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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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The candidates for the Microsoft DP-900 test will be measured in a variety of technical skill areas. These domains are divided into several topics. It is recommended that the applicants review the exam topics in detail and choose the appropriate study materials to prepare for the test. Below are the highlights of the objectives covered in this certification exam:

  • Explain Analytics Workloads on Azure: 25-30%

    This domain of the DP-900 exam measures one’s knowledge of analytics workloads, data processing, and ingestion on Azure, as well as the elements of modern data warehouses. The candidates will also be tested on their understanding of data visualization within Microsoft Power BI. This involves describing the specific role of dashboards, paginated reporting, interactive reports, and workflows in Power BI.

  • Explain how Non-Relational Data Works on Azure: 25-30%

    The questions from this section will evaluate the understanding of the candidates in non-relational data workloads as well as non-relational data contributions on Azure. They must be able to explain Azure File storage, Azure Cosmos DB APIs, Azure Blob Storage, and Azure Table storage. They should also be able to identify the foundational management tasks for non-relational data.

  • Explain the Concepts of Core Data: 15-20%

    Within this subject area, the students should be able to explain different categories of core data workloads that are available. To answer questions from this subtopic, the examinees should be able to explain batch data and streaming data. They should also know the attributes of relational data and core differences between streaming and batch data. It also covers their skills in core concepts of data analytics. This requires an understanding of data visualization, basic chart types, analytics techniques, data processing concepts, as well as ETL & ELT processing.

  • Explain how Relational Data Works on Azure: 25-30%

    The test takers will be required to have the ability to describe some concepts in this topic, including relational data workloads and relational Azure data services. They will also need to know how to identify the basic management functions for relational data. Additionally, they need to possess a good understanding of how to use the SQL language to describe the query methods for data.

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Topics of Microsoft DP-900: Microsoft Azure Data Fundamentals Exam

  • Describe an analytics workload on Azure (25-30%)
  • Describe how to work with relational data on Azure (25-30%)
  • Describe core data concepts (15-20%)
  • Describe how to work with non-relational data on Azure (25-30%)

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Microsoft DP-900 Exam Syllabus Topics:

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

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