying relational databases, analyzing data by utilizing Transact-SQL (T-SQL), and visualizing data. Additionally, they should be proficient in consuming data from Azure Data Lake.

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What Domains are Covered in the DP-500 Exam:

This exam assesses your ability to complete the technical topics listed below, based on the most recent update from Microsoft Learning.

  • 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%)

Remember that the bullets after each of the skills to be evaluated are supposed to show how we are assessing that competency. This isn’t a comprehensive or final list.

Also keep in mind that the majority of questions are about things that are open to everyone (GA). If you use the Preview features frequently, the exam may include questions about them.

Now look at the detailed contents of the course:

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

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Exam Information

Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI
$165 USD
Approximately 40-60
700 (on a scale of 1000)
120 Minutes

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