DP-900 exam is a certification exam for Microsoft Azure Data Fundamentals. This exam measures the candidates' understanding of fundamental concepts of data, including data storage, data processing, data management, and data security. The DP-900 certification is intended for individuals who are interested in beginning a career in data-related technologies, or who are looking to build a foundation of knowledge in data-related concepts.
The passing score for the Microsoft DP-900 exam is 700 on a scale of 100-1000. The exact passing score may vary as Microsoft periodically updates the exam objectives and content. It's best to check the latest information on the Microsoft website or consult with a Microsoft training partner to get the most up-to-date information.
Prerequisites
The prerequisites of the DP-900 exam are not specified by Microsoft. However, it is recommended to have a basic understanding of cloud computing and its services, as well as experience with deploying and managing data in Azure. Familiarity with Azure data storage options, Azure data security, and Azure data integration and migration is also helpful for the DP-900 exam
Skills measured
- Understand cloud concepts: Understanding the basic concepts of cloud computing and its benefits, deployment models and the basics of Microsoft Azure services and solutions.
- Understand core Azure services and solutions: Understanding the key Azure services and solutions including virtual machines, web apps, storage solutions and databases.
- Understand security, privacy, compliance, and trust: Understanding the security, privacy, compliance, and trust concepts and how they are applied to Microsoft Azure services and solutions.
- Understand Azure pricing and support: Understanding Azure pricing and support services, and how to monitor and manage Azure resources.
Functional groups
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 (BI))
- 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 solutions
- describe Azure SQL family of products including Azure SQL Database, Azure SQL
- Managed Instance, and SQL Server on Azure Virtual Machines
Describe Azure Synapse Analytics
- describe Azure Database for PostgreSQL, Azure Database for MariaDB, and Azure Database for MySQL
- Identify basic management tasks for relational data
- describe provisioning and deployment of relational data services
- describe method for deployment including the Azure portal, Azure Resource Manager templates, Azure PowerShell, and the Azure command-line interface (CLI)
- identify data security components (e.g., firewall, authentication)
- identify basic connectivity issues (e.g., accessing from on-premises, access from 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 Data Definition Language (DDL) versus Data Manipulation Language (DML) query relational data in Azure SQL Database, Azure Database for PostgreSQL, and Azure Database for MySQL
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 data
- recommend the correct data store
- determine when to use non-relational data
Identify basic management tasks for non-relational data
- describe provisioning and deployment of non-relational data services
- describe method for deployment including the Azure portal, Azure Resource Manager templates, Azure PowerShell, and the Azure command-line interface (CLI)
- identify data security components (e.g., firewall, authentication, encryption)
- identify basic connectivity issues (e.g., accessing from on-premises, access from
- 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 Storage Gen2, Azure Synapse Analytics, Azure Databricks, and Azure HDInsight escribe 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., Azure HDInsight, 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
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