PART - I : Implement data storage solutions (40-45%)
Implement non-relational data stores
- implement a solution that uses Cosmos DB, Data Lake Storage Gen2, or Blob storage
- implement data distribution and partitions
- implement a consistency model in CosmosDB
- provision a non-relational data store
- provide access to data to meet security requirements
- implement for high availability, disaster recovery, and global distribution
Implement relational data stores
- configure elastic pools
- configure geo-replication
- provide access to data to meet security requirements
- implement for high availability, disaster recovery, and global distribution
- implement data distribution and partitions for SQL Data Warehouse
- Implement PolyBase
Manage data security
- implement data masking
- encrypt data at rest and in motion
PART - II: Manage and develop data processing (25-30%)
Develop batch processing solutions
- develop batch processing solutions by using Data Factory and Azure Databricks
- ingest data by using PolyBase
- implement the integration runtime for Data Factory
- create linked services and datasets
- create pipelines and activities
- create and schedule triggers
- implement Azure Databricks clusters, notebooks, jobs, and autoscaling
- ingest data into Azure Databricks
Develop streaming solutions
- configure input and output
- select the appropriate windowing functions
- implement event processing using Stream Analytics
PART - III: Monitor and optimize data solutions (30-35%)
Monitor data storage
- monitor relational and non-relational data sources
- implement BLOB storage monitoring
- implement Data Lake Store monitoring
- implement SQL Database monitoring
- implement SQL Data Warehouse monitoring
- implement Cosmos DB monitoring
- configure Azure Monitor alerts
- implement auditing by using Azure Log Analytics
Monitor data processing
- design and implement Data Factory monitoring
- monitor Azure Databricks
- monitor HDInsight processing
- monitor stream analytics
Optimize Azure data solutions
- troubleshoot data partitioning bottlenecks
- optimize Data Lake Storage
- optimize Stream Analytics
- optimize SQL Data Warehouse
- optimize SQL Database
- manage data life cycle