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How to Prepare For Microsoft DP-500 Certification Exam
Preparation Guide for the Microsoft DP-500 Certification Exam
Complete Guide of Microsoft DP-500 Certification Exam
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Enterprise analytics solutions helps knowledge workers and their management with decision-making and thus improving their business performance. They transform business data into useful information that can be provided to a large number of stakeholders via intuitive interfaces and collaboration capabilities. You would want your enterprise to benefit from the possibilities of enterprise analytics. DP-500 exam focuses on providing foundation skills on implementing enterprise analytics solutions using Power BI, Excel PivotTable reports, Excel Power Query, Excel Data Model and Analysis Services cubes/models. Microsoft DP-500 Dumps are the best way to get the most out of your certification. There is no better alternative to Microsoft DP-500 practice test than using the latest Microsoft DP-500 Exam Questions.
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Briefly explain the Key Features of Microsoft DP-500 Exam
Microsoft DP-500 exam is an important certification exam, which is part of the Microsoft Data Analytics Solutions certification.
The following are the key features of Microsoft DP-500 exam:
This exam tests your ability to design a secure, reliable, and high-performing cloud solution for business analytics.
Following Topics:
Designing and implementing enterprise scale analytics solutions using SQL Server 2016 Databases
Designing and implementing enterprise scale analytics solutions using Microsoft Azure
Designing and implementing enterprise scale analytics solutions using Microsoft Azure HDInsight
Schedule exam
Languages: English
Retirement date: none
This exam measures your ability to accomplish the following technical tasks: implement and manage a data analytics environment; query and transform data; implement and manage data models; and explore and visualize data.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-500
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Microsoft DP-500 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement and Manage Data Models | 30-35% | - Design scalable semantic models
|
| Topic 2: Explore and Visualize Data | 20-25% | - Analyze data
|
| Topic 3: Query and Transform Data | 20-25% | - Transform and prepare data
|
| Topic 4: Implement and Manage a Data Analytics Environment | 25-30% | - Manage analytics lifecycle
|








