Validates skills in building Microsoft Fabric pipelines, including ingestion and transformation with PySpark, SQL and KQL, and monitoring.
DP-700 tests whether you can choose, build, secure and fix data engineering solutions in Fabric through scenarios and a case study. The three areas carry equal weight, so build batch and streaming pipelines yourself, practise SQL, PySpark and KQL, and learn how to diagnose failures and slow items.
Example scenario: A notebook must run only after a pipeline copies new files, and it needs the file date as input. How would you orchestrate and pass that value?
Example scenario: A source table updates throughout the day and you only want changed rows each night. How would you design the incremental load?
Example scenario: Queries on a lakehouse table slow down after months of small daily appends. What maintenance would you run?
DP-700 study guide · Skills measured as of October 19, 2026
Choose the best answer from the options, or the stated number of answers when a question asks for more than one.
A pipeline must run a notebook only after a copy activity succeeds. Which dependency should connect the two activities?
A. On success runs the notebook only when the copy works. On completion would also run it after a failure.
Move items from a list into the correct places, such as matching items to targets or putting steps in order.
Drag each need to the Fabric item that fits best.
Low-code transformations → Dataflow Gen2; orchestrate steps → data pipeline; PySpark code → notebook.
Choose the correct option for each part of an exhibit or statement list, often Yes or No for each statement.
For each statement, select Yes or No.
Yes, Yes, No. OPTIMIZE compacts small files; it does not delete rows.
Read one scenario with its background, requirements and exhibits, then answer several questions about it.
Case study: A factory streams sensor events and wants an alert within seconds when a temperature passes a limit. Question 1 of 3: Which items should receive the stream and raise the alert?
A. An eventstream takes the live events and Activator can act on a condition as the data arrives.
Sign in to Microsoft Learn with a personal Microsoft account, complete your certification profile, then schedule DP-700 with Pearson VUE.
Take the free practice assessment to see where you start, then work through the official learning paths and build each pipeline in a Fabric trial.
Most candidates spend 60–120 hours. Plan for the low end if you already build Fabric or Spark data pipelines; add time if Fabric is new to you or you have not built data pipelines before.
The free learning paths cover the skills measured, but passers say the modules alone are not enough. Add hands-on work in a Fabric trial and a video course if it helps.
Best free official courseImplement data engineering solutions using Microsoft Fabric (DP-700T00)
Best video courseMicrosoft DP-700 prep: Fabric Data Engineer Associate
Best for projectsExam Prep DP-700: Microsoft Fabric Data Engineer AssociatePractise all three languages. Write the same transformation in PySpark, T-SQL and KQL at least once, and learn which tool fits each job.
Start with Microsoft's free practice assessment, then MeasureUp's official test. Candidates who leaned only on practice tests often struggled, so pair them with hands-on labs.
Free official checkDP-700 practice assessment
Official practice testDP-700 Practice Test: Implementing Data Engineering Solutions Using Microsoft Fabric
DP-700 Fabric Data Engineer Practice ExamsChoose the checklist for your test-centre or online appointment.
A passer wrote that simply going through the Microsoft Learn modules was not enough, because real questions go further than the knowledge checks. She tracked progress against the skill list. Another found it tougher than expected and practitioner-level.
One candidate failed with 540 after relying mainly on practice tests, then passed with 912 after adding daily SQL, KQL and PySpark practice and Fabric labs.
The same passer used questions from the retired DP-203 Azure Data Engineer exam for extra practice, since the underlying concepts still apply.