Following is the info about the Passing Score, Duration & Questions for the Microsoft DP-500 Exam
The passing score: 700
Languages: English
Number of Questions: 40-60 questions
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-500
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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.
The best way to pass this difficult certification is to practice with the help of real time developed questions as it will help you to get rid of all your confusions. You can easily find this kind of questions in DP-500 exam dumps that are created according to the latest syllabus of DP-500 Exam. These real time developed questions are available in PDF format with answers for each question so that you can easily read them without any hassle.
You can also get free updates for one year from the date of purchase if any new changes happen regarding this exam.
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Microsoft DP-500 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement and Manage a Data Analytics Environment | 25-30% | - Administer analytics environments
|
| Implement and Manage Data Models | 30-35% | - Design scalable semantic models
|
| Explore and Visualize Data | 20-25% | - Analyze data
|
| Query and Transform Data | 20-25% | - Query data sources
|
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