Practice Exams | Microsoft Azure DP-900 Data Fundamentals

Be prepared for the Microsoft Azure Data Fundamentals DP-900 Exam (Data Processing and Cloud Architecture)

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.

The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.

Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.

The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B"  last time you went through the test.

NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.

Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.

This exam is an opportunity to demonstrate your knowledge of core data concepts and related Microsoft Azure data services. As a candidate for this exam, you should have familiarity with Exam DP-900’s self-paced or instructor-led learning material.

This exam is intended for you, if you’re a candidate beginning to work with data in the cloud.

You should be familiar with:

• The concepts of relational and non-relational data.

• Different types of data workloads such as transactional or analytical.

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

Skills at a glance

• Describe core data concepts (25–30%)

• Identify considerations for relational data on Azure (20–25%)

• Describe considerations for working with non-relational data on Azure (15–20%)

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

Describe core data concepts (25–30%)

Describe ways to represent data

• Describe features of structured data

• Describe features of semi-structured

• Describe features of unstructured data

Identify options for data storage

• Describe common formats for data files

• Describe types of databases

Describe common data workloads

• Describe features of transactional workloads

• Describe features of analytical workloads

Identify roles and responsibilities for data workloads

• Describe responsibilities for database administrators

• Describe responsibilities for data engineers

• Describe responsibilities for data analysts

Identify considerations for relational data on Azure (20–25%)

Describe relational concepts

• Identify features of relational data

• Describe normalization and why it is used

• Identify common structured query language (SQL) statements

• Identify common database objects

Describe relational Azure data services

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

• Identify Azure database services for open-source database systems

Describe considerations for working with non-relational data on Azure (15–20%)

Describe capabilities of Azure storage

• Describe Azure Blob storage

• Describe Azure File storage

• Describe Azure Table storage

Describe capabilities and features of Azure Cosmos DB

• Identify use cases for Azure Cosmos DB

• Describe Azure Cosmos DB APIs

Describe an analytics workload (25–30%)

Describe common elements of large-scale analytics

• Describe considerations for data ingestion and processing

• Describe options for analytical data stores

• Describe Microsoft cloud services for large-scale analytics, including Azure Databricks and Microsoft Fabric

Describe consideration for real-time data analytics

• Describe the difference between batch and streaming data

• Identify Microsoft cloud services for real-time analytics

Describe data visualization in Microsoft Power BI

• Identify capabilities of Power BI

• Describe features of data models in Power BI

• Identify appropriate visualizations for data