Learn DP-900 Microsoft Azure Data Fundamentals Training Course Online
Microsoft Azure Data Fundamentals is a course designed to help learners become proficient in working with and managing data in the Azure cloud platform. The course provides a thorough exploration of how data management works in the Azure environment, and how users can create and utilize resources in Azure to optimize their data management operations. Through the course, learners will gain an understanding of how to effectively manage and analyze data using Azure, as well as best practices for using data resources in Azure. Topics covered in the course include working with Azure data services, creating and managing data resources, and analyzing data using Power BI and Azure services. This course is recommended for professionals who are looking to increase their knowledge and skills with data management in the cloud, and is a great fit for both IT professionals and data professionals.
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Interview Preparation
The DP-900 Microsoft Azure Data Fundamentals Training does not have any prerequisites. It is designed for anyone who is interested in learning about the cloud services provided by Microsoft Azure involving data and analytics.
Learning Objectives of DP-900: Microsoft Azure Data Fundamentals
1. Explain data engineering and data science fundamentals.
2. Identify core components of Azure data services.
3. Describe common data services, roles, and terminology used by data engineers and data scientists.
4. Explain relational data stores including Azure SQL Database and Azure Synapse Analytics.
5. Describe data modeling concepts and Microsoft Power BI.
6. Utilize Azure Data Lake Storage to store and manage data.
7. Manage and transform data using Azure Databricks, HDInsight, and Azure Data Factory.
8. Utilize analytics service offerings within the Microsoft Azure platform.
9. Access data from non-relational data stores.
10. Utilize the Azure Cosmos DB database service for storage and data retrieval.
11. Learn best practices for data security, privacy, and compliance.
12. Explain the various options for streaming data processing.
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