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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.

Microsoft DP-900: Microsoft Azure Data Fundamentals Exam Certified Professional salary

The average salary of a Microsoft DP-900: Microsoft Azure Data Fundamentals Exam Certified Expert in

  • Europe - 60,547 EURO
  • England - 60,532 POUNDS
  • India - 12,10,327 INR
  • United States - 80,000 USD

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:

SectionWeightObjectives
Topic 1: Describe an analytics workload on Azure25–30%- Describe big data and analytics concepts
  • 1. Data ingestion and processing
    • 2. Modern data warehouse architecture
      • 3. Data lakes and data warehouses
        - Describe Azure analytics services
        • 1. Azure Data Lake Storage Gen2
          • 2. Azure Synapse Analytics
            • 3. Microsoft Fabric
              • 4. Azure HDInsight
                • 5. Azure Databricks
                  - Describe data visualization
                  • 1. Data models and reports
                    • 2. Power BI capabilities
                      - Describe real-time analytics
                      • 1. Stream processing concepts
                        • 2. Azure Stream Analytics
                          Topic 2: Identify considerations for relational data on Azure20–25%- Describe relational data concepts
                          • 1. Relationships, keys, constraints
                            • 2. Query data with SQL
                              • 3. Normalization
                                - Describe Azure relational data services
                                • 1. Azure SQL Database
                                  • 2. SQL Server on Azure Virtual Machines
                                    • 3. Azure SQL Managed Instance
                                      • 4. Open-source databases: PostgreSQL, MySQL, MariaDB
                                        Topic 3: Describe considerations for working with non-relational data on Azure15–20%- Describe Azure Cosmos DB
                                        • 1. Capabilities and features
                                          • 2. APIs and data models
                                            • 3. Consistency levels and partitioning
                                              - Describe Azure storage services
                                              • 1. Table Storage
                                                • 2. Blob Storage
                                                  • 3. Queue Storage
                                                    • 4. File Storage
                                                      - Describe non-relational data concepts
                                                      • 1. Use cases for non-relational data
                                                        • 2. Characteristics of NoSQL data
                                                          Topic 4: Describe core data concepts25–30%- Describe roles and responsibilities
                                                          • 1. Data engineer, data analyst, database administrator
                                                            - Describe types of data
                                                            • 1. Structured, semi-structured, and unstructured data
                                                              • 2. File formats
                                                                - Describe data workloads
                                                                • 1. Batch and streaming data
                                                                  • 2. Analytical workloads
                                                                    • 3. Transactional workloads

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