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via Udemy |
Go to Course: https://www.udemy.com/course/ms_dp_900/
The course titled "Microsoft Azure Data (DP-900) Exam Questions Apr - 2025" on Coursera is an excellent resource designed for individuals aiming to enhance their understanding of core data concepts and prepare for the DP-900 certification exam. Although the syllabus is not explicitly detailed, the course content comprehensively covers key areas vital for data professionals working with Azure. **Course Review:** This course provides a thorough overview of essential data concepts, including structured, semi-structured, and unstructured data, as well as data representation and storage options. It offers clear insights into relational versus non-relational data, highlighting considerations for working with each type on Azure. The inclusion of modules on data workloads, such as transactional and analytical, helps learners understand the diverse applications of data services in Azure. One of the notable strengths is its focus on Azure-specific data services, including Azure SQL Database, Azure Cosmos DB, and storage solutions like Blob and File storage. The course also emphasizes the analytical workload aspect, detailing data ingestion, processing, and visualization tools such as Azure Databricks, Microsoft Fabric, and Power BI. For professionals interested in real-time data analytics, this course provides essential knowledge on streaming data and real-time insights. **Who Should Enroll:** This course is ideal for data engineers, database administrators, data analysts, and IT professionals seeking to validate their skills with the DP-900 exam or deepen their understanding of Azure data services. **Recommendation:** Given its comprehensive coverage of foundational data concepts, specific Azure data services, and analytics workloads, I highly recommend this course for aspiring cloud data professionals. It effectively balances theoretical knowledge with practical insights, making it a valuable preparatory resource for the DP-900 exam. Enrolling in this course will not only prepare you for certification but also equip you with practical skills for managing and analyzing data on Azure, advancing your career in cloud data solutions. --- If you need further details on specific modules or how to approach your studies for this course, feel free to ask!
Skills at a glanceDescribe core data concepts (25-30%)Identify considerations for relational data on Azure (20-25%)Describe considerations for working with non-relational data on Azure (15-20%)Describe an analytics workload on Azure (25-30%)Describe core data concepts (25-30%)Describe ways to represent dataDescribe features of structured dataDescribe features of semi-structuredDescribe features of unstructured dataIdentify options for data storageDescribe common formats for data filesDescribe types of databasesDescribe common data workloadsDescribe features of transactional workloadsDescribe features of analytical workloadsIdentify roles and responsibilities for data workloadsDescribe responsibilities for database administratorsDescribe responsibilities for data engineersDescribe responsibilities for data analystsIdentify considerations for relational data on Azure (20-25%)Describe relational conceptsIdentify features of relational dataDescribe normalization and why it is usedIdentify common structured query language (SQL) statementsIdentify common database objectsDescribe relational Azure data servicesDescribe the Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual MachinesIdentify Azure database services for open-source database systemsDescribe considerations for working with non-relational data on Azure (15-20%)Describe capabilities of Azure storageDescribe Azure Blob storageDescribe Azure File storageDescribe Azure Table storageDescribe capabilities and features of Azure Cosmos DBIdentify use cases for Azure Cosmos DBDescribe Azure Cosmos DB APIsDescribe an analytics workload (25-30%)Describe common elements of large-scale analyticsDescribe considerations for data ingestion and processingDescribe options for analytical data storesDescribe Microsoft cloud services for large-scale analytics, including Azure Databricks and Microsoft FabricDescribe consideration for real-time data analyticsDescribe the difference between batch and streaming dataIdentify Microsoft cloud services for real-time analyticsDescribe data visualization in Microsoft Power BIIdentify capabilities of Power BIDescribe features of data models in Power BIIdentify appropriate visualizations for data