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  • Exam Code: DP-900 Korean
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900 Korean Version)
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  • Exam Code: DP-900 Korean
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Topics of Microsoft DP-900: Microsoft Azure Data Fundamentals Exam

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

What Are the Exam Topics?

Core data concepts

This topic majorly addresses the following skills:

  • Various data workloads – this involves an overview of the features of relational data as well as the description, similarities and differences between streaming data and batch data as well as.
  • Main concepts of data analytics – data visualization, business intelligence (BI), analytics techniques (diagnostic, predictive, descriptive, cognitive, prescriptive) and basic chart types such as pie charts and bar charts are the main concepts addressed in this category. Also, data processing techniques are covered along with ETL processing.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-900

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Microsoft DP-900 Korean Exam Syllabus Topics:

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

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