Candidates for this exam should have foundational knowledge of core data concepts and how they are implemented using Microsoft Azure data services.

This exam is intended for candidates beginning to work with data in the cloud.

Candidates should be familiar with the concepts of relational and non-relational data, and different types of data workloads such as transactional or analytical.

Azure Data Fundamentals can be used to prepare for other Azure role-based certifications like Azure Database Administrator Associate or Azure Data Engineer Associate, but it’s not a prerequisite for any of them.

Prove that you can describe the following: core data concepts; how to work with relational data on Azure; how to work with non-relational data on Azure; and an analytics workload on Azure.

Skills measured

• Describe core data concepts (15-20%)

  • describe batch data

  • describe streaming data

  • describe the difference between batch and streaming data

  • describe the characteristics of relational data

• Describe how to work with relational data on Azure (25-30%)

  • describe data visualization (e.g., visualization, reporting, business intelligence (BI))

  • describe basic chart types such as bar charts and pie charts

  • describe analytics techniques (e.g., descriptive, diagnostic, predictive, prescriptive, cognitive)

  • describe ELT and ETL processing

  • describe the concepts of data processing

• Describe how to work with non-relational data on Azure (25-30%)

  • Describe relational data workloads

    • identify the right data offering for a relational workload

    • describe relational data structures (e.g., tables, index, views)

  • Describe relational Azure data services

    • describe and compare PaaS, IaaS, and SaaS solutions

    • describe Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines

    • describe Azure Synapse Analytics

    • describe Azure Database for PostgreSQL, Azure Database for MariaDB, and Azure Database for MySQL

• Describe an analytics workload on Azure (25-30%)

  • Describe analytics workloads

    • describe transactional workloads

    • describe the difference between a transactional and an analytics workload

    • describe the difference between batch and real time

    • describe data warehousing workloads

    • determine when a data warehouse solution is needed

  • Describe the components of a modern data warehouse

    • describe Azure data services for modern data warehousing such as Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Databricks, and Azure HDInsight

    • describe modern data warehousing architecture and workload

  • Describe data ingestion and processing on Azure

    • describe common practices for data loading

    • describe the components of Azure Data Factory (e.g., pipeline, activities, etc.)

    • describe data processing options (e.g., Azure HDInsight, Azure Databricks, Azure Synapse Analytics, Azure Data Factory)

Good Luck!