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Exam DP-500: Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI
Candidates for this exam should have advanced Power BI skills, including managing data repositories and data processing in the cloud and on-premises, along with using Power Query and Data Analysis Expressions (DAX). They should also be proficient in consuming data from Azure Synapse Analytics and should have experience querying relational databases, analyzing data by using Transact-SQL (T-SQL), and visualizing data.
Passing score: 700. Learn more about exam scores.
Part of the requirements for: Microsoft Certified: Azure Enterprise Data Analyst Associate
What is the difficulty to write Microsoft DP-500 Exam
The Microsoft DP-500 exam is a difficult exam to pass. This exam is designed and structured by the experts of Microsoft. The questions are designed by keeping in mind the real world scenarios to test your knowledge and skills.
The candidates who have a dream to become an expert in Enterprise Scale Analytics Solutions Using Microsoft Azure should be very careful while choosing their exam preparation material.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-500
Get to know about the Objective of Microsoft DP-500 Certification
The objective of Microsoft DP-500 certification is to validate your skills in building data warehouses, implementing a data warehouse solution, developing ETL processes, creating and managing data marts, designing business intelligence solutions and reporting.
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DP-500 exam is intended for:
Business Analysts who want to demonstrate their knowledge of designing and implementing enterprise scale analytics solutions
Database Administrators who are responsible for managing the design, implementation and maintenance of the database environment for an organization
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Microsoft DP-500 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement and Manage Data Models | 30-35% | - Implement advanced modeling techniques
|
| Topic 2: Implement and Manage a Data Analytics Environment | 25-30% | - Govern and manage data assets
|
| Topic 3: Query and Transform Data | 20-25% | - Transform and prepare data
|
| Topic 4: Explore and Visualize Data | 20-25% | - Analyze data
|
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