DP-900 Exam Study Guide (Microsoft Azure Data Fundamentals)

DP-900 Microsoft Azure Data Fundamentals Study Guide

Preparing for the DP-900 Microsoft Azure Data Fundamentals Certificate exam? Don’t know where to start? This post is the DP-900 Certificate Study Guide (with links to each exam objective).

I have curated a list of articles from Microsoft documentation for each objective of the DP-900 exam. I hope this article will help you to prepare for the DP-900 Certification exam. Also, please share the post within your circles so it helps them to prepare for the exam.

DP-900 Azure Data Fundamentals Course

Pluralsight (Free trial)Azure data-related topics on Pluralsight
LinkedIn Learning (Free trial)Azure Data Topics on LinkedIn
UdemyAzure Data Engineer Technologies for Beginners

DP-900 Azure Data Fundamentals Other Stuff

To view other Azure Certificate Study Guides, click here

Full Disclosure: Some of the links in this post are affiliate links. I receive a commission when you purchase through them.

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Using DP-900 exam dumps can get you permanently banned from taking any future Microsoft certificate exam. Read the FAQ page for more information. However, I strongly suggest you validate your understanding with practice questions.

Links for exam objectives to be updated soon

Describe core data concepts (15-20%)

Describe types of core data workloads

Describe batch data
Describe streaming data
Describe the difference between batch and streaming data
Describe the characteristics of relational data

Describe data analytics core concepts

Describe data visualization (e.g., visualization, reporting, business intelligence)
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 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 delivery models
Describe Azure SQL Database
Describe Azure Synapse Analytics
Describe SQL Server on Azure Virtual Machine
Describe Azure Database for PostgreSQL, Azure Database for MariaDB, and Azure
Database for MySQL
Describe Azure SQL Managed Instance

Identify basic management tasks for relational data

Describe provisioning and deployment of relational data services
Describe method for deployment including ARM templates and Azure Portal
Identify data security components (e.g., firewall, authentication)
Identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls)
Identify query tools (e.g., Azure Data Studio, SQL Server Management Studio, sqlcmd utility, etc.)

Describe query techniques for data using SQL language

Compare DDL versus DML
Query relational data in PostgreSQL, MySQL, and Azure SQL Database

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

Describe non-relational data workloads

Describe the characteristics of non-relational data
Describe the types of non-relational and NoSQL data
Recommend the correct data store
Determine when to use non-relational data

Describe non-relational data offerings on Azure

Identify Azure data services for non-relational workloads
Describe Azure Cosmos DB APIs
Describe Azure Table storage
Describe Azure Blob storage
Describe Azure File storage

Identify basic management tasks for non-relational data

Describe provisioning and deployment of non-relational data services
Describe method for deployment including ARM templates and Azure Portal
Identify data security components (e.g., firewall, authentication)
Identify basic connectivity issues (e.g., accessing from on-premises, access with Azure
VNets, access from Internet, authentication, firewalls)
Identify management tools for non-relational data

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,
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., HDI, Azure Databricks, Azure Synapse Analytics, Azure Data Factory)

Describe data visualization in Microsoft Power BI

Describe the role of paginated reporting
Describe the role of interactive reports
Describe the role of dashboards
Describe the workflow in Power BI

This brings us to the end of DP-900 Study Guide

What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!

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