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Explain Analytics Workloads on Azure: 25-30%

• 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.
• 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 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 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.

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

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The DP-900: Microsoft Azure Data Fundamentals exam is designed for the individuals pursuing the Microsoft Certified: Azure Data Fundamentals certification. This test evaluates their foundational knowledge of core concepts of data and how these are implemented with the use of Microsoft Azure data services. The associated certificate is an ideal choice for those professionals who want to start a career in the IT field with the main focus on working with data in Cloud. The successful candidates with this certification can continue their journey by earning more advanced options such as Microsoft Certified: Azure Data Engineer Associate or Microsoft Certified: Azure Database Administrator Associate. However, it is important to mention that this exam is not a requirement for any of these certificates.

This is a foundational certification exam, so it does not require any prerequisites. However, the potential candidates should have a good knowledge of the concepts of non-relational and relational data as well as different categories of data workloads, including analytical and transactional.