Get instant Coupon on our Telegram ChannelBe prepared for the Microsoft Azure Data Fundamentals DP-900 Exam (Data Processing and Cloud Architecture)
Description
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 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 on Azure (25–30%)
Describe common elements of large-scale analytics
Describe considerations for data ingestion and processing
Describe options for analytical data stores
Describe Azure services for data warehousing, including Azure Synapse Analytics, Azure Databricks, Microsoft Fabric, Azure HDInsight, and Azure Data Factory
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
| Total Students | 396 |
|---|---|
| Original Price($) | |
| Sale Price | Free |
| Number of lectures | 0 |
| Number of quizzes | 6 |
| Total Reviews | 55 |
| Global Rating | 4.6 |
| Instructor Name | Wade Henderson |
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