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Capability Development / Data Engineering

Microsoft Azure Data Fundamentals (DP-900)

This DP-900 certification course is designed to help candidates pass the Microsoft DP-900 certification exam and is ideal for candidates beginning to work with data in the cloud. Candidates learn core data concepts and related Microsoft Azure data services, including relational and non-relational storage, analytics workloads and data visualization with Power BI. The Azure Data Fundamentals certification can also be used to prepare for other Azure role-based certifications such as Azure Data Engineer Associate or Azure Database Administrator Associate.

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DP-900Course Code
24 HoursCourse Duration
BeginnerLevel
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Program Overview

This DP-900 certification course is designed to help candidates pass the Microsoft DP-900 certification exam and is ideal for candidates beginning to work with data in the cloud. Candidates learn core data concepts and related Microsoft Azure data services, including relational and non-relational storage, analytics workloads and data visualization with Power BI. The Azure Data Fundamentals certification can also be used to prepare for other Azure role-based certifications such as Azure Data Engineer Associate or Azure Database Administrator Associate.

Audience Profile

Beginners starting a career in cloud data roles; IT professionals exploring Azure data services; Candidates preparing for the DP-900 certification exam; Aspiring Azure Data Engineers or Database Administrators; and Business and technical stakeholders needing core data literacy.

Course Syllabus

Describe Core Data Concepts

Topics

  • Describe Ways to Represent Data: structured data (tables and rows), semi-structured data (JSON and XML), unstructured data (files and media)
  • Identify Options for Data Storage: file-based storage (CSV, JSON, XML), relational databases, NoSQL databases
  • Describe Common Data Workloads: transactional workloads (daily business operations), analytical workloads (reports and insights)
  • Identify Roles and Responsibilities for Data Workloads: database administrators (security and performance), data engineers (pipelines and platforms), data analysts (reports and dashboards)

Identify Considerations for Relational Data on Azure

Topics

  • Describe Relational Concepts: tables, keys and relationships, normalization, data consistency, SQL queries, views and indexes
  • Describe Relational Azure Data Services: Azure SQL Database (managed relational database), SQL Managed Instance (SQL Server compatibility), SQL Server on Azure VMs (lift-and-shift option), open-source databases (MySQL, PostgreSQL, MariaDB)

Describe Considerations for Working with Non-Relational Data on Azure

Topics

  • Describe Capabilities of Azure Storage: Blob storage (objects and media), File storage (shared access), Table storage (key-value data)
  • Describe Capabilities and Features of Azure Cosmos DB: global distribution (low-latency access), multiple APIs (flexible data models), partitioning (scalable throughput)

Describe Analytics Workloads on Azure

Topics

  • Describe Common Elements of Large-Scale Analytics: data ingestion, data preparation, data lakes, data warehouses, Azure Synapse Analytics, Databricks and HDInsight, Data Factory pipelines
  • Describe Considerations for Real-Time Data Analytics: batch processing (scheduled workloads), streaming events (real-time insights), Azure Stream Analytics, Spark Structured Streaming
  • Describe Data Visualization in Microsoft Power BI: Power BI dashboards (interactive reports), data models (measures and calculations), charts and maps (KPIs and insights)