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Explain Analytics Workloads on Azure: 25-30%

  • Explain Modern Data Warehouse Components: The test takers should understand Azure data services for the latest data warehousing, including Azure Synapse Analytics, Azure HDInsight, Azure Data Lake, and Azure Databricks, as well as explainin the workload and architecture for modern data warehousing.
  • Explain Data Visualization within Microsoft Power BI: You need to have competence in explaining the function of dashboards, interactive reports, and paginated reporting, as well as the workflow within Power BI.
  • Explain Analytics Workloads: The applicants should be able to explain transactional workloads, data warehousing workloads, and when data warehouse solutions are needed. This subtopic also covers their skills and knowledge associated with the differences between analytics and a transactional workload as well as the differences between real-time and batch.
  • Explain Data Processing & Ingestion on Azure: The learners should be able to explain the fundamental practices for data loading, Azure Data Factory components, and data processing options.

Helpful Training Course for Acing DP-900

To help students get fully ready for DP-900, Microsoft has created an official 1-day teacher-guided program for students named ‘Course DP-900T00-A: Microsoft Azure Data Fundamentals’. There are 4 modules in this course which will address all the topics needed for the Microsoft DP-900. However, such a class does not have any related lab exercises, meaning students will not get the scope for applying the things they have learnt in theory. In general, this training is perfect for those who want to learn the main concepts and skills needed to provide cloud data services with the help of Microsoft Azure.

There is no doubt that in the future information society, knowledge and skills will be a major driver for economic growth and one of the major contributors to the sustainable development of the information industry. And getting the related Microsoft Azure Data Fundamentals certification in your field will be the most powerful way for you to show your professional knowledge and skills. However, it is not easy for the majority of candidates to prepare for the exam in order to pass it, if you are one of the candidates who are worrying about the exam now, congratulations, there is a panacea for you--our DP-900 study tool. We can assure you that you can pass the exam as well as getting the related certification in a breeze with the guidance of our Microsoft Azure Data Fundamentals test torrent, now I would like to introduce some details about our DP-900 guide torrent for you.

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What job roles can you pursue after passing the Microsoft DP-900 exam?

Microsoft Certified: Azure Data Fundamentals is an entry-level certificate. However, the candidates who pass the prerequisite exam and obtain this certification can pursue a variety of rewarding job roles. These include an IT Administrator, a Desktop Engineer, a Windows Administrator, an Azure Security Engineer, an Azure Solutions Architect, an Azure Application Architect, a Technical Help Desk, and an IT Service Desk Analyst, among others. The salary outlook for these positions is an average of $56,000 per annum.

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

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Microsoft DP-900 Exam Syllabus Topics:

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

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