DP-203: Azure data engineering, historical path

Objectives and progression

Independent historical path based on DP-203 objectives dated October 24,2024, with six lessons,40 questions,and six cases. The exam and certification retired March 31,2025. Original examples connect storage, batch and stream processing, authorization, monitoring, and optimization to APS and technical management work. Technical documentation was inspected in October2026; later updates are distinguished from the archived syllabus. The internal assessment reuses30 decisions in60 minutes without awarding external certification. This is not automatically equivalent preparation for another exam.

Audience: Azure data engineers, APS teams, L3 support, and data project managers.

Prerequisites: Azure fundamentals, SQL, file operations, and basic Python. Fictional cases require no real cloud resources.

300 estimated study minutes

• Choose data layout from query patterns and actual distribution.
• Define the replay unit and when a load is actually complete.
• Separate consumer progress, lateness handling, and effect uniqueness.
• Validate execution identity and each authorization and network boundary.
• Relate technical states to freshness, completeness, and expected results.
• Improve performance without losing correctness or recovery capability.

Modules

1. Storage, distribution, and exploration
2. Incremental loads and recovery
3. Streams, time, and external effects
4. Lake access and secret management
5. Monitoring and data-product acceptance
6. Optimization, cost, and retention

Continue learning

Data / AI

• SQL
• Python
• Production Support L3
• Databricks Certified Data Engineer Associate

References and version

DP-203 objectives 2024-10-24; retired 2025-03-31

• Azure Data Engineer retirement · 2026-10-01
• DP-203 archived objectives October 24 2024 · 2026-10-01
• Incremental loading and change tracking · 2026-10-01
• Integration runtime network and execution boundaries · 2026-10-01
• Copy fault tolerance and skipped records · 2026-10-01
• Stateful tumbling window triggers · 2026-10-01
• Serverless SQL storage layout and query efficiency · 2026-10-01
• File and folder metadata in serverless SQL · 2026-10-01
• Dedicated SQL distribution and skew · 2026-10-01
• Data Lake RBAC ABAC and ACL evaluation · 2026-10-01
• Structured Streaming foreachBatch delivery · 2026-10-01
• Structured Streaming watermarks and state · 2026-10-01
• Delta merge ambiguity and runtime differences · 2026-10-01
• Event Hubs partitions consumer groups and checkpoints · 2026-10-01
• Data Lake traversal and default ACLs · 2026-10-01
• Data Factory run monitoring · 2026-10-01
• Pipeline execution and trigger types · 2026-10-01
• Dedicated SQL statistics maintenance · 2026-10-01
• Delta data file retention and vacuum · 2026-10-01
• Data Factory lineage integration limitations · 2026-10-01
• Stream Analytics time handling · 2026-10-01
• Conditional paths and pipeline outcome · 2026-10-01
• Copy activity metrics and bottlenecks · 2026-10-01
• Storage private endpoints and DNS · 2026-10-01
• Data Factory Key Vault credential references · 2026-10-01
• Delta compaction and data layout · 2026-10-01
• Synapse Spark performance diagnosis · 2026-10-01
• Stream Analytics processing and delivery guarantees · 2026-10-01
• Mapping data flow schema drift · 2026-10-01