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Azure Data Engineer (DP-203)

Microsoft

Complete guide to passing the Azure Data Engineer (DP-203) exam on your first attempt.

HardHigh Search Volume
Key Information at a Glance
Cost

$165

Pass Rate

~50%

Validity

1 year (renewal required)

Region

Global

Provider

Microsoft

Salary Impact

$110k-$160k

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Complete Overview

Exam DP-203: Data Engineering on Microsoft Azure was the single exam behind the Microsoft Certified: Azure Data Engineer Associate credential, and it targeted engineers who integrate, transform and consolidate data from structured, unstructured and streaming sources into schemas that support analytics. Microsoft retired DP-203 and the certification on March 31, 2025 at 11:59 PM Central Standard Time, so the exam can no longer be booked. Microsoft published no global fee for it; its exam pages state only that the price is based on the country or region in which the exam is proctored.

The blueprint was built around six Azure services that Microsoft named directly in the candidate profile: Azure Data Factory, Azure Synapse Analytics, Azure Stream Analytics, Azure Event Hubs, Azure Data Lake Storage and Azure Databricks. Microsoft also required solid knowledge of three data processing languages, SQL, Python and Scala, plus an understanding of parallel processing and data architecture patterns including the modern data warehouse, big data and lakehouse designs.

The final skills-measured version took effect on October 24, 2024 and used only three functional groups. Design and implement data storage was 15 to 20 percent, develop data processing was 40 to 45 percent, and secure, monitor and optimize data storage and data processing was 30 to 35 percent. Microsoft publishes these as ranges rather than fixed figures, and it does not publish a separate weight for the nine sub-objectives underneath them. The shape of the exam followed from those numbers: roughly three quarters of the questions concerned building pipelines and then running them well, and only a fifth concerned storage design.

Scoring used the standard Microsoft technical exam rules. Scores were reported on a scale of 1 to 1,000 with 700 or greater required to pass, and Microsoft states that because the score is scaled it may not equal 70 percent of the available points. There is no penalty for guessing, and every form contains unscored pilot items that Microsoft does not identify.

Anyone who passed before the retirement date keeps the certification on their Microsoft Learn transcript. The renewal assessment retired alongside the exam, so the credential cannot be renewed and lapses at the end of its one-year validity. The live Microsoft credential for this role is now Fabric Data Engineer Associate, earned through exam DP-700. Its published skill areas are implementing and managing an analytics solution, ingesting and transforming data, and monitoring and optimizing an analytics solution, all inside Microsoft Fabric; Microsoft asks candidates for skill in SQL, PySpark and Kusto Query Language, allows 100 minutes, and offers the exam in English, Japanese, Chinese (Simplified), German, French, Spanish and Portuguese (Brazil). The DP-203 material still transfers well: partitioning, incremental loads, watermarking, schema drift and Spark job troubleshooting all behave the same way in Fabric as they did in Synapse.

Why Get Azure Data Engineer (DP-203) Certified?

DP-203 was the only exam required for the Azure Data Engineer Associate certification, so one pass at 700 out of 1,000 earned the whole credential rather than a component of it.

The blueprint names six Azure services and three languages explicitly, which gives a data engineer a tightly bounded reading list instead of the whole Azure catalogue.

Two of the three functional groups, worth a combined 70 to 80 percent, are about building and operating pipelines rather than designing storage, so the study time maps onto what data engineers actually do every week.

Anyone who passed before March 31, 2025 keeps a permanent transcript entry on Microsoft Learn even though the certification itself is retired.

Microsoft Partners who earned the credential before retirement continue to earn points or credit toward Partner Program requirements for one year after the retirement date.

The DP-203 topics on incremental loads, watermarking, schema drift, data skew, data spill and small-file compaction reappear under the ingest, transform, monitor and optimize skill areas of DP-700, the live Fabric Data Engineer Associate exam.

Roles that list this credential sit in the $110k-$160k band used across this site for Azure data engineering positions.

Exam Format & Structure

Duration

100 minutes. The Azure Data Engineer Associate certification page still carries the line "You will have 100 minutes to complete this assessment" alongside its retirement warning. Microsoft's general table allocates associate role-based exams without labs 100 minutes of exam time against 120 minutes of seat time, so the appointment ran about 20 minutes longer than the clock to cover instructions, the Candidate Agreement and post-exam comments.

Questions

Microsoft does not publish a question count for individual exams. Its official statement is that most Microsoft certification exams typically contain between 40 and 60 questions and that the number varies by exam and changes when the exam is updated.

Passing Score

700 or greater on a scale of 1 to 1,000. Microsoft states this is a scaled score that may not equal 70 percent of available points, and that the bar reflects the difficulty of the question set you received.

Question Types

  • Multiple choice
  • Multiple response
  • Hot area
  • Drag and drop
  • Build list, used heavily on DP-203 for ordering pipeline and transformation steps
  • Active screen
  • Case studies describing a full data platform with several linked questions
  • Problem-and-solution question sets, where the same scenario repeats with different proposed solutions
  • Interactive labs in a live Azure environment on forms that include them

Delivery Method

Proctored through Pearson VUE, either at a physical test centre or online with the OnVUE secure browser. Online candidates complete a self check-in, upload a headshot, a government-issued ID photo and room-scan photos taken with a mobile phone, then wait in a proctor queue. Check-in opens 30 minutes before the appointment and closes 15 minutes after it.

Exam Domains & Topics

Design and implement data storage
15-20%

The smallest functional group, split into partitioning and the data exploration layer. Partitioning questions span files, analytical workloads, streaming workloads and dedicated SQL pools in Synapse, and ask when partitioning helps rather than how to click it. The exploration half covers serverless SQL and Spark queries plus data cataloguing and lineage in Microsoft Purview.

Key Topics to Master:

  • Implementing a partition strategy for files in Azure Data Lake Storage Gen2
  • Implementing a partition strategy for analytical workloads and for streaming workloads
  • Implementing a partition strategy for Azure Synapse Analytics, including distribution choice on dedicated SQL pools
  • Identifying when partitioning is needed in Data Lake Storage Gen2 and when it hurts
  • Creating and running queries with serverless SQL pools and Spark clusters
  • Recommending and implementing Azure Synapse Analytics database templates
  • Pushing new or updated data lineage to Microsoft Purview
  • Browsing and searching metadata in the Microsoft Purview Data Catalog
Ingest and transform data
Not published. Microsoft weighted only the parent group, Develop data processing, at 40-45%

The single densest objective on the exam, with sixteen listed tasks. It covers moving data in and reshaping it, using Spark, T-SQL in Synapse, Data Factory or Synapse Pipelines, and Stream Analytics. Most of the tasks concern data quality problems: duplicates, missing values, late arrivals, schema shredding and error handling during a transformation.

Key Topics to Master:

  • Designing and implementing incremental data loads with watermarks
  • Transforming data with Apache Spark and with Transact-SQL in Azure Synapse Analytics
  • Ingesting and transforming data using Azure Synapse Pipelines or Azure Data Factory mapping data flows
  • Transforming data using Azure Stream Analytics queries
  • Cleansing data, handling duplicate data and using Stream Analytics Exactly Once Delivery to avoid duplicates
  • Handling missing data and late-arriving data
  • Splitting data, shredding JSON, and encoding and decoding data
  • Configuring error handling for a transformation and normalising or denormalising data
Develop a batch processing solution
Not published. Microsoft weighted only the parent group, Develop data processing, at 40-45%

Batch pipelines built across Data Lake Storage Gen2, Databricks, Synapse Analytics and Data Factory. Beyond building the pipeline, the objective covers operational concerns: scaling compute, sizing batches, testing pipelines, handling exceptions, reverting to a previous state and setting batch retention. Delta Lake reads and writes appear here and again in the streaming objective.

Key Topics to Master:

  • Developing batch solutions with Data Lake Storage Gen2, Azure Databricks, Synapse Analytics and Data Factory
  • Using PolyBase to load data into a dedicated SQL pool
  • Implementing Azure Synapse Link and querying the replicated data
  • Creating data pipelines, scaling resources and configuring batch size
  • Creating tests for data pipelines and integrating Jupyter or Python notebooks into a pipeline
  • Upserting batch data and reverting data to a previous state
  • Configuring exception handling and batch retention
  • Reading from and writing to a delta lake
Develop a stream processing solution
Not published. Microsoft weighted only the parent group, Develop data processing, at 40-45%

Streaming with Stream Analytics and Event Hubs, plus Spark structured streaming. This objective carries the exam's hardest conceptual content: windowing functions, watermarking, checkpointing, schema drift and processing across or within partitions. Several tasks mirror the batch objective, which is deliberate, because the exam asks you to apply the same operational thinking to an unbounded stream.

Key Topics to Master:

  • Creating a stream processing solution with Azure Stream Analytics and Azure Event Hubs
  • Processing data with Spark structured streaming
  • Creating windowed aggregates using tumbling, hopping, sliding, session and snapshot windows
  • Handling schema drift and processing time series data
  • Processing data across partitions and within a single partition
  • Configuring checkpoints and watermarking during processing
  • Handling interruptions, configuring exception handling and upserting stream data
  • Replaying archived stream data and reading from and writing to a delta lake
Manage batches and pipelines
Not published. Microsoft weighted only the parent group, Develop data processing, at 40-45%

The operations objective inside the data processing group. It covers triggering, scheduling, validating and repairing pipeline runs in Data Factory or Synapse Pipelines, plus source control for pipeline artefacts and management of Spark jobs that sit inside a pipeline. Questions often present a failed run and ask what the correct recovery action is.

Key Topics to Master:

  • Triggering batches with schedule, tumbling window, storage event and custom event triggers
  • Handling failed batch loads and validating batch loads
  • Managing data pipelines in Azure Data Factory or Azure Synapse Pipelines
  • Scheduling data pipelines in Data Factory or Synapse Pipelines
  • Implementing version control for pipeline artefacts using Git integration
  • Managing Spark jobs inside a pipeline
  • Rerunning failed activities from the point of failure rather than from the start
Implement data security
Not published. Microsoft weighted only the parent group, Secure, monitor, and optimize data storage and data processing, at 30-35%

Eleven listed tasks spanning masking, encryption at rest and in motion, row-level and column-level security, Azure role-based access control and POSIX-like access control lists on Data Lake Storage Gen2. Databricks-specific items appear here too, including resource tokens and loading a DataFrame that carries sensitive information.

Key Topics to Master:

  • Implementing dynamic data masking and encrypting data at rest and in motion
  • Implementing row-level security and column-level security
  • Implementing Azure role-based access control and POSIX-like access control lists for Data Lake Storage Gen2
  • Understanding how RBAC and ACLs interact when both grant or deny access to a path
  • Implementing a data retention policy and secure endpoints, both private and public
  • Implementing resource tokens in Azure Databricks
  • Loading a DataFrame with sensitive information and writing encrypted data to tables or Parquet files
  • Managing sensitive information and classification
Monitor data storage and data processing
Not published. Microsoft weighted only the parent group, Secure, monitor, and optimize data storage and data processing, at 30-35%

Observability across the platform, built mainly on Azure Monitor. The objective covers configuring logging and monitoring services, measuring the performance of data movement and of queries, keeping statistics current, scheduling pipeline tests, interpreting Azure Monitor metrics and logs, and implementing an alert strategy for pipelines and streams.

Key Topics to Master:

  • Implementing logging used by Azure Monitor and configuring monitoring services
  • Monitoring stream processing, including watermark delay and backlogged input events
  • Measuring the performance of data movement and measuring query performance
  • Monitoring and updating statistics about data across a system
  • Monitoring data pipeline performance in Data Factory or Synapse Pipelines
  • Scheduling and monitoring pipeline tests
  • Interpreting Azure Monitor metrics and logs
  • Implementing a pipeline alert strategy with action groups
Optimize and troubleshoot data storage and data processing
Not published. Microsoft weighted only the parent group, Secure, monitor, and optimize data storage and data processing, at 30-35%

The diagnostic objective. It names specific distributed-systems failures rather than describing them: compact small files, handle skew in data, handle data spill, troubleshoot a failed Spark job, and troubleshoot a failed pipeline run including activities executed in external services. Two further tasks cover tuning queries by using indexers and by using cache. Each has a recognised remedy the exam expects you to know.

Key Topics to Master:

  • Compacting small files to reduce metadata and task overhead
  • Handling skew in data, including redistributing on a better key
  • Handling data spill when a query exceeds available memory and writes to disk
  • Optimising resource management across Spark pools and SQL pools
  • Tuning queries by using indexes, including clustered columnstore and nonclustered indexes
  • Tuning queries by using result set caching and materialised views
  • Troubleshooting a failed Spark job from the Spark UI and driver logs
  • Troubleshooting a failed pipeline run, including activities executed in external services

Recommended Study Plan

Week 1: Blueprint, environment and the three published weights
8-10 hours
  • 1Read the Microsoft Learn study guide for DP-203 and write the three functional groups with their percentage ranges at the top of your notes
  • 2Create an Azure free account and provision an Azure Data Lake Storage Gen2 account with hierarchical namespace enabled
  • 3Create a Synapse Analytics workspace and confirm you can run a query on the built-in serverless SQL pool
  • 4Run the Microsoft Learn exam sandbox so build list, hot area and case study formats are familiar before exam day
  • 5Refresh SQL window functions and Python basics, since the candidate profile names SQL, Python and Scala
Week 2: Data Lake Storage Gen2 and partitioning
10-12 hours
  • 1Load a partitioned Parquet dataset into Data Lake Storage Gen2 using a year, month and day folder layout
  • 2Query the same dataset with and without partition pruning from a serverless SQL pool and record the data-scanned difference
  • 3Compare Parquet, Delta, Avro, CSV and JSON on compression, schema evolution and predicate pushdown
  • 4Set POSIX-like ACLs on a folder tree and test what a user with RBAC but no ACLs can and cannot read
  • 5Write down the cases where partitioning makes things worse, such as many tiny partitions
Week 3: Azure Synapse Analytics dedicated and serverless SQL pools
10-12 hours
  • 1Create a dedicated SQL pool and load a fact table using hash distribution, then reload it using round robin and compare query plans
  • 2Build a small star schema and replicate the dimension tables
  • 3Load data with PolyBase and with the COPY statement and compare the two
  • 4Implement Azure Synapse Link against a Cosmos DB container and query the replicated analytical store
  • 5Recommend and apply a Synapse database template, then browse the resulting metadata in Microsoft Purview
Week 4: Azure Data Factory and Synapse Pipelines
12-14 hours
  • 1Build a copy pipeline from Blob Storage to Data Lake Storage Gen2 using linked services, datasets and an integration runtime
  • 2Implement an incremental load using a watermark table and a lookup activity
  • 3Add a mapping data flow that cleanses data, removes duplicates and handles missing values
  • 4Configure schedule, tumbling window and storage event triggers, and observe how a tumbling window trigger backfills
  • 5Connect the workspace to a Git repository and publish pipeline artefacts through source control
Week 5: Apache Spark and Azure Databricks
12-14 hours
  • 1Run PySpark transformations in a Synapse Spark pool and again in an Azure Databricks notebook
  • 2Write a Delta Lake table, run MERGE for an upsert, then use time travel to revert the table to a previous version
  • 3Deliberately create a skewed join, observe the straggler task in the Spark UI, then fix it with salting or a broadcast join
  • 4Trigger a data spill by under-provisioning executor memory and read the spill metrics
  • 5Integrate a Databricks notebook into a Data Factory pipeline and pass parameters into it
Week 6: Streaming with Event Hubs and Stream Analytics
12-14 hours
  • 1Send events to Event Hubs with a generator script and consume them from a Stream Analytics job
  • 2Write queries using tumbling, hopping, sliding, session and snapshot windows and record the difference between each
  • 3Configure watermarking and late-arrival tolerance, then feed the job deliberately late events and observe the result
  • 4Enable Exactly Once Delivery on a Stream Analytics output and explain what problem it solves
  • 5Scale a job with streaming units, then partition input and output to raise throughput
Week 7: Spark structured streaming and schema drift
10-12 hours
  • 1Build a structured streaming job that reads from Event Hubs and writes to a Delta table
  • 2Configure checkpoint locations and prove that restarting the job resumes rather than reprocesses
  • 3Handle schema drift by enabling schema evolution on the Delta sink and adding a new source column mid-stream
  • 4Replay archived stream data from Event Hubs Capture and compare the result with the live run
  • 5Compare Stream Analytics and Spark structured streaming on latency, language and operational overhead
Week 8: Data security and governance
10-12 hours
  • 1Apply dynamic data masking and column-level security to a Synapse dedicated SQL pool table and test them with a low-privilege user
  • 2Implement row-level security with a security predicate function and confirm it filters correctly
  • 3Configure private endpoints for the storage account and the Synapse workspace and verify public access is blocked
  • 4Configure a data retention policy through Data Lake Storage lifecycle management
  • 5Push lineage from a Data Factory pipeline to Microsoft Purview and inspect the resulting lineage graph
Week 9: Monitoring, optimisation and troubleshooting
10-12 hours
  • 1Route Data Factory, Synapse and Stream Analytics diagnostics into a Log Analytics workspace and query them with Kusto
  • 2Build an alert rule and action group that fires on pipeline failure and on Stream Analytics watermark delay
  • 3Compact a folder full of small files and measure the query time before and after
  • 4Update statistics on a dedicated SQL pool table and compare the execution plan with the stale-statistics plan
  • 5Break a Spark job on purpose, then diagnose it from the Spark UI stages and driver log rather than from the error banner
Week 10: Case studies and timed rehearsal
10-12 hours
  • 1Take the free Microsoft practice assessment on Microsoft Learn and record which of the three functional groups scored lowest
  • 2Sit a full timed practice test from MeasureUp or Whizlabs under 100-minute conditions with no notes
  • 3Work through two full case studies end to end, since DP-203 case studies bundle storage, processing and security into one scenario
  • 4Rebuild every lab you got wrong rather than rereading the explanation
  • 5Practise finding three specific answers on learn.microsoft.com in under 90 seconds each, since the exam gave you Learn in a split-screen panel with no extra time

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Best Study Resources

Microsoft Learn study guide for Exam DP-203

Official blueprint

The authoritative skills-measured document, including the October 24, 2024 version, the percentage range for each of the three functional groups, every bullet under the nine sub-objectives, and a change log against the previous version. Check every third-party resource against this one.

Free

Microsoft Learn data engineering learning paths

Self-paced training

Microsoft's module collection covering Data Factory, Synapse Analytics, Stream Analytics, Databricks and Data Lake Storage. Several modules include browser-based sandboxes so you can complete exercises without provisioning billable Synapse or Databricks resources.

Free

Microsoft free practice assessment for DP-203

Practice questions

Microsoft's own practice item set, written to match the style, wording and difficulty of live questions. Microsoft states these are not the exam questions and do not reflect exam length or complexity. Practice assessments for retired exams may be removed from Learn.

Free

Microsoft exam sandbox

Interface demo

A demo of the live exam user interface, with the same introductory screens, instructions, help pages and Candidate Agreement. It matters more on DP-203 than on most exams because the build list and case study formats carry a lot of the marks. The secure browser is not enabled in the sandbox.

Free

Azure free account

Hands-on lab environment

Required for realistic practice, because DP-203 tests operational behaviour that only appears when you run something. Watch the spend: Synapse dedicated SQL pools and Databricks clusters bill by the hour, so pause pools and set cluster auto-termination after every session.

Free tier plus pay-as-you-go beyond it

Azure Synapse Analytics and Azure Data Factory documentation

Documentation

The reference Microsoft itself links from the DP-203 study guide. The distribution guidance for dedicated SQL pools, the mapping data flow transformation reference and the trigger types page each answer a recurring family of exam questions.

Free

Azure Stream Analytics windowing functions documentation

Documentation

The page that defines tumbling, hopping, sliding, session and snapshot windows with diagrams. Windowing is the single most reliably tested concept in the stream processing objective, and the diagrams are what make the difference between hopping and sliding stick.

Free

Delta Lake documentation

Documentation

Both the batch and stream objectives list reading from and writing to a delta lake. The open Delta Lake docs cover MERGE for upserts, time travel for reverting to a previous state, OPTIMIZE for small-file compaction and schema evolution, which are four distinct DP-203 exam tasks.

Free

MeasureUp practice tests

Practice exam

MeasureUp is Microsoft's officially licensed practice test partner. Its timed certification mode plus a study mode with per-objective explanations helps locate which of the three functional groups is holding your scaled score below 700.

Paid

Microsoft Q&A for Azure Synapse Analytics and Data Factory

Community forum

Microsoft-moderated question and answer site where product engineers answer specific pipeline, Spark and Stream Analytics failures. The optimise and troubleshoot objective names failed Spark jobs and failed pipeline runs including activities executed in external services, and this is where the exact error text from those failures gets explained.

Free

Common Mistakes to Avoid

Spending equal study time on the three functional groups.

Microsoft weighted them 15 to 20 percent for storage, 40 to 45 percent for data processing and 30 to 35 percent for secure, monitor and optimize. Data processing alone was worth more than twice as much as storage. Allocate weeks to the four data processing sub-objectives before touching partitioning theory.

Learning Stream Analytics window types by name without the timing behaviour.

Tumbling windows are fixed and non-overlapping, hopping windows are fixed and overlap by a hop size, sliding windows only emit when an event enters or leaves, session windows group by activity separated by a timeout, and snapshot windows group events with the identical timestamp. Run one query per type over the same input and read the differing output.

Choosing a Synapse dedicated SQL pool distribution by guesswork.

Hash distribution suits large fact tables joined on a common key, round robin suits staging tables with no obvious join key, and replicated tables suit small dimensions under a couple of gigabytes. Exam scenarios describe table size and join pattern, which is exactly the information the decision needs.

Treating Azure RBAC and Data Lake Storage Gen2 ACLs as one access control system.

They are evaluated separately, and a Storage Blob Data Owner role assignment bypasses ACL checks while a lower role does not. Set up a test user with RBAC only, then with ACLs only, then with both, and record what each combination can actually read and write.

Skipping the troubleshooting objective because it looks like production experience you cannot fake.

It names the failure modes explicitly: small files, data skew, data spill, failed Spark jobs and failed pipeline runs including activities executed in external services. Each has a standard remedy. Compact small files, salt or broadcast to fix skew, raise memory or reduce partition size for spill, read the Spark UI stage detail for failed jobs.

Ignoring Microsoft Purview because it sounds like a governance product rather than an engineering one.

Two of the four tasks in the data exploration layer objective are pushing new or updated data lineage to Microsoft Purview and browsing and searching metadata in the Purview Data Catalog. Register a source, run a scan, and look at a lineage graph produced by a real Data Factory pipeline.

Studying Databricks only as a notebook environment.

The security objective lists implementing resource tokens in Azure Databricks, loading a DataFrame with sensitive information and writing encrypted data to tables or Parquet files. The batch objective lists integrating notebooks into a pipeline. Practise Databricks as a component of a governed pipeline, not as a standalone tool.

Answering case study questions before reading the whole case study.

DP-203 case studies bundle storage design, processing, security and monitoring requirements into one scenario, and a constraint stated in the business requirements tab frequently rules out the answer that looks correct from the technical tab alone. Read every tab first, then answer, because you cannot return to any case study question after taking a break.

Assuming Delta Lake time travel and pipeline rerun solve the same problem.

Reverting data to a previous state is a data-level operation using Delta versioning or RESTORE, while rerunning a failed pipeline from the point of failure is an orchestration-level operation in Data Factory. The blueprint lists both separately, and scenarios describing corrupted downstream data want the first while scenarios describing a transient activity failure want the second.

Exam Day Tips

  • 1

    DP-203 retired on March 31, 2025 at 11:59 PM Central Standard Time, so there is no longer an exam day for it. The tips below describe how it ran, and every one of them is current Microsoft policy that applies unchanged to DP-700, the live Fabric Data Engineer Associate exam.

  • 2

    The name on your government-issued ID must exactly match the name on your Microsoft Certification profile. Microsoft does not accept legal name-change forms or ID renewal forms for online proctored exams, so correct any mismatch on the profile well before booking.

  • 3

    Online check-in opens 30 minutes before the appointment and closes 15 minutes after it, and Microsoft says the launch process takes roughly 15 minutes. You photograph your headshot, ID and room with a mobile phone, and a greeter reviews all of it before you are queued for a proctor.

  • 4

    Clear the desk entirely. Books, notepads, sticky notes, printed pages, pens, markers and whiteboards must be out of arm's reach, extra monitors unplugged and turned away, and any other computers in the room switched off. There is no scratch paper on an online Microsoft exam, so practise doing pipeline reasoning in your head.

  • 5

    Water is permitted without an accommodation request but only in a transparent, spill-proof container. Eating, smoking and chewing gum are prohibited unless you hold an approved accommodation.

  • 6

    Five minutes of break time is built into the exam clock, but the clock keeps running while you are away and you cannot return to any question you have already seen. On DP-203 this bites hardest on case studies, where breaking mid-case loses every question in that case.

  • 7

    You cannot start a break inside a lab or inside a problem-and-solution question set, the format where the same data pipeline scenario repeats with a different proposed solution each time. Decide each of those independently rather than assuming only one can be correct.

  • 8

    Use the Microsoft Learn button in the left navigation pane to open learn.microsoft.com in a split screen, and search within a page using Ctrl+F or Command+F. Microsoft Q&A, practice assessments and your profile are blocked, all non-Learn domains are blocked, and no extra time is added for using it.

  • 9

    If your form contains labs, expect roughly 30 minutes for scoring instead of an immediate result, and the score appears in your Learn profile within 24 hours either way.

Career Paths & Salary Ranges

Azure data engineer

Builds and operates ingestion and transformation pipelines across Data Factory, Synapse, Databricks and Data Lake Storage. This is the role the DP-203 blueprint was written against, and it sits in the lower part of the $110k-$160k band this site uses for the credential.

$110k-$140k

Analytics engineer

Owns the modelling layer between raw storage and business reporting, working in SQL against Synapse or a lakehouse. The partitioning, distribution and statistics material from the storage and optimisation objectives is the part of DP-203 this role uses every day.

$110k-$140k

Streaming data engineer

Specialises in real-time ingestion with Event Hubs, Stream Analytics and Spark structured streaming. The windowing, watermarking, checkpointing and late-arrival handling tasks in the stream processing objective map directly onto this job, and it commands a premium because fewer engineers do it well.

$125k-$155k

Data platform lead

Sets the architecture pattern, whether modern data warehouse, big data or lakehouse, and owns security, lineage and cost across the platform. The secure, monitor and optimize group, worth 30 to 35 percent of DP-203, is the closest match to this role's scope.

$135k-$160k

Microsoft Fabric data engineer

The forward-looking version of the same role, built on Microsoft Fabric rather than standalone Azure services and certified through DP-700. Fabric asks for SQL, PySpark and Kusto Query Language, so a DP-203 background covers two of the three languages already.

$115k-$150k

Prerequisites & Requirements

  • There were no mandatory prerequisites. Microsoft did not require another exam or certification before DP-203.
  • Microsoft's published candidate profile asked for subject matter expertise integrating, transforming and consolidating data from structured, unstructured and streaming systems into a schema suitable for analytics.
  • Solid knowledge of SQL, Python and Scala, which Microsoft named directly as the required data processing languages.
  • An understanding of parallel processing and of data architecture patterns including modern data warehouse, big data and lakehouse designs.
  • Proficiency with Azure Data Factory, Azure Synapse Analytics, Azure Stream Analytics, Azure Event Hubs, Azure Data Lake Storage and Azure Databricks.
  • As of March 31, 2025 the exam is retired, so passing it is no longer possible regardless of preparation.

Frequently Asked Questions

Can I still take the DP-203 exam?

No. Microsoft retired DP-203 and the Azure Data Engineer Associate certification on March 31, 2025 at 11:59 PM Central Standard Time. Microsoft's retirement guidance states that retired exams can no longer be taken and the associated certification can no longer be earned after that date. The study guide remains published on Microsoft Learn with a retirement warning at the top.

What replaced DP-203?

Microsoft's live data engineering credential is Fabric Data Engineer Associate, earned through exam DP-700. Its three published skill areas are implementing and managing an analytics solution, ingesting and transforming data, and monitoring and optimizing an analytics solution, all within Microsoft Fabric. Microsoft asks DP-700 candidates for skill in SQL, PySpark and Kusto Query Language, allows 100 minutes, and offers the exam in English, Japanese, Chinese (Simplified), German, French, Spanish and Portuguese (Brazil), which is a shorter list than the ten languages DP-203 supported.

I already hold the Azure Data Engineer Associate certification. What happens to it?

It stays on your Microsoft Learn transcript. Microsoft states that certifications already earned remain on the transcript in your Learn profile after retirement. Because the renewal assessment retired at the same time as the exam, the certification cannot be renewed, so it will show as expired once its one-year validity period ends.

How much did DP-203 cost?

Microsoft published no figure that can be quoted here. Its exam detail pages state one thing about price: it is based on the country or region in which the exam is proctored. The amount charged at Pearson VUE checkout therefore varied with local pricing, currency and tax, and each retake required paying it again.

What was the passing score?

700 on a scale of 1 to 1,000. Microsoft applies this to all technical exams and notes that because the score is scaled it may not equal 70 percent of the available points. Harder question sets require fewer raw points to clear 700 and easier ones require more, which is why Microsoft does not publish a percentage.

How many questions did DP-203 have?

Microsoft never published a question count for it. Microsoft's official statement covering all of its exams is that most contain between 40 and 60 questions, that the count varies by exam, and that it changes as exams are updated. Case studies on DP-203 grouped several questions under one scenario, which affects how the count feels even when the number is unchanged.

How long did the exam take?

100 minutes of exam time. The Azure Data Engineer Associate certification page still states "You will have 100 minutes to complete this assessment" under its retirement warning. Book about 120 minutes of seat time against that clock, which is what Microsoft's published table allocates for an associate role-based exam without labs; the extra 20 minutes covers instructions, the Candidate Agreement and the comment screens rather than questions.

What was the retake policy?

A 24-hour wait after a first failure, then 14 days between each subsequent attempt, with a maximum of five attempts in any 12-month period counted from the first attempt. After five failures you became eligible again 12 months from the date of that first attempt. Every retake required paying the exam fee again.

Could the retake waiting period be waived?

Only after a technical failure. Microsoft grants waivers when a candidate experienced internet connectivity issues or equipment failure during the exam and a Pearson VUE case was created, and the request must quote that Pearson VUE case number. Microsoft states that waivers will not be granted for any other reason.

How quickly did results arrive?

Most Microsoft exams return a result within minutes of finishing, with a report showing the numeric score, pass or fail status and a bar chart of performance by skill area. Exams containing labs take about 30 minutes to score. In both cases the result reaches your Microsoft Learn profile within 24 hours.

Does the score report show which questions I failed?

No. Microsoft does not disclose which individual questions were answered incorrectly, citing exam integrity. You receive one overall scaled score and a bar chart of relative strength across the three functional groups. Microsoft warns that bar lengths cannot be converted into a question count, because the groups contain different numbers of questions, and that a small group can produce a zero bar.

What ID did I need for an online proctored exam?

A current government-issued ID with a name that exactly matches the name on your Microsoft Certification profile, with acceptable document types listed on Pearson VUE's Before test day page. Microsoft does not accept legal name-change forms or ID renewal forms for online proctored exams, so an in-progress name change had to be settled before test day.

What were the rules for testing from home?

The room had to be walled, have a closed door and be free of disruption, with nobody else entering at any point. You unplugged additional monitors, turned off other computers, cleared the desk, and kept notes, books, pens and your phone out of arm's reach. Pearson VUE recorded video and audio throughout, may have used facial comparison technology to match you to your ID, and could ask you to turn out pockets, roll up sleeves or pull back long hair. Candidates who preferred not to be recorded at home could book a test centre.

Were reference materials, notes or a calculator allowed?

No physical materials, notes or calculators were permitted. Because DP-203 was an associate role-based exam, you could open learn.microsoft.com in a split-screen panel during the exam and search within pages using Ctrl+F or Command+F. Microsoft Q&A, practice assessments and your own profile were blocked, all other domains were blocked, and Microsoft added no extra time for using the feature.

How did breaks work on DP-203?

Unscheduled breaks were allowed without advance arrangement, and Microsoft built five minutes into the exam time by removing questions to fund it. The clock kept running during the break, multiple breaks were allowed, and no adjustment was made if the timer expired while you were away. Once a break began you could not return to any previously seen question, and breaks could not be started inside a lab or inside a problem-and-solution question set.

What accommodations were available?

Microsoft offers accommodations including extra time, assistive devices and modifications to the exam experience, and they must be requested before scheduling rather than on the day. Assistive devices need prior approval because the secure browser blocks third-party applications during a live exam. Separately, candidates taking an exam not offered in their preferred language could request an extra 30 minutes, which did not apply to the ten languages DP-203 supported.

How did DP-203 compare with DP-700?

DP-203 tested six standalone Azure services while DP-700 tests the same engineering discipline inside Microsoft Fabric as a single platform. The transferable material is large: partitioning, incremental loading, watermarking, schema drift, Delta Lake operations, small-file compaction and Spark job troubleshooting all carry across. The main new demand in DP-700 is Kusto Query Language, alongside the SQL and PySpark that DP-203 already required.

Does Microsoft publish a pass rate for DP-203?

No. Microsoft does not publish pass rates for any of its certification exams, and it does not publish the number of candidates who attempted or passed a given exam. Any pass rate figure you see for DP-203, including the one in this site's catalogue, is an estimate drawn from candidate-reported data rather than from Microsoft.

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