DP-203 Azure Data Engineering Guide

Data Engineering on Microsoft Azure (DP-203)

This certification (DP-203) is designed for candidates who want to demonstrate expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems into a suitable schema for building analytics solutions.

Azure data engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and frameworks to store and produce cleansed and enhanced datasets for analysis. This data store can be designed with different architecture patterns based on business requirements, including modern data warehouse (MDW), big data, or lakehouse architecture.

Skills measured

• Design and implement data storage (15–20%)
• Develop data processing (40–45%)
• Secure, monitor, and optimize data storage and data processing (30–35%)

Design and implement data storage (15–20%)

• Implement a partition strategy
  • Implement a partition strategy for files
  • Implement a partition strategy for analytical workloads
  • Implement a partition strategy for streaming workloads
  • Implement a partition strategy for Azure Synapse Analytics
  • Identify when partitioning is needed in Azure Data Lake Storage Gen2
• Design and implement the data exploration layer
  • Create and execute queries by using a compute solution that leverages SQL serverless and Spark cluster
  • Recommend and implement Azure Synapse Analytics database templates
  • Push new or updated data lineage to Microsoft Purview
  • Browse and search metadata in Microsoft Purview Data Catalog

Develop data processing (40–45%)

• Ingest and transform data
  • Design and implement incremental loads
  • Transform data by using Apache Spark
  • Transform data by using Transact-SQL (T-SQL)
  • Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory
  • Transform data by using Azure Stream Analytics
  • Cleanse data
  • Handle duplicate data
  • Handle missing data
  • Handle late-arriving data
  • Split data
  • Shred JSON
  • Encode and decode data
  • Configure error handling for a transformation
  • Normalize and denormalize data
  • Perform data exploratory analysis
• Develop a batch processing solution
  • Develop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory
  • Use PolyBase to load data to a SQL pool
  • Implement Azure Synapse Link and query the replicated data
  • Create data pipelines
  • Scale resources
  • Configure the batch size
  • Create tests for data pipelines
  • Integrate Jupyter or Python notebooks into a data pipeline
  • Upsert data
  • Revert data to a previous state
  • Configure exception handling
  • Configure batch retention
  • Read from and write to a delta lake
• Develop a stream processing solution
  • Create a stream processing solution by using Stream Analytics and Azure Event Hubs
  • Process data by using Spark structured streaming
  • Create windowed aggregates
  • Handle schema drift
  • Process time series data
  • Process data across partitions
  • Process within one partition
  • Configure checkpoints and watermarking during processing
  • Scale resources
  • Create tests for data pipelines
  • Optimize pipelines for analytical or transactional purposes
  • Handle interruptions
  • Configure exception handling
  • Upsert data
  • Replay archived stream data
• Manage batches and pipelines
  • Trigger batches
  • Handle failed batch loads
  • Validate batch loads
  • Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines
  • Schedule data pipelines in Data Factory or Azure Synapse Pipelines
  • Implement version control for pipeline artifacts
  • Manage Spark jobs in a pipeline

Secure, monitor, and optimize data storage and data processing (30–35%)

• Implement data security
  • Implement data masking
  • Encrypt data at rest and in motion
  • Implement row-level and column-level security
  • Implement Azure role-based access control (RBAC)
  • Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2
  • Implement a data retention policy
  • Implement secure endpoints (private and public)
  • Implement resource tokens in Azure Databricks
  • Load a DataFrame with sensitive information
  • Write encrypted data to tables or Parquet files
  • Manage sensitive information
• Monitor data storage and data processing
  • Implement logging used by Azure Monitor
  • Configure monitoring services
  • Monitor stream processing
  • Measure performance of data movement
  • Monitor and update statistics about data across a system
  • Monitor data pipeline performance
  • Measure query performance
  • Schedule and monitor pipeline tests
  • Interpret Azure Monitor metrics and logs
  • Implement a pipeline alert strategy
• Optimize and troubleshoot data storage and data processing
  • Compact small files
  • Handle skew in data
  • Handle data spill
  • Optimize resource management
  • Tune queries by using indexers
  • Tune queries by using cache
  • Troubleshoot a failed Spark job
  • Troubleshoot a failed pipeline run, including activities executed in external services