DP-203 study guide
DP-203 · Work through your exam, one topic at a time.
The Microsoft Certified: Azure Data Engineer Associate is an intermediate-level, Azure-focused credential for data engineers. Microsoft states that this certification and its renewal assessment are retired, with a retirement date of 03/31/2025, so candidates should confirm the current status before planning a study schedule. The credential targets professionals who integrate, transform, and consolidate structured, unstructured, and streaming data into schemas suitable for analytics. Day-to-day work includes helping stakeholders explore data, building secure and compliant pipelines, and producing cleansed and enhanced datasets using modern data warehouse, big data, or Lakehouse patterns. Topics on this exam: Design and implement data storage, Develop data processing, and Secure, monitor, and optimize data storage and data processing. The DP-203 study guide has 3 sections and about 823 words.
01Design and Implement Data Storage
Azure storage for analytics centers on Azure Data Lake Storage Gen2 (ADLS Gen2) , which layers a hierarchical namespace over Blob Storage. Understand the difference between flat blob namespaces and true directories: ADLS Gen2 enables atomic directory renames and efficient partition elimination. Know how to choose between Parquet , Delta Lake , Avro , and ORC formats, and when to use partitioning versus bucketing . PolyBase and Synapse external tables require specific file layouts and statistics.
287 words, about 1 min02Develop Data Processing
Data processing in DP-203 spans Azure Data Factory (ADF) , Synapse Pipelines , Databricks , and Stream Analytics . You must know how to design copy activities , data flows , and mapping data flows with transformations. Understand tumbling window vs hopping window triggers, and event-based triggers. For code-based processing, know PySpark DataFrame operations, Delta Lake ACID transactions, and merge/upsert patterns.
268 words, about 1 min03Secure, Monitor, and Optimize Data Storage and Data Processing
Security in Azure analytics involves Azure Active Directory (AAD) authentication, role-based access control (RBAC) , ACLs on ADLS Gen2, managed identities , and private endpoints . Understand column-level security , row-level security , and dynamic data masking in Synapse. For monitoring, know Azure Monitor , Log Analytics , Synapse Studio monitoring , and Databricks cluster metrics . Optimization includes file sizing (target 256 MB–1 GB per file), compaction , statistics , caching , and compute scaling .
268 words, about 1 min