DP-900 is the name of the exam. If you pass this exam, you will be certified as Microsoft Certified: Azure Data Fundamentals. Some certifications require passing multiple exams, while others, such as Azure Data Fundamentals this time, are certified with just one exam. Twice

The outline of the exam is shown on the official page below, but it is a test that requires knowledge of the concepts of relational and non-relational data, as well as various types of data workloads such as transactions and analytics. As we say relational and non-relational, we are also asked about relational databases such as SQL Server and MySQL / PostgreSQL, as well as Document type Cosmos DB, NoSQL Table Storage, and other Graphs, as well as an overview of databases provided by Azure. You will be asked to use them properly. Also the concept of OLTP / OLAP.

Exam DP-900: Microsoft Azure Data Fundamentals

Explain the concept of core data (15-20%)

Describe the types of core data workloads

∙ Explain batch data

∙ Explain streaming data

∙ Explain the difference between batch data and streaming data

∙ Explain the characteristics of relational data

Explain the core concept of data analysis

∙ Describe data visualization (eg visualizations, reports, business intelligence (BI))

∙ Explain basic chart types such as bar charts and pie charts

∙ Explain analytical methods (eg, descriptive, diagnostic, predictive, normative, cognitive)

∙ Explain ELT and ETL processing

∙ Explain the concept of data processing

How to work with relational data on Azure (25-30%)

Relational data workload

∙ Identify the right data offering for relational workloads

∙ (For example, table is index, view) Describe relational data structure

Azure data services relational

∙ Comparison of PaaS, IaaS, and SaaS solutions

∙ About Azure SQL database services such as Azure SQL database, Azure SQL managed instance, on Azure virtual machine

∙ SQL Server ∙ Azure Synapse Analytics

∙ PostgreSQL, MariaDB, MySQL

Identify basic administrative tasks for relational data

∙ Provisioning and deployment of relational data services

∙ Explain how to deploy Azure portal etc.

∙ Azure resource manager template, Azure PowerShell, and Azure command line interface (CLI)

∙ Specific data security components (eg firewall, authentication)

∙ Identify basic connectivity issues (eg on-premises access, AC Azure VNet access, internet access, authentication, firewall)

∙ Identify query tools (Azure Data Studio, SQL Server Management Studio, sqlcmd utility, etc.)

Explanation of data query method using SQL language

∙ Comparison of data definition language (DDL)) and data manipulation language (DML)

∙ Query relational data with Azure SQL database, Azure database for PostgreSQL, Azure database for MySQL

Explain how to work with non-relational data (25-30%)

Explain non-relational data workloads

∙ Describes the characteristics of non-relational data

∙ Explain the types of NoSQL data with non-relational

∙ Correct data store

∙ Scenes using non-relational data

Non-relational data offering on Azure

∙ Identifying non-relational workloads

∙ Cosomos DB API

∙ Table storage

∙ BLOB storage

∙ File Storage

Identify basic administrative tasks for non-relational data

∙ Provisioning and deploying non-relational data services

∙ How to deploy Azure portal, Azure Resource Manager template, Azure PowerShell, Azure command line interface (CLI), etc.

∙ Data security components (identify firewalls, authentication, encryption, etc.)

∙ Identify basic connectivity issues (eg on-premises access, Azure VNet access, internet access, authentication, firewall)

∙ Identify non-relational data management tools

Describe your Azure analytics workload (25-30%)

Description of analytic workload

∙ Describe transaction workload

∙ Explain and analyze differences between transactions workload ∙ Describe the difference between batch and real time

∙ Describe the workload as warehouse data

∙ Determine when a data warehousing solution is needed

Components of Modern Dataware Houser

∙ Azure Data Lake, Azure Synapse Analytics, AzureDatabric, HDInsight

∙ DWH architecture and workload

Data ingestion and processing

∙ Common practice for reading data

∙ Describe the components of Azure Data Factory (eg pipelines, activities, etc.)

∙ Data processing options (eg HDInsight, Databricks, Synapse Analytics, Data Factory)

Explain data visualization in Power BI

∙ Role of page break reporting

∙ The role of interactive reports

∙ The role of the dashboard

∙ Workflow