2022/08/04
(25–30%)
(20–25%)
(15–20%)
(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
描述表示數據的方法
描述結構化數據的特徵
描述半結構化的特徵
描述非結構化數據的特徵
確定數據存儲選項
描述數據文件的常用格式
描述數據庫的類型
描述常見的數據工作負載
描述事務性工作負載的特徵
描述分析工作負載的特徵
確定數據工作負載的角色和職責
描述數據庫管理員的職責
描述數據工程師的職責
描述數據分析師的職責
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
描述關係概念
識別關係數據的特徵
描述規範化以及為什麼使用它
識別常見的結構化查詢語言 (SQL) 語句
識別常見的數據庫對象
描述關係 Azure 數據服務
描述 Azure SQL 系列產品，包括 Azure SQL 數據庫、Azure SQL
Azure 虛擬機上的託管實例和 SQL Server
識別開源數據庫系統的 Azure 數據庫服務
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
描述 Azure 存儲的功能
描述 Azure Blob 存儲
描述 Azure 文件存儲
描述 Azure 表存儲
描述 Azure Cosmos DB 的功能和特性
確定 Azure Cosmos DB 的用例
描述 Azure Cosmos DB API
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, Azure HDInsight, and Azure Data Factory
Describe consideration for real-time data analytics
Describe the difference between batch and streaming data
Describe technologies for real-time analytics including Azure Stream Analytics, Azure Synapse Data Explorer, and Spark structured streaming
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
描述大規模分析的常見元素
描述數據攝取和處理的注意事項
描述分析數據存儲的選項
描述用於數據倉庫的 Azure 服務，包括 Azure Synapse Analytics、Azure Databricks、Azure HDInsight 和 Azure Data Factory
描述對實時數據分析的考慮
描述批處理數據和流數據之間的區別
描述實時分析技術，包括 Azure 流分析、Azure Synapse 數據資源管理器和 Spark 結構化流
描述 Microsoft Power BI 中的數據可視化
確定 Power BI 的功能
描述 Power BI 中數據模型的功能
確定適當的數據可視化