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Microsoft DP-600: Implementing Analytics Solutions Using Microsoft Fabric Practice Exam

Cover all three current DP-600 skill areas — maintaining the analytics solution, preparing data, and implementing semantic models — aligned to Microsoft’s July 2026 blueprint, with scenario questions, immediate feedback in Learn mode, and full timed simulation in Exam mode.

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DP-600 exam at a glance

Vendor
Microsoft (Microsoft Fabric)
Exam code
DP-600 (Implementing Analytics Solutions Using Microsoft Fabric)
Certification
Microsoft Certified: Fabric Analytics Engineer Associate
Level
Associate (role-based; analytics engineering)
Blueprint
Skills measured as of July 21, 2026 (current, three-domain edition)
Format
Typically 40–60 questions; multiple choice, multiple response, drag-and-drop, and case studies
Duration
About 100 minutes
Passing score
700 of 1000 (scaled, not a raw percentage)
Delivery
Pearson VUE test center or online proctored
Recommended experience
Designing and managing analytical assets (semantic models, lakehouses, warehouses); querying with SQL, KQL, and DAX; building semantic models and reports in Power BI
Cost (USD)
$165 USD list price (associate tier; varies by region)
Validity
Valid one year; renewed free via an online assessment on Microsoft Learn

Source: Microsoft Learn — DP-600 study guide (skills measured) and the Fabric Analytics Engineer Associate certification page. Verify current details with Microsoft before scheduling.

About the Microsoft Certified: Fabric Analytics Engineer Associate certification

DP-600 validates that you can implement end-to-end analytics solutions in Microsoft Fabric — preparing and enriching data in lakehouses and warehouses, building and optimizing semantic models, and securing and maintaining the analytics solution across its lifecycle. It is the first Fabric-native certification Microsoft launched and remains the most-taken exam in the Fabric family. Expect scenario-based questions that ask you to pick the right Fabric component or design pattern, using SQL, KQL, and DAX, and favoring managed Fabric primitives like Direct Lake, shortcuts, and OneLake. A single exam earns the Fabric Analytics Engineer Associate certification.

DP-600 is an active exam, and its blueprint was significantly restructured. The current edition (skills measured as of July 21, 2026) uses three skill areas — not the older four-domain outline with a separate “Plan” area that many third-party guides still show. If a study resource lists a top-level “Plan, implement, and manage” domain at 10–15%, it is out of date. This practice exam is built to the current three-area outline. If you are new to Fabric, the DP-900 Azure Data Fundamentals exam is a good conceptual starting point.

Every PowerKram practice question maps to one of the three current DP-600 skill areas and links to the specific Microsoft Learn page it was derived from, so your weak spots become a focused reading list rather than a guess.

DP-600 skill areas and weights

The current edition (July 21, 2026) has three skill areas. Preparing data dominates the exam at 45–50% — nearly half — while maintaining the solution and implementing semantic models each carry 25–30%. Microsoft published each area as a weighting range; the ranges below are reproduced as Microsoft stated them. Plan your study time in proportion.

Maintain a data analytics solution

Implement security and governance (workspace- and item-level access; row-, column-, object-, and file-level access control; sensitivity labels; endorsement); and maintain the analytics development lifecycle (workspace version control, Power BI Desktop projects, deployment pipelines, downstream impact analysis, XMLA-endpoint deployment, reusable .pbit/.pbids assets and shared semantic models).

25–30%
Prepare data

The core of the exam: get data (connections, OneLake catalog and Real-Time hub discovery, ingestion, choosing a data store, OneLake integration); transform data (views, functions, stored procedures, star schema, denormalization, aggregation, joins, cleansing, type conversion, filtering); and query and analyze data using the Visual Query Editor, SQL, KQL, and DAX.

45–50%Heaviest
Implement and manage semantic models

Design and build semantic models (storage mode, star schema, relationships including bridge and many-to-many, DAX variables and functions, calculation groups, dynamic format strings, field parameters, large-model storage, composite models); and optimize enterprise-scale models (query and visual performance, DAX tuning, Direct Lake configuration and fallback, Direct Lake on OneLake vs SQL analytics endpoint, incremental refresh).

25–30%

Source: Microsoft Learn — DP-600 study guide (skills measured, as of July 21, 2026). Microsoft published weightings as ranges; preparing data is the single heaviest area at 45–50%. Note: older guides showing a four-domain outline with a separate “Plan” area are out of date.

Who DP-600 is for

Fabric Analytics Engineer Associate is aimed at data professionals building analytics solutions on Microsoft Fabric:

  • Analytics engineers designing and managing semantic models, lakehouses, and warehouses in Fabric.
  • BI developers and Power BI practitioners moving from report-building into end-to-end Fabric analytics.
  • Data engineers preparing and transforming data in OneLake with SQL, KQL, and DAX.
  • Data analysts deepening from visualization into modeling, optimization, and governance.

DP-600 assumes real experience with data modeling and Power BI — it is not an entry point. If you are new to data concepts, the DP-900 Azure Data Fundamentals exam is the conceptual starting point. If your focus is data engineering pipelines in Fabric rather than analytics modeling, the sibling DP-700 Fabric Data Engineer exam is the closer match. For the roles this credential supports — with skills, tools, and salary ranges — see the Data Engineer career path.

What this DP-600 practice exam delivers

Learn mode

Get the correct answer, the reasoning, and a direct link to the exact Microsoft Learn page each question was derived from — immediately after each question. Built for the “pick the right Fabric component” scenarios the exam favors.

Exam mode

A timed run in the DP-600 shape — roughly 40 to 60 questions in about 100 minutes, including case studies — so you build pacing for the exam’s scenario-heavy format, which can run tight.

Source-linked explanations

Every answer cites the exact Microsoft Learn documentation page it was built from — so you can verify Fabric behavior around lakehouses, semantic models, Direct Lake, and governance, not just memorize.

Score by skill area

Results break down by the three current DP-600 areas — maintain, prepare data, and semantic models — so practice tells you exactly which area to revisit. Expect to spend the most time on preparing data.

Sample DP-600 practice questions

Ten free questions across the three current DP-600 skill areas, weighted toward preparing data as the real exam is, with full explanations and source links to the Microsoft Learn pages each is derived from. The complete bank is available with the 24-hour trial.

Question 1 · Prepare data (45–50%)

Your team wants to query data that physically resides in an external Azure Data Lake Storage Gen2 account from a Fabric lakehouse, without copying or moving the data into OneLake.

Which Fabric feature should you use?

  1. A OneLake shortcut
  2. A full data copy with Data Factory
  3. A Power BI import dataset
  4. A deployment pipeline
Show answer & explanation

Correct: A — a OneLake shortcut. Shortcuts create a reference to data in external storage (such as ADLS Gen2) so Fabric can query it in place without copying or moving it — exactly the no-copy requirement described.

Why not the others: a full Data Factory copy (B) physically moves the data, the opposite of the requirement; a Power BI import dataset (C) also copies data into the model; a deployment pipeline (D) promotes content across environments, not data access.

Source: Microsoft Learn — OneLake shortcuts → Further reading: PowerKram — DP-700 Fabric Data Engineer →
Question 2 · Prepare data (45–50%)

You are modeling a Fabric warehouse for reporting. You want a design that separates descriptive attributes from numeric measures to make queries fast and intuitive for analysts.

Which data-modeling approach should you implement?

  1. A fully normalized third-normal-form model
  2. A star schema with fact and dimension tables
  3. A single wide flat table with no relationships
  4. A key-value document store
Show answer & explanation

Correct: B — a star schema with fact and dimension tables. A star schema separates numeric facts from descriptive dimensions, which is the recommended analytics model for a Fabric lakehouse or warehouse — fast, intuitive, and the pattern DP-600 expects.

Why not the others: full normalization (A) optimizes transactional writes, not analytics reads; a single flat table (C) loses reusable dimensions and bloats storage; a key-value store (D) is not a relational analytics model.

Source: Microsoft Learn — Dimensional modeling in Fabric →
Question 3 · Prepare data (45–50%)

Analysts want to run T-SQL SELECT queries against the tables in a Fabric lakehouse without writing Spark code.

Which lakehouse capability lets them do this?

  1. The Spark job definition
  2. The SQL analytics endpoint of the lakehouse
  3. A deployment pipeline
  4. A sensitivity label
Show answer & explanation

Correct: B — the SQL analytics endpoint of the lakehouse. Every Fabric lakehouse automatically exposes a read-only SQL analytics endpoint that lets you run T-SQL queries over its Delta tables without Spark — exactly the analyst requirement.

Why not the others: a Spark job definition (A) requires Spark code; a deployment pipeline (C) promotes content between stages; a sensitivity label (D) is a governance classification. None provides T-SQL query access.

Source: Microsoft Learn — Lakehouse SQL analytics endpoint → Further reading: PowerKram — DP-900 Azure Data Fundamentals →
Question 4 · Prepare data (45–50%)

A source table has customer records with several exact duplicate rows and some rows missing the country value. You must clean it before loading it into a dimension table.

Which two transformation steps directly address these issues?

  1. Increase the capacity SKU and add a sensitivity label
  2. Configure Direct Lake and a composite model
  3. Remove duplicate rows and handle the missing (null) values
  4. Create a deployment pipeline and endorse the item
Show answer & explanation

Correct: C — remove duplicate rows and handle the missing (null) values. Deduplication and resolving missing/null values are the standard data-cleansing transformations DP-600 tests, and they directly address the two problems described.

Why not the others: capacity SKU and labels (A) are governance/scaling, not cleansing; Direct Lake and composite models (B) are semantic-model concerns; pipelines and endorsement (D) are lifecycle actions. None cleans the data.

Source: Microsoft Learn — Data warehousing in Fabric →
Question 5 · Prepare data (45–50%)

A new team member needs to find existing certified datasets and tables across the tenant to reuse rather than rebuild them.

Which Fabric capability helps them discover these data items?

  1. A calculation group
  2. Incremental refresh
  3. The XMLA endpoint
  4. The OneLake catalog
Show answer & explanation

Correct: D — the OneLake catalog. The OneLake catalog lets users discover and explore data items across the tenant, so they can find and reuse existing assets — exactly the discovery requirement in the “get data” skill.

Why not the others: a calculation group (A) organizes DAX calculations; incremental refresh (B) optimizes model refresh; the XMLA endpoint (C) enables model management tooling. None is a data-discovery catalog.

Source: Microsoft Learn — OneLake overview →
Question 6 · Semantic models (25–30%)

You need a semantic model over a large lakehouse table that delivers near-import query speed while always reflecting the latest data in OneLake, without scheduled imports.

Which storage mode should you use?

  1. Import mode with scheduled refresh
  2. DirectQuery to an external SQL Server
  3. Direct Lake
  4. A live connection to Analysis Services
Show answer & explanation

Correct: C — Direct Lake. Direct Lake reads Delta tables directly from OneLake, giving near-import performance while reflecting the latest data without scheduled imports — exactly the requirement, and a signature Fabric feature DP-600 emphasizes.

Why not the others: Import (A) requires scheduled refresh and can be stale; DirectQuery to external SQL (B) adds latency and is not the OneLake-native option; an Analysis Services live connection (D) targets a different, external model source.

Source: Microsoft Learn — Direct Lake overview → Further reading: PowerKram — DP-700 Fabric Data Engineer →
Question 7 · Semantic models (25–30%)

A report shows sales that must be sliced by both the order date and the ship date from a single date table, and you want to avoid duplicating the date dimension.

Which modeling technique addresses this?

  1. A bridge table with a many-to-many relationship on every table
  2. Role-playing dimensions using inactive relationships activated with DAX (for example, USERELATIONSHIP)
  3. Importing the date table twice as unrelated copies
  4. Switching the model to DirectQuery
Show answer & explanation

Correct: B — role-playing dimensions using inactive relationships activated with DAX. A single date dimension can play multiple roles (order date, ship date) through additional inactive relationships that measures activate with USERELATIONSHIP — avoiding duplicate date tables, exactly as asked.

Why not the others: a many-to-many bridge on every table (A) is the wrong tool and adds ambiguity; importing the date table twice (C) is the duplication you are trying to avoid; DirectQuery (D) is a storage mode, unrelated to role-playing dimensions.

Source: Microsoft Learn — Fabric semantic models →
Question 8 · Maintain the solution (25–30%)

A warehouse table contains rows for many regional sales teams. Each manager must see only the rows for their own region when querying the same semantic model.

Which security feature enforces this?

  1. Row-level security (RLS)
  2. A sensitivity label
  3. A deployment pipeline
  4. Workspace admin role assignment
Show answer & explanation

Correct: A — row-level security (RLS). RLS filters table rows based on the querying user’s identity or role, so each manager sees only their region’s rows in the same model — exactly the requirement.

Why not the others: a sensitivity label (B) classifies an item but does not filter rows; a deployment pipeline (C) promotes content; a workspace admin role (D) grants broad management access, the opposite of row-level restriction.

Source: Microsoft Learn — Row-level security in Fabric → Further reading: PowerKram — DP-900 Azure Data Fundamentals →
Question 9 · Maintain the solution (25–30%)

A team wants to promote Fabric content through development, test, and production stages in a controlled, repeatable way, comparing differences between stages before publishing.

Which Fabric capability should they use?

  1. OneLake shortcuts
  2. A calculation group
  3. The Visual Query Editor
  4. Deployment pipelines
Show answer & explanation

Correct: D — deployment pipelines. Deployment pipelines promote content through development, test, and production workspaces with stage comparison, supporting a controlled, repeatable release process — exactly the lifecycle requirement.

Why not the others: shortcuts (A) reference external data; a calculation group (B) organizes DAX; the Visual Query Editor (C) builds queries. None manages staged promotion.

Source: Microsoft Learn — Deployment pipelines →
Question 10 · Maintain the solution (25–30%)

Before changing the schema of a warehouse table, an analytics engineer wants to see which downstream semantic models and reports depend on it, to gauge the blast radius.

Which Fabric capability provides this view?

  1. Sensitivity labels
  2. The OneLake catalog
  3. Impact analysis of downstream dependencies (lineage)
  4. A Spark notebook
Show answer & explanation

Correct: C — impact analysis of downstream dependencies (lineage). Impact analysis shows which downstream items (semantic models, reports, dataflows) depend on an item, letting you gauge the effect of a change before making it — exactly the requirement in the lifecycle skill.

Why not the others: sensitivity labels (A) classify data; the OneLake catalog (B) discovers items but does not map downstream dependency chains; a Spark notebook (D) runs code. None provides dependency impact analysis.

Source: Microsoft Learn — Microsoft Fabric overview →

Keep going: study & career resources

DP-600 is the analytics-engineering credential in the Microsoft Fabric family. Alongside it, the sibling Fabric data-engineering exam and the data-fundamentals base round out the track — and the data-engineer career path shows where these skills lead.

🔧 Sibling exam — DP-700 Fabric Data Engineer The data-engineering companion in the Fabric family: pipelines, Spark, Real-Time Intelligence, and data ingestion. Pairs naturally with DP-600’s analytics-modeling focus. Explore DP-700 → 💼 Career Hub — Data Engineer Where Fabric analytics and data-platform skills lead: analytics engineer, BI developer, and data engineer roles. Skills, tools, salary ranges, and the certification path. See the Data Engineer career path →
Deep dive: DP-600 format, the three current areas, the 2026 restructure, and study path

Exam format and scoring

DP-600 delivers roughly 40 to 60 questions in about 100 minutes, with a passing score of 700 out of 1000 (scaled). It includes multiple choice, multiple response, drag-and-drop, and one or more case studies. It is a scenario-driven exam: you are asked to choose the right Fabric component or design pattern for a business need, and Microsoft’s framing tends to reward the SaaS-native, lower-overhead Fabric option (Direct Lake, shortcuts, mirroring) over hand-rolled alternatives. The pace can be tight, especially when case studies run long. See the DP-700 Fabric Data Engineer path →

The 2026 restructure — three areas, not four

DP-600 was significantly restructured in 2026. The current edition (skills measured as of July 21, 2026) has three skill areas: Maintain a data analytics solution (25–30%), Prepare data (45–50%), and Implement and manage semantic models (25–30%). Many third-party guides still show the older four-domain outline with a separate “Plan, implement, and manage” area at 10–15% — that outline is out of date, and its governance and lifecycle content now lives inside “Maintain a data analytics solution.” If a resource lists a top-level “Plan” domain, it predates the restructure. See the DP-900 Data Fundamentals path →

Why preparing data dominates

At 45–50%, preparing data is nearly half the exam. It spans getting data (connections, OneLake catalog and Real-Time hub discovery, choosing a store, ingestion), transforming data (views, functions, stored procedures, star schema, denormalization, cleansing), and querying and analyzing with the Visual Query Editor, SQL, KQL, and DAX. If you are strong on Power BI modeling but lighter on data engineering and multi-language querying, this is where to invest. See the DP-700 Fabric Data Engineer path →

Semantic models and Direct Lake

The semantic-models area (25–30%) covers designing star-schema models, relationships (including bridge and many-to-many), advanced DAX (calculation groups, dynamic format strings, field parameters), and optimizing enterprise-scale models. Direct Lake is a recurring theme: it reads Delta tables directly from OneLake for near-import speed with live data, and the exam tests when to use Direct Lake on OneLake versus on the SQL analytics endpoint, plus fallback and refresh behavior. See the DP-900 Data Fundamentals path →

Realistic study path

Most candidates come to DP-600 with data-modeling and Power BI experience and need eight to twelve weeks of focused study built around mandatory hands-on labs in a Fabric environment. Work through the official Microsoft Learn DP-600 path, build a real solution (lakehouse with shortcuts, a warehouse with a star schema, a Direct Lake semantic model, RLS, and a deployment pipeline), then drill scenario questions in PowerKram’s Learn mode and finish with timed Exam-mode runs. Weight your time toward preparing data. See the DP-700 Fabric Data Engineer path →

Career outlook and next steps

The Fabric Analytics Engineer is a strategic, in-demand role as organizations consolidate analytics onto Microsoft Fabric. DP-600 pairs naturally with the sibling DP-700 Fabric Data Engineer exam for pipeline and real-time skills, and builds on DP-900 fundamentals. For BI developers and analysts moving into end-to-end analytics engineering, it is a strong differentiator. Career Hub — Data Engineer →

Microsoft DP-600 (Fabric Analytics Engineer Associate) exam FAQ

What are the current DP-600 skill areas and weights?
Per Microsoft’s current DP-600 study guide (skills measured as of July 21, 2026): Maintain a data analytics solution (25–30%), Prepare data (45–50%), and Implement and manage semantic models (25–30%). Preparing data is by far the heaviest area. Note that many third-party guides still show an older four-domain outline with a separate “Plan” area at 10–15% — that outline is out of date. Microsoft publishes these as ranges.
Is DP-600 still active?
Yes. DP-600 is a current, active exam and the single required exam for the Microsoft Certified: Fabric Analytics Engineer Associate certification. Its blueprint was updated (skills measured as of July 21, 2026) and there is no announced retirement date. It is the first and most-taken exam in the Microsoft Fabric family.
What is the difference between DP-600 and DP-700?
Both are Microsoft Fabric associate exams. DP-600 (Fabric Analytics Engineer) focuses on analytics engineering — preparing data, building and optimizing semantic models, and maintaining the solution. DP-700 (Fabric Data Engineer) focuses on data engineering — ingestion pipelines, Spark, and Real-Time Intelligence. Many professionals pursue both to cover the full Fabric workflow.
What is the DP-600 passing score and format?
A passing score of 700 out of 1000 (scaled), with roughly 40 to 60 questions in about 100 minutes, including one or more case studies. Formats include multiple choice, multiple response, and drag-and-drop. There is no penalty for incorrect answers.
Does the certification expire?
Yes. Microsoft associate certifications like Fabric Analytics Engineer Associate are valid for one year and must be renewed. Renewal is free through an online assessment on Microsoft Learn during the six months before the certification expires.

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