Retired exam
DP-203 was retired on March 31, 2025
Microsoft retired DP-203; the exam URL redirects to the Azure Data Engineer Associate certification page, which prints 'Retirement Date 03/31/2025' and 'This certification and the renewal assessment are retired.' No successor is named.
No direct replacement is named in the reviewed official sources.
Historical DP-203 Scope
Data Engineering on Microsoft Azure, known as DP-203, was a Microsoft certification exam covering data storage, processing, and security domains. This exam is now retired. Because Microsoft has not named a successor, candidates should check official Microsoft certification resources for the paths available for data engineers. This page serves as a reference for the final published objectives and structure of the retired exam, which consisted of 40 questions in total for all candidates.
Who DP-203 Was For
This exam was intended for data engineers who manage, monitor, and secure data solutions on Azure. Candidates needed experience with data processing, storage implementation, and optimization within the Azure ecosystem to perform the tasks covered in the exam objectives.
Skills You Should Be Ready to Demonstrate
- Implementing data partition strategies
- Designing data exploration layers
- Ingesting and transforming data
- Performing exploratory data analysis
- Securing and monitoring data storage
- Optimizing data processing workflows
How to Reuse Your Preparation
Since DP-203 is retired and has no successor, do not prepare for this specific exam. Focus your learning on current Azure data engineering technologies and services. Review the official Microsoft documentation and training modules to build skills in data integration, storage, and processing. Consult the Microsoft certification website to identify active credentials that align with your professional goals in the Azure data engineering field. Always verify the latest requirements through official channels to ensure your study efforts remain relevant to current industry standards.
Historical Domain Guide
Design and implement data storage: Historical Scope
This domain covers the creation and management of data storage solutions within the Azure environment. It focuses on how to organize data effectively and build layers for exploration, ensuring that storage architectures meet the specific requirements of data engineering projects.
- Implement a partition strategy
- Design and implement the data exploration layer
Develop data processing: Historical Scope
This domain addresses the movement and refinement of data through various pipelines. It involves technical tasks related to ingestion, cleansing, and analyzing data sets to ensure high quality and reliability for downstream consumption, which is required for successful data engineering workflows in the cloud.
- Ingest and transform data
- Cleanse data
- Perform data exploratory analysis
Secure, monitor, and optimize data storage and data processing: Historical Scope
This domain focuses on the maintenance and protection of data environments. It covers security protocols, monitoring performance, and troubleshooting storage and processing systems to ensure that data solutions remain secure, efficient, and highly available throughout their entire lifecycle within the Azure platform.
- Implement data security
- Monitor data storage and data processing
- Optimize and troubleshoot data storage and data processing
Frequently asked questions
How many questions were on the exam?
The exam consisted of 40 questions in total. Candidates were expected to navigate various question types, including multiple choice and case studies, within the time frame provided by the testing center during their scheduled appointment for the certification assessment.
What was the passing score?
The passing score for the exam was 700 on a scale of 1-1000. This score represented the minimum level of proficiency required to demonstrate competency in data engineering tasks on the Azure platform before the exam was officially retired by Microsoft.
How long was the exam?
The exam had a duration of 100 minutes. During this period, candidates were required to complete all sections and question types to be evaluated against the established performance standards for data engineers working within the Microsoft Azure cloud ecosystem.
What was the cost of the exam?
The exam price was 165 dollars. This fee covered the registration and administration of the certification test at authorized testing centers. Since the exam is now retired, this price is no longer applicable for any current or future certification attempts.
Sources and Verification
Verified 2026-08-30
How this page was made
This page was built by synthesizing verified facts and official Microsoft documentation regarding the retired DP-203 exam to provide a clear reference for its final objectives and structure for all interested candidates.
Historical DP-203 Domains
Design and implement data storage Design and implement data storage
18%
- Implement a partition strategyImplement 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 layerDesign and implement the data exploration layer
Create and execute queries by using a compute solution that leverages SQL serverless and Spark clusters; 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 Develop data processing
42%
- Ingest and transform dataIngest and transform data
Design and implement incremental data loads; Transform data by using Apache Spark; Transform data by using Transact-SQL (T-SQL) in Azure Synapse Analytics; Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory; Transform data by using Azure Stream Analytics
- Cleanse dataCleanse data
Handle duplicate data; Avoiding duplicate data by using Azure Stream Analytics Exactly Once Delivery; 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 analysisPerform data exploratory analysis
- Develop a batch processing solutionDevelop a batch processing solution
Develop batch processing solutions by using Azure Data Lake Storage Gen2, 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 pipelinesCreate data pipelines
Scale resources; Configure the batch size; Create tests for data pipelines; Integrate Jupyter or Python notebooks into a data pipeline; Upsert batch 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 solutionDevelop 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 stream data; Replay archived stream data; Read from and write to a delta lake
- Manage batches and pipelinesManage 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 Secure, monitor, and optimize data storage and data processing
35%
- Implement data securityImplement 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 processingMonitor 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 processingOptimize 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