Exam DP-700 (Implementing Data Engineering Solutions Using Microsoft Fabric) earns you the Microsoft Certified: Fabric Data Engineer Associate credential. You get 100 minutes, you need a score of 700 or higher, and each of the three skill areas is weighted 30–35%. We suggest about six weeks of hands-on practice, not just reading.
What the DP-700 exam proves and who it is for
Microsoft lists the credential as Intermediate level, for the Data Engineer role, on Microsoft Fabric. Per the official certification page, candidates should have subject matter expertise with data loading patterns, data architectures, and orchestration processes.
The role covers three jobs:
- Ingesting and transforming data.
- Securing and managing an analytics solution.
- Monitoring and optimizing an analytics solution.
You work with analytics engineers, architects, analysts, and administrators. Microsoft expects you to be skilled at transforming data with SQL, PySpark, and KQL (Kusto Query Language). If one of those three is new to you, add extra time for it in your plan.
Skills measured and their weights
The official study guide (last updated 2026-10-30) splits the exam into three equally weighted areas.
| Skill area | Weight | What it covers |
|---|---|---|
| Implement and manage an analytics solution | 30–35% | Workspace settings, lifecycle management, security and governance, orchestration |
| Ingest and transform data | 30–35% | Loading patterns, batch data, streaming data |
| Monitor and optimize an analytics solution | 30–35% | Monitoring items, resolving errors, performance tuning |
Here are the topics inside each area that deserve the most hands-on time.
Implement and manage an analytics solution
- Spark, domain, OneLake, and data workflow workspace settings.
- Version control, database projects, and deployment pipelines.
- Workspace-level and item-level access, plus row-level, column-level, object-level, and file-level controls.
- Dynamic data masking, sensitivity labels, and endorsement.
- Choosing between a pipeline and a notebook, schedules, event-based triggers, parameters, and dynamic expressions.
Ingest and transform data
- Full and incremental loads, and preparing data for a dimensional model.
- Shortcuts, mirroring, and ingestion with pipelines.
- Transforming with PySpark, SQL, and KQL.
- Handling duplicate, missing, and late-arriving data.
- Eventstreams, Spark structured streaming, KQL, and windowing functions.
Monitor and optimize an analytics solution
- Monitoring ingestion, transformation, and semantic model refresh, plus alerts.
- Fixing errors in pipelines, dataflows, notebooks, eventhouses, eventstreams, and T-SQL.
- Optimizing lakehouse tables, pipelines, warehouses, eventstreams, eventhouses, Spark, and queries.
Microsoft notes that most questions cover generally available features. The exam may also include Preview features if they are commonly used.
Exam format and rules
| Item | Detail |
|---|---|
| Duration | 100 minutes |
| Passing score | 700 or greater |
| Delivery | Proctored, scheduled through Pearson VUE |
| Question style | May include interactive components |
| Retakes | 24 hours after the first attempt; longer waits for later retakes |
| Languages | English, Japanese, Chinese (Simplified), Chinese (Traditional), German, French, Italian, Korean, Spanish, Portuguese (Brazil) |
Microsoft recommends registering with a personal Microsoft account. If you use a work or school account and leave that organization, your exam records can be lost for good. Full retake rules are on Microsoft's retake policy page.
Try the exam sandbox before test day. It uses the same interface as the real exam, so the question types will not surprise you.
What DP-700 costs and how the voucher works
The table below shows prices in USD as of 2026-11-04. The list price comes from Microsoft's official exam price, as published by the vendor.
| Option | Price |
|---|---|
| Microsoft list price | $165.00 |
| Our voucher price | $44.99 |
Microsoft says the exam price depends on the country or region where you take it. Check the final price in your own region at checkout on the Pearson VUE scheduling flow.
Here is how our DP-700 voucher works:
- We email the code within 12 hours of payment. It also appears in your account.
- The voucher is valid for 12 months and is redeemed with Pearson VUE.
- You can cancel an order for a full refund before delivery.
- A delivered code is non-refundable unless the test provider rejects it as invalid, already used, or expired on first use.
- If that happens, contact support within 14 days with your order number and a screenshot. We replace or refund the code.
The full terms are on our refund policy and delivery policy pages.
A six-week study plan with weekly hours
This plan assumes about 8 hours a week, roughly 48 hours in total. It is a suggested pace, not a Microsoft figure. Cut it if you already build Fabric solutions at work. Stretch it if SQL, PySpark, or KQL is new to you.
| Week | Hours | Focus | Hands-on task |
|---|---|---|---|
| 1 | 8 | Workspace settings, OneLake, lifecycle management | Create a workspace, connect Git, build a deployment pipeline |
| 2 | 8 | Security and governance | Set item-level access, row-level security, dynamic data masking, sensitivity labels |
| 3 | 8 | Orchestration and batch loading | Build a pipeline with parameters and a schedule, then an incremental load |
| 4 | 8 | Transformation in PySpark, SQL, and KQL | Deduplicate data, handle late arrivals, build a dimensional table |
| 5 | 8 | Streaming | Build an eventstream, a Spark structured streaming job, and a windowed KQL query |
| 6 | 8 | Monitoring, errors, optimization, then practice | Break things on purpose and fix them. Take the practice assessment and the sandbox |
Use Microsoft's free resources first. The study guide links to Microsoft Learn paths, Fabric documentation, and an exam prep video. Microsoft recommends the new Practice Assessment on AI Skills Navigator. The legacy one at https://aka.ms/DP700-LearnPA is no longer updated.
When you miss a practice question, write down the skill name from the study guide. Review that skill in a real workspace, not just in notes.
Picking the right Fabric tool: a worked example
The study guide asks you to choose between pipelines and notebooks, and between dataflows, notebooks, and T-SQL. This table is a simple way to reason through those questions. It is our own study aid, so confirm details in the Fabric documentation.
| Situation | Usually a good fit |
|---|---|
| Copy data on a schedule or after an event, with branching and retries | Pipeline |
| Complex transformation or custom logic on large data | Notebook (PySpark) |
| Set-based work on warehouse tables by SQL-focused teams | T-SQL |
| Low-code reshaping by analysts | Dataflow |
| Real-time events with time windows | Eventstream, or KQL in an eventhouse |
Now a small worked example. You load new orders from a bronze lakehouse table into a silver lakehouse table. Both tables live in the notebook's default lakehouse, so one PySpark cell does the whole job. It finds the last load time, then reads only newer rows, removes duplicates, and appends them.
The first run needs care. When SalesSilver is empty, MAX(LoadedAt) returns None, and a filter against None returns no rows. The code below falls back to a minimum timestamp so the first run loads everything.
from pyspark.sql.functions import col
from datetime import datetime
last_load = spark.sql('SELECT MAX(LoadedAt) FROM SalesSilver').first()[0]
if last_load is None:
last_load = datetime(1900, 1, 1)
new_rows = spark.read.table('SalesBronze').filter(col('LoadedAt') > last_load)
new_rows.dropDuplicates(['OrderId']).write.mode('append').saveAsTable('SalesSilver')
For streaming, practice a windowed count in KQL:
Events
| where Timestamp > ago(1h)
| summarize Events = count() by bin(Timestamp, 5m)
Exam questions often test why you picked a pattern. Be ready to explain why an incremental load beats a full reload, and when a tumbling window beats a raw count.
Common mistakes to avoid
- Reading only. The exam covers errors and tuning. You learn those by breaking and fixing real items.
- Skipping KQL. Many candidates focus on SQL and PySpark, but KQL appears in transformation and streaming, and the guide also lists eventhouse errors.
- Treating the three areas as unequal. Each is weighted 30–35%, so a weak monitoring score can sink you.
- Memorizing exam dumps. They often reflect old features and teach no skills. Questions can also include interactive components that dumps cannot prepare you for.
- Using a work account to register. Records can be lost if you leave the organization.
- Ignoring the sandbox. Learn the interface before the clock is running.
After you pass
You earn the Microsoft Certified: Fabric Data Engineer Associate credential. Microsoft role-based and specialty certifications expire unless renewed. You can renew at no cost by passing an online assessment on Microsoft Learn.
If you want more data and analytics options, browse our Data & Analytics certifications. Ready to book? Get the DP-700 exam voucher and schedule your exam through Pearson VUE.
Originally published at BuyExamVouchers.
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