Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric earns the Microsoft Certified: Fabric Analytics Engineer Associate credential. It tests whether you can turn raw data into reusable analytics assets in Fabric: lakehouses, warehouses, notebooks, dataflows, pipelines and semantic models, using SQL, PySpark and DAX. Preparing and serving data is the largest area at 40–45%.
DP-600 sits between PL-300 (Power BI analyst) and DP-700 (Fabric data engineer). This guide follows the official study guide: four skill areas with weights, the DAX and SQL you need, a 6-week plan and 15 original scenario questions.
Quick facts (Microsoft Learn, November 2026)
| Item | Detail |
|---|---|
| Certification | Microsoft Certified: Fabric Analytics Engineer Associate (Intermediate) |
| Time | 100 minutes; proctored; may include interactive components |
| Passing score | 700 |
| Languages | English, Japanese, Chinese (Simplified), German, French, Spanish, Portuguese (Brazil) |
| Price | Based on the country where you take the exam (Pearson VUE) |
| Renewal | Every 12 months, with a free online assessment |
| Practice | Practice Assessment on AI Skills Navigator (sign-in required) |
Read the official certification page and DP-600 study guide before booking.
PL-300, DP-600 or DP-700?
| Exam | Role | Main tools |
|---|---|---|
| PL-300 | Power BI data analyst | Power Query, data modelling, DAX, reports |
| DP-600 | Fabric analytics engineer | Lakehouse, warehouse, semantic models, Direct Lake, DAX, SQL, PySpark |
| DP-700 | Fabric data engineer | Pipelines, Spark, Real-Time Intelligence, security and monitoring |
Skills measured and weights
| Skill area | Weight |
|---|---|
| Plan, implement and manage a solution for data analytics | 10–15% |
| Prepare and serve data | 40–45% |
| Implement and manage semantic models | 20–25% |
| Explore and analyse data | 20–25% |
1. Plan and manage the solution (10–15%)
- Plan: requirements, features, performance and capacity SKUs; Fabric admin portal settings; data gateway type; custom Power BI report themes.
- Manage: workspace and item-level access; data sharing for workspaces, warehouses and lakehouses; sensitivity labels; workspace settings; Fabric capacity.
- Lifecycle: workspace version control; Power BI Desktop projects (.pbip); deployment solutions; impact analysis of downstream dependencies; the XMLA endpoint; reusable .pbit, .pbids and shared semantic models.
2. Prepare and serve data (40–45%)
- Objects: ingest with a pipeline, dataflow or notebook; shortcuts; file partitioning in a lakehouse; views, functions and stored procedures; enrichment.
- Copy: choosing a copy method; adding stored procedures, notebooks and dataflows to pipelines; scheduling.
- Transform: cleansing; star schema with Type 1 and Type 2 slowly changing dimensions; bridge tables; denormalising; aggregating; joins; duplicates and nulls; type conversion in SQL or PySpark; filtering.
- Optimise: loading bottlenecks in dataflows, notebooks and SQL; Delta table file-size problems.
-- Type 2 SCD in a Fabric warehouse: close the old row, insert the new one
UPDATE dim_customer
SET is_current = 0, end_date = CAST(GETDATE() AS date)
FROM dim_customer d
JOIN stg_customer s ON s.customer_id = d.customer_id
WHERE d.is_current = 1 AND s.city <> d.city;
INSERT INTO dim_customer (customer_id, name, city, start_date, end_date, is_current)
SELECT s.customer_id, s.name, s.city, CAST(GETDATE() AS date), NULL, 1
FROM stg_customer s
LEFT JOIN dim_customer d ON d.customer_id = s.customer_id AND d.is_current = 1
WHERE d.customer_id IS NULL;3. Semantic models (20–25%)
- Design: storage mode, including Direct Lake; DAX Studio and Tabular Editor 2; star schema; bridge tables and many-to-many relationships; DAX variables, iterators, table filtering, windowing and information functions; calculation groups, dynamic format strings and field parameters; large format datasets; composite models with aggregations; dynamic row-level and object-level security.
- Optimise: query and visual performance; DAX Studio; Tabular Editor 2; incremental refresh.
-- DAX: sales vs same period last year, using variables
Sales YoY % =
VAR CurrentSales = [Total Sales]
VAR LastYear = CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))
RETURN DIVIDE(CurrentSales - LastYear, LastYear)
-- Dynamic row-level security: each manager sees only their region
[Region Manager Email] = USERPRINCIPALNAME()4. Explore and analyse (20–25%)
- Descriptive and diagnostic analytics; prescriptive and predictive analytics in visuals; data profiling.
- Querying a lakehouse or warehouse with SQL or the visual query editor; querying semantic models through the XMLA endpoint.
6-week study plan
| Week | Focus | Lab (Fabric trial) |
|---|---|---|
| 1 | Fabric workspaces, capacities, admin settings, lakehouse vs warehouse | Create a workspace, lakehouse and warehouse |
| 2 | Pipelines, dataflows Gen2, notebooks, shortcuts, scheduling | Load the same CSV three ways and compare |
| 3 | Star schema, SCD Type 1 and 2, bridge tables, cleansing in SQL and PySpark | Build a sales star schema with a Type 2 customer dimension |
| 4 | Semantic models: Direct Lake, relationships, DAX, calculation groups | Direct Lake model with YoY and running-total measures |
| 5 | RLS and OLS, composite models, aggregations, incremental refresh, DAX Studio, Tabular Editor | Add dynamic RLS and test it as two users |
| 6 | Git integration, .pbip, deployment pipelines, XMLA; Practice Assessment | Promote a model from dev to test; book the exam |
15 scenario questions with answers
These are original scenarios written for this guide, not exam questions.
1. Data in ADLS Gen2 must be used in a lakehouse without copying it. What do you create?
A shortcut.
2. A report must read Delta tables in OneLake with near import-mode speed and no scheduled refresh. Which storage mode?
Direct Lake.
3. Customer address history must be kept when a customer moves. Which dimension type?
Slowly changing dimension Type 2.
4. Students and courses have a many-to-many relationship. What do you add to the model?
A bridge table.
5. Many measures need the same YTD, QTD and MTD logic. What reduces duplication?
A calculation group.
6. Users want to switch a visual's measure with a slicer. What feature?
Field parameters.
7. Each sales manager must see only their own region's rows. What do you implement?
Dynamic row-level security using USERPRINCIPALNAME().
8. The salary column must be hidden from some users entirely. What do you use?
Object-level security.
9. A large fact table refresh takes hours, but only recent data changes. What helps?
Incremental refresh.
10. Which tool shows which DAX query in a visual is slow?
DAX Studio (with Performance Analyzer in Power BI Desktop).
11. A lakehouse table has thousands of tiny Parquet files and queries are slow. What do you fix?
Delta table file sizes, for example by running OPTIMIZE to compact files.
12. Model changes must be tracked in Git with readable files. What format?
A Power BI Desktop project (.pbip) with workspace Git integration.
13. Before changing a warehouse table, you must know which reports depend on it. What do you run?
Impact analysis of downstream dependencies.
14. An external tool must deploy changes to a published semantic model. Through what?
The XMLA endpoint.
15. A sensitive lakehouse must be labelled Confidential so the label follows exports. What do you apply?
A Microsoft Purview sensitivity label.
After DP-600
- Practise Microsoft Fabric interview questions and SQL interview questions.
- Add DP-700 for data engineering or compare with the Databricks Data Engineer Associate.
- See live data and BI jobs.
Cloud Soft Solutions in Ameerpet, Hyderabad teaches Power BI, Microsoft Fabric, SQL and data engineering with real-time projects. See the APEX program and all courses.
📞 Call +91 96660 19191 · WhatsApp · Flat No. 513, 5th Floor, Nilagiri Block, Aditya Enclave, Ameerpet, Hyderabad
Frequently Asked Questions
What certification does DP-600 earn?
Microsoft Certified: Fabric Analytics Engineer Associate.
How long is the DP-600 exam?
100 minutes. It is proctored and may include interactive components.
What is the passing score for DP-600?
700.
Which DP-600 skill area has the most weight?
Prepare and serve data, at 40–45%.
What is the difference between DP-600 and DP-700?
DP-600 focuses on analytics assets such as semantic models, warehouses and DAX. DP-700 focuses on data engineering: pipelines, Spark, real-time data and monitoring.
How often must DP-600 be renewed?
Every 12 months, with a free online renewal assessment on Microsoft Learn.
