Microsoft Azure Data Fundamentals : DP-900 exam

How to operate with relational data on Azure

Here, the candidate should be equipped with knowledge and skills in the following sub domains:

• Relational Azure data services – this section of the exam is focused on the following: the understanding of IaaS, SaaS and PaaS solutions and their comparison, Azure SQL DB services such as Azure SQL managed instance and Azure virtual machine, Azure Synapse Analytics, and Azure Database for MariaDB, PostgreSQL and for MySQL.
• Relational data workloads – the candidate has to gain skills in identifying the appropriate data offerings when it comes to relational workload and learn more about structures of relational data.
• Identification of basic management roles for relational data – under this sub domain there are several skills covered that include the deployment and provisioning of relational data, various deployment methods such as Azure Resource Manager templates, Azure portal, Azure command-line interface (CLI), etc. Also, one should be knowledgeable in data components identification (authentication and firewall), the identification of basic connectivity problems such as accessing Azure VNets, firewalls, access from the internet and authentication). One more knowledge area involved is query tools (SQL Management Studio, Azure Data Studio, utility, sqlcmd etc.).
• Data querying techniques that require utilizing SQL language – it deals with the Data Manipulation Language (DML) and Data Definition Language (DDL) as well as querying relational data on Azure SQL DB.

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

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The Microsoft DP-900 exam is intended for candidates who already have foundational knowledge and skills in performing basic tasks and using Microsoft Azure data services. It is also recommended for those who are starting their careers in the field of cloud data. Although there are no strict prerequisites, every individual who desires to take DP-900 test should have familiarity with non-relational and relational data as well as various types of data workloads such as analytical and transactional.

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