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About Microsoft Azure Data Fundamentals (DP-900) Exam!

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

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 is not a prerequisite for any of them.

DP-900 Exam Details 

• Exam Code: DP-900
• Exam Name: “Microsoft Azure Data Fundamentals”
• Exam Cost: USD 99
• Passing score: 700/1000 or 70%
• Language: English.
• Number of Questions: 40-60
• Exam Time Duration: 120 min.
• Type of Questions: Multiple Choice Questions and Multiple Response Questions

How to Become a Certified DP-900 Professional? 

Candidates may access the Microsoft Azure DP-900 learning path, which takes them step-by-step through the Modules. But these courses feature modules that assist applicants to improve their abilities and expertise. The following are some of the modules:

• The fundamentals of data management
• The non-relational data in Azure.
• data that is linked to Azure
• Azure

Microsoft Azure Data Fundamentals Course Outline

Creating and managing sales entities and configuring additional tools and services are among the tasks included in the Exam DP-900. As a consequence, students should read the course overview once to familiarize themselves with the objectives.

Module 1: Describe core data concepts (15-20%)

1.1 Describing the many kinds of labor that go into maintaining core data

• Defining a set of data
• Demonstrating how streaming data may be explained
• The distinction between batch and real-time information
• A description of the properties of relational information

1.2 Defining the Foundational Concepts of Data Analytics

• Explain the way data is represented graphically
• Delineating common chart shapes, such as bars and circles, on a single graph
• Explain the methods used to analyze the data
• A description of the elt and ETL methods of operation
• Explains the fundamentals of data manipulation.

Module 2: “Describe how to work with relational data on Azure (25-30%)”

2.1 Explain the burdens associated with relational data.

• For a relational load, finding the proper data solution
• Explains how to build relational databases

2.2 Explain the relational Azure data services that are available.

• “Azure Database for PostgreSQL, Azure Database for Maria DB, and Azure Database for MySQL” are all described in this section.
• Comparisons between paas, saas, and iaas delivery methods were made
• Delivers the outline of the Azure SQL product family, which includes the databases, the administered instance, and the virtual machine.
• Providing a brief introduction to Azure Synapse Analytics

2.3 Prioritizing and identifying data relationship management activities is an important first step.

• Relational data services procurement and installation are described.
• How to use arm templates and the azure portal to implement a deployment.
• Recognizing the elements of data security (e.g., firewall, authentication).
• Recognize the fundamental difficulties of access from on-premises, azure vnets, internet, authentication, and firewall concerning the connection.
• Knowing how to use the right search engine.

2.4 Explain SQL querying methods for data.

• Contrasted to DDL and DML.
• “PostgreSQL, MySQL, and Azure SQL Database” querying.

Module 3: “Explain how to work with non-relational data on Azure (25-30%)”

3.1 Provide an example of a non-relational task.

• To characterize the non-relational data’s features.
• Utilize non-relational data where necessary.

3.2 Discuss Azure’s options for non-relational data.

• A brief explanation of Azure Table storage.
• Explain the Azure Blob storage system.
• Providing a brief introduction to Azure File storage.

3.3 Recognize the most fundamental non-relational data management activities.

• ARM templates, Azure Portal, Azure PowerShell, and the Azure command-line interface are all covered in this article (CLI).
• Components of data security must be identified and configured (e.g., firewall, authentication, encryption)
• Finding the core source of any connectivity issues, such as on-premises access, Azure VNets, the Internet and authentication, firewalls, and other security measures.
• Find data management solutions for non-relational datasets. 

Module 4: “Define an analytics workload on Azure (25-30%)”

4.1 Provide examples of analytical tasks.

• Transaction-oriented workloads may be described.
• The financial workload differs from the analytical workload.
• Emphasizes the contrast between batch and real-time scheduling.
• Data warehouse workloads are described in this manner.
• To decide whether or not a data warehouse is necessary.

4.2 A contemporary database server consists of the following:

• Services such as Azure Data Lake, Storage Gen2, and Synapse Analytics for contemporary data warehousing are available on the Azure platform.
• Discussing the design and workload of contemporary data warehouses.

4.3 On Azure, outline how data is ingested and processed.

• Describing the components of Azure Data Factory (e.g., pipelines, activities, etc.).
• For example, HDI, Azure Data bricks, Synapse Analytics in the cloud, and the Azure Data Factory in the cloud.

4.4 Give an example of Microsoft Power BI’s data visualization.

• Explain browse able reporting’s function.
• Provide a brief explanation of how interactive reports work
• Defining dashboards’ purpose.
• I’ll walk you through the steps in Power BI.

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