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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

How to Register For Exam DP-900: Microsoft Azure Data Fundamentals?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=DP-900&examname=Exam%20DP-900:%20Microsoft%20Azure%20Data%20Fundamentals&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fdp-900

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Microsoft DP-900 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Non-Relational Data in Azure15-20%- Data processing options for non-relational data
- Azure storage services
  • 1. Azure Table Storage
    • 2. Azure Blob Storage
      • 3. Azure Cosmos DB
        - Non-relational data concepts
        Core Data Concepts25-30%- Data analytics workloads overview
        - Roles and responsibilities in data work
        - Relational vs non-relational data
        - Types of core data workloads
        Relational Data in Azure20-25%- Relational data concepts
        - Querying relational data
        - Azure relational data services
        • 1. Azure SQL Database
          • 2. SQL Server on Azure VMs
            • 3. Azure SQL Managed Instance
              Analytics Workloads in Azure25-30%- Data analytics concepts
              - Azure analytics services
              • 1. Azure Synapse Analytics
                • 2. Power BI
                  • 3. Azure Data Lake Storage
                    - Data ingestion and processing
                    - Data visualization and reporting

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