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How to Prepare For Microsoft DP-900: Microsoft Azure Data Fundamentals Exam
Preparation Guide for Microsoft DP-900: Microsoft Azure Data Fundamentals Exam
Introduction
If you're looking to enter the IT world but don't have the education required, you may want to consider taking one of the many certification exams offered by respected tech companies. Microsoft offers a range of certifications, including those for developers. The DP-900 exam is one such exam. This article will help you prepare for this exam so that it will be successful and lead to your certification. Microsoft DP-900 exam dumps are one of the most popular resources for DP-900 exam preparation.
General Overview of Microsoft DP-900 Exam
The Microsoft DP-900 exam evaluates the individuals’ understanding of core data concepts, analytics workloads on Azure, as well as how to function with relational and non-relational data within Azure. The test contains 40-60 questions and the applicants have 90 minutes for the completion. The students or instructors who want to take this exam are required to schedule it through Certiport while the non-students are to schedule the test with Pearson VUE. The fee for the exam is $99 and it is available in a range of languages, including English, Spanish, French, Korean, German, Japanese, and Simplified Chinese.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-900
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Microsoft DP-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Relational Data in Azure | 20-25% | - Querying relational data - Relational data concepts - Azure relational data services
|
| Topic 2: Analytics Workloads in Azure | 25-30% | - Data ingestion and processing - Data analytics concepts - Data visualization and reporting - Azure analytics services
|
| Topic 3: Non-Relational Data in Azure | 15-20% | - Azure storage services
- Data processing options for non-relational data |
| Topic 4: Core Data Concepts | 25-30% | - Types of core data workloads - Data analytics workloads overview - Roles and responsibilities in data work - Relational vs non-relational data |
