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Workload analytics on Azure

  • Data visualization in Microsoft Power BI – this topic requires candidates to learn more about paginated reporting, dashboards, interactive reports, as well as workflow in Power BI.
  • Elements of a modern data warehouse – this sub-section is focused on Azure data services related to modern data warehousing. In addition, applicants should be familiar with modern data warehousing workload and architecture.
  • Analytics workloads – under this topic, candidates should learn more about transactional workloads as well as data warehousing workloads. Also, they have to get skilled in differentiating between an analytics and a transactional workload, know the difference between real and batch time, and be able to determining when a data warehouse solution is required.
  • Date processing and ingestion on Azure – main topics within this subdomain include data loading common practices, Azure Data Factory components (activities, pipeline, etc.), and data processing.

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Preparation Guide for Microsoft DP-900: Microsoft Azure Data Fundamentals Exam

Introduction

If you're looking to enter the IT world but don't have the education required, you may want to consider taking one of the many certification exams offered by respected tech companies. Microsoft offers a range of certifications, including those for developers. The DP-900 exam is one such exam. This article will help you prepare for this exam so that it will be successful and lead to your certification. Microsoft DP-900 exam dumps are one of the most popular resources for DP-900 exam preparation.

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

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Microsoft DP-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 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. Capabilities and features
                      • 2. Consistency levels and partitioning
                        • 3. APIs and data models
                          - Describe Azure storage services
                          • 1. Queue Storage
                            • 2. File Storage
                              • 3. Blob Storage
                                • 4. Table Storage
                                  Topic 3: Describe core data concepts25–30%- Describe types of data
                                  • 1. File formats
                                    • 2. Structured, semi-structured, and unstructured data
                                      - 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 4: Describe an analytics workload on Azure25–30%- Describe data visualization
                                              • 1. Power BI capabilities
                                                • 2. Data models and reports
                                                  - Describe real-time analytics
                                                  • 1. Azure Stream Analytics
                                                    • 2. Stream processing concepts
                                                      - Describe Azure analytics services
                                                      • 1. Azure Synapse Analytics
                                                        • 2. Microsoft Fabric
                                                          • 3. Azure HDInsight
                                                            • 4. Azure Databricks
                                                              • 5. Azure Data Lake Storage Gen2
                                                                - Describe big data and analytics concepts
                                                                • 1. Data lakes and data warehouses
                                                                  • 2. Data ingestion and processing
                                                                    • 3. Modern data warehouse architecture

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