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Explain the Techniques Involved in Working with Relational Data on Microsoft Azure: 25-30%

  • Explain the Query Methods for Data with the SQL Language: The learners should be capable of comparing the Data Manipulation Language with the Data Definition Language. They should also be able to query relational data within Azure Database for MySQL, Azure SQL Database, and Azure Database for PostgreSQL.
  • Identify the Fundamental Management Tasks for Relational Data: The applicants should be able to explain the deployment and provisioning of relational data services. They also need to understand the techniques for deployment, such as Azure Resource Manager templates, Azure portal, Azure CLI, and Azure PowerShell. In addition, the students should be able to identify the fundamental connectivity issues and components of data security.
  • Explain the Concepts of Relational Data Workloads: The examinees should be able to explain the structures of relational data and identify the appropriate data for relational workloads.
  • Explain Relational Azure Data Services: The individuals should have the ability to explain and compare SaaS, PaaS, and IaaS solutions as well as Azure Synapse Analytics. They also need the knowledge of Azure SQL database services, Azure Database for PostgreSQL, Azure Database for MySQL, and Azure Database for MariaDB.

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

Microsoft DP-900 Korean Exam Syllabus Topics:

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

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