1. Design and implement data storage (40-45%)

• Design a data storage structure
  • Recommend file types and storage mechanisms for various scenarios
  • Optimize data storage for performance and cost
  • Design and implement a distribution strategy for Azure Synapse Analytics
• Design and develop data partitioning schemes
  • Design partition strategies for databases or data warehouses
  • Implement effective data partitioning and sharding
• Design and implement the integration of a data lake
  • Design data lake security using Azure storage
  • Integrate Azure Data Lake Storage with Azure Synapse Analytics
• Design for data security and compliance
  • Manage encryption and secure data access

2. Design and develop data processing (25-30%)

• Ingest and transform data
  • Design and implement data ingestion with Azure Data Factory or Azure Synapse Pipelines
  • Prepare and transform data using Spark in Azure Synapse Analytics
  • Implement mapping data flows
• Design and implement stream processing solutions
  • Use Azure Stream Analytics and Azure Databricks for stream data processing
  • Handle late-arriving and out-of-order data

3. Design and implement data security (10-15%)

• Implement data masking and encryption
  • Apply data masking techniques to data storage
  • Implement data encryption for data at rest and in transit
• Secure data using access control
  • Configure role-based access control (RBAC)
  • Manage Azure Key Vault for secrets and data encryption

4. Monitor and optimize data storage and data processing (10-15%)

• Monitor data storage
  • Implement monitoring using Azure Monitor
  • Optimize storage performance
• Monitor data processing
  • Configure monitoring for data processing solutions
  • Optimize data pipelines for performance and cost