Exam DP-900: Microsoft Azure Data Fundamentals

微软MCF认证考试考什么？考试内容？

微软MCF认证考试是分科目的，现在我们要看得就是微软给出的MCF考试代码：DP-900 考试名称：Microsoft Azure Data Fundamentals的大纲，如果你通过了这科考试之后，将会获得Microsoft Azure Data Fundamentals的MCF证书

注意：由于云技术在不断发展，本大纲包含了DP-900微软MCF认证考试中进行衡量的技能。但是，MCF考试并能不确保包括这些技能的最新发展被包含在内，如想了解最新发展，请参阅相关技能的技术文档。

注意：每个技能下方列出的内容，说明我们将如何评估该技能。但是由于技术不断更新，此列表不能确保是确定的或详尽的。

注意：大多数问题都只涉及已经正式发布的功能。考试可能包含在预览阶段功能的问题（如果这些预览的功能是常用的）。

Describe core data concepts (15-20%)

  1. 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
  2. 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%)

  1. Describe relational data workloads
    • identify the right data offering for a relational workload
    • describe relational data structures (e.g., tables, index, views)
  2. 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
  3. 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.)
  4. 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%)

  1. 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
  2. Describe non-relational data offerings on Azure
    • identify Azure data services for non-relational workloads
    • describe Azure Cosmos DB APIs
    • describe Azure Table storage
    • describe Azure Blob storage
    • describe Azure File storage
  3. 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%)

  1. 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
  2. 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
    • describe modern data warehousing architecture and workload
  3. 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)
  4. 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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