Microsoft Certified: Fabric Data Engineer Associate
Fabric Data Engineer certifies that you can ingest, transform and serve data inside Microsoft Fabric: lakehouses, warehouses, pipelines, notebooks and the semantic layer that analysts consume.
A hundred minutes, twelve months of validity, and a narrower bet than the other data exams here.
Exam facts
- Exam code
- DP-700
- Level
- Associate
- Field
- Data & AI
- Duration
- 100 minutes
- Questions
- about 40 to 60, including case studies and drag-and-drop; code in SQL, PySpark and KQL to read and complete
- Passing score
- 700 of 1,000 (scaled)
- Languages
- English, German, French, Spanish, Portuguese, Japanese, Chinese
- Price
- 165 USD (read 8 September 2026)
- Validity
- 1 year, renewable
- Exam delivery
- Pearson VUE (test centre or online proctored); students through Certiport
Prerequisites: No required certification. Microsoft expects experience with data loading patterns, data architectures and orchestration, and with manipulating data in SQL, PySpark and KQL.
Renewal: Free yearly renewal through an online assessment on Microsoft Learn, available from six months before expiry
Source: exam page at Microsoft · Price: Priced by the country where the exam is proctored: 165 USD in the United States, 126 EUR in Germany and Austria (Microsoft Learn country selector, read 2026-09-08)
How to prepare
1. The vendor's free learning path
Microsoft publishes the exam objectives and a learning path free of charge — the authoritative source for scope and weighting. Open the learning path (opens in a new tab)
2. A practice test in the exam format
MeasureUp sells a practice test for DP-700 with questions in the exam format, an explanation for every answer and a timed mode; the price shows in your currency on the shop page. Affiliate link.
Practice test for DP-700 at MeasureUp (opens in a new tab)Book the exam
Delivered by Pearson VUE (test centre or online proctored); students through Certiport. Schedule with Microsoft (opens in a new tab)
Affiliate disclosure: the practice-test link above is affiliate links — buying through them may earn us a commission at no extra cost to you. The vendor's learning path and booking links carry no commission.
The syllabus divides almost evenly between ingesting and transforming, implementing the analytics solution, and monitoring and optimising it. That even split is unusual and tells you something: Fabric is sold as one product covering the whole path from source to report, and the exam tests the whole path rather than a speciality within it. Candidates who arrived from a single tool find the breadth harder than the depth.
- Lakehouse against warehouse is the recurring decision, and you are expected to argue for one rather than fall into it.
- Pipelines and notebooks both appear, and questions often hinge on which is appropriate rather than how to write either.
- Incremental loading and what happens when a source changes shape underneath you.
- Capacity and cost, which in Fabric is a single pooled resource and behaves unlike per-service billing elsewhere.
- Security on data rather than infrastructure, including row-level and column-level controls the analysts will meet.
- If Fabric is already your platform, this is the most directly useful certificate on this site for your work, because almost nothing else covers it.
- If your organisation is evaluating Fabric, the syllabus doubles as a decent map of what adopting it would involve.
- If you work with data elsewhere, the concepts transfer and the certificate does not, and a platform-neutral engineering exam will travel further.
- If you are choosing a first data certificate, start at the fundamentals tier or with an analytics exam; this assumes a working environment you probably do not have.
Three thirds, three languages of code
| Skill area | Share | The code you will read |
|---|---|---|
| Implement and manage an analytics solution | 30 to 35 % | SQL for the warehouse side, plus the security and lifecycle settings around it |
| Ingest and transform data | 30 to 35 % | PySpark in notebooks, SQL in pipelines, and the choice between them |
| Monitor and optimise an analytics solution | 30 to 35 % | KQL against the logs, and the capacity metrics that explain a slow morning |
The right-hand column is the part candidates underestimate. Microsoft says outright that you will read and complete code in SQL, PySpark and KQL, and each third of the paper leans on a different one. An analyst fluent in SQL meets PySpark cold in the ingestion third and KQL cold in the monitoring third; a Spark engineer meets the warehouse and its T-SQL habits. Nobody arrives fluent in all three, and the sensible preparation is to identify which of the three you cannot read and spend the first fortnight there rather than on the one you already know.
No prep course in our catalogue matched this exam
Our latest pass through the English course pool found nothing current on Fabric. So the route we can recommend is Microsoft's free learning path plus an exam-format rehearsal from our partner, stated plainly instead of dressing up a course about a neighbouring product. A trial capacity, which Microsoft offers, is worth more than either.
Against the other data engineering associates
Set beside its neighbours the shape is distinctive. The AWS data engineer exam lasts three years, is offered in four languages and renews at half price or through a higher exam; the Databricks associate lasts two years and renews only by resitting; this one is valid for a year and renews at no cost via an online assessment, in seven languages including German. A hundred minutes, a question count Microsoft publishes only as a bracket, a scaled 700 to pass, Pearson VUE delivery and a fee set by the country of sitting complete the facts. The bet is the platform's youth: Fabric is moving fast enough that some of what you learn will be renamed within a cycle, which is precisely why the renewal is annual and free, and why certified engineers are scarce enough to be worth something while that lasts.
What a question looks like
Written by us in the exam's style. It is not a real question from any question bank, and we do not publish those.
A nightly load into a lakehouse table has begun taking hours longer as history accumulates, though the daily volume is unchanged. Analysts querying the table report no change in behaviour. What is the most likely cause and the appropriate remedy?
The symptom looks like a performance problem on the read side and is not, because the readers are unaffected. The question rewards understanding how a table accumulates small files or unoptimised history and what maintenance addresses it, which is the sort of operational knowledge that only comes from having run one for a few months.
What it costs to get and to keep
| Item | Amount | Note |
|---|---|---|
| Exam fee | 165 USD | United States price; Microsoft charges by the country of proctoring (read 12 September 2026) |
| Exam fee, Germany and Austria | 126 EUR | from the Microsoft Learn country selector (read 8 September 2026) |
| Annual renewal | not published | free and unproctored on Microsoft Learn, in the six months before expiry only (read 12 September 2026) |
| Platform access for practice | not published | not published as a certification cost, but a Fabric capacity is required to practise meaningfully and most candidates need an employer trial rather than a personal one (read 12 September 2026) |
How much preparation, from where you are
- You build in Fabric at work
- The gaps are usually monitoring and capacity management, which tend to belong to whoever owns the bill rather than to whoever writes the pipelines.
- You engineer data on another stack
- The ideas carry over and the product does not. Expect the effort to be almost entirely about where things live in Fabric and how its pooled capacity model differs from paying per service.
- You are an analyst moving upstream
- A reasonable destination and a real climb. The semantic layer will be familiar; ingestion, incremental loading and optimisation will not.
What passing this does not prove
- Whether the data arriving is correct, which remains the job rather than the exam.
- Anything portable, beyond concepts, to a stack that is not Fabric.
- Cost governance in practice, only the mechanisms that report it.
- Whether the platform will still look like this in three years.
Against the alternatives
- DEA-C01 — AWS Certified Data Engineer – Associate
- AWS's data engineering associate covers the same discipline across composable services rather than one integrated product. Broader ecosystem, more transferable habits, and a three-year validity against this one's twelve months.
- PL-300 — Microsoft Certified: Power BI Data Analyst Associate
- The analytics certificate on the consumption side of the same platform. If you build the reports rather than the pipelines, that is your exam.
- DP-900 — Microsoft Certified: Azure Data Fundamentals
- One tier down and purely definitional; nothing gates this exam behind it, so anybody already engineering data elsewhere can pass straight over.
DP-700 — quick answers
Is Fabric too young to certify in?
It is a fair concern and it cuts both ways. The product is moving quickly, which means some of what you learn will be renamed, and the twelve-month renewal exists partly to absorb that. Against it: certified Fabric engineers are scarce, and scarcity is worth something while it lasts.
Can I prepare without a capacity?
Poorly. Too many questions describe behaviour you would only recognise from having watched a load run. Trials exist, and where an employer is considering Fabric they are usually willing to provision one, which is the realistic route for most candidates.
Does Power BI experience help?
Substantially on the serving side, because the semantic layer is the part analysts already know. It helps very little with ingestion and optimisation, which carry two thirds of the weighting between them.
How much PySpark do I need?
Enough to read a notebook cell and say what it does to a dataframe, and to spot the line that will not scale. Nobody asks you to author a job from a blank cell. SQL-only candidates should budget real time here, because the ingestion third assumes it.
What is KQL doing on a data engineering exam?
Monitoring. Fabric exposes its logs and metrics through the Kusto Query Language, and the third of the paper about optimisation expects you to read a query against them and say what it shows. It is the language most candidates have never met, and the one that costs the most marks per line.
What comes next
Concepts this exam draws on
Glossary entries with the reason each one matters for DP-700.
- Data Engineering
Fabric data engineering end to end: ingestion, lakehouse, pipelines.
- Data Wrangling
Transforming data with Spark, SQL and dataflows.
- Data Analysis
Monitoring and optimising analytics workloads in Fabric.
- SQL
Warehouse-side work in Fabric is T-SQL, one of the three languages the exam has you read.
Last reviewed 12 September 2026 · Getting Digital