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Microsoft Certified:

Azure Data Engineer Associate

Azure Data Engineers design and implement the management, monitoring, security, and privacy of data using the full stack of Azure data services to satisfy business needs.

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Required exams

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Exam DP-200: Implementing an Azure Data Solution

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Exam DP-201: Designing an Azure Data Solution

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Skills and knowledge

Candidates who earn an Azure Data Engineer certification are verified by Microsoft to have the following skills and knowledge.

Implement Azure cloud data warehouses
  • design Data Lake architecture
  • design the data schema
  • provision the data warehouse
Implement No-SQL Databases
  • manage data distribution and partitions
  • select the database platform
  • model data storage based on use cases
  • select storage types
  • provision storage accounts
  • provision Data Lake storage
  • integrate WebHDFS applications with Data Lake Storage
  • provision in CosmosDB
Implement Azure SQL Database
  • provision Azure SQL Database
  • configure elastic pools
  • configure data backup strategies
  • configure elastic jobs
  • provision Azure SQL database managed instance
  • configure connections
  • manage data synchronization
Implement hybrid data scenarios
  • design hybrid solution
  • design data replication and synchronization
Manage Azure DevOps Pipelines
  • use a build service
  • deploy using Azure Resource Manager templates
Implement big data environments
  • implement Hadoop clusters
  • implement Databricks environment
Develop batch processing solutions
  • develop batch processing solutions using Spark
  • develop batch processing solutions using Azure Databricks
Develop streaming solutions
  • implement event processing using Azure stream analytics
  • query data using Azure Stream Analytics
  • configure Azure Stream Analytics to read from Event Hub
  • configure Azure Stream Analytics to read from BLOB storage
Develop integration solutions
  • create data pipeline systems in Azure
  • develop data integration pipelines with Azure Data Factory
  • develop data integration pipelines with Azure Databricks
Implement data migration
  • transform data
  • bulk load data with PolyBase
Automate Data Factory Pipelines
  • deploy data factory pipelines
  • configure Data Factory
Manage source data access security
  • connect to sources
  • create connection objects
  • install Gateways
Configure authentication and authorization
  • set up firewall rules
  • integrate Azure AD
  • design Cosmos DB security
  • design Data Lake security
  • design Azure SQL DB security
  • manage Access Control
  • manage permissions on resources
Manage and enforce data policies and standards
  • mask data
  • encrypt data at rest
  • encrypt data in motion
  • encrypt data elements
  • configure audit
Set up notifications
  • set up alerts on security threats
  • set up alerts on unexpected resource usage
Monitor data storage
  • implement BLOB storage monitoring
  • implement Data Lake Store monitoring
  • implement HDInsight monitoring
Monitor databases for a specified scenario
  • implement SQL Database monitoring
  • implement SQL Data Warehouse monitoring
  • implement Cosmos DB monitoring
Monitor data processing
  • design and implement Data Factory monitoring
  • monitor Azure Databricks
  • monitor HDInsight processing
  • monitor stream analytics
Manage Optimization
  • troubleshoot data partitioning bottlenecks
  • optimize HIVE processing
  • optimize Data Lake
  • optimize SPARK processing
  • optimize Azure Stream Analytics
  • optimize Data Warehouse
  • optimize SQL DB
  • manage data life cycle
Manage business continuity
  • implement a disaster recovery strategy
  • design for High Availability
  • import and export data
  • design a data retention policy
Design an Azure Data solution
  • choose the correct data storage solution to meet the technical and business requirements
  • calculate the storage capacity of data
  • recommend tier based on data requirements
  • decide partition distribution type
Design Azure Cloud data warehouses
  • design the data schema
  • design for scale
Design No-SQL Databases
  • design data distribution and partitions
  • design a CosmosDB solution
  • select the database platform
  • model data storage based on use cases
  • select storage types
Design Azure SQL Database
  • design for scale
  • design data load technologies
  • design solution architecture
Design hybrid data scenarios
  • design hybrid solution
  • design data replication and synchronization
Design batch processing solutions
  • design batch processing solutions using Spark
  • design batch processing solutions using Azure Databricks
design big data real-time processing solutions
  • Design for real-time processing
  • design for LAMBDA architecture
  • design and provision compute resources
Design integration solutions
  • design cloud analytic solutions
  • design data processing activities
  • design data migration
Design source data access security
  • design network security
  • choose the appropriate authentication mechanism
  • choose the appropriate authorization mechanism
Design security for data policies and standards
  • design for data encryption
  • design for data auditing
  • design for data privacy
Design a data retention policy
  • design a backup and recovery policy
  • evaluate business requirements for data retention
Design for Optimization
  • design for optimized HIVE processing
  • design for optimized Data Lake
  • design for optimized SPARK processing
  • design for optimized Azure Stream Analytics
  • design for optimized Data Warehouse
  • design for optimized SQL DB
  • plan for the data life cycle
Design and implement a disaster recovery strategy
  • design a backup and recovery policy
  • design an online disaster recovery strategy
  • design for multi-region availability
  • plan for backup and restore
Design for High Availability
  • design for multi-region availability
  • design for read scale
  • design for multi-master writes