DP-600 sample questions: 5 free questions
Five original scenario questions in the style of the DP-600 skills outline: governance, choosing a data store, T-SQL, Direct Lake and DAX. Read the requirement in each scenario first, then eliminate options that break it. Click “Show answer” after you commit. There is a longer set in the free practice test.
Maintain a data analytics solution
Question 1. A group of finance analysts must query the curated tables in one lakehouse through its SQL analytics endpoint. They must not edit anything and must not see the other items in the workspace. What should you do?
- A. Add the analysts to the workspace as Contributors
- B. Share only the lakehouse with them and grant read access to the SQL analytics endpoint data
- C. Add the analysts to the workspace as Members
- D. Add the analysts to the workspace as Viewers
Show answer
Answer: B
Sharing the single lakehouse and granting read access to its SQL endpoint data gives item-level access without exposing the rest of the workspace. Contributor and Member roles allow writing and apply to every item in the workspace, and a Viewer role also exposes the workspace’s other items, so none of those meet the requirement of seeing only one item.
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Prepare data
Question 2. Millions of device readings arrive every minute. Engineers need to run time-series filtering and aggregation on the most recent hours of data, using a query language built for that. Which Fabric item fits best?
- A. A warehouse
- B. A lakehouse
- C. An eventhouse
- D. A semantic model
Show answer
Answer: C
An eventhouse is designed for streaming and time-series data and is queried with KQL. A warehouse is a T-SQL engine for structured analytical tables, a lakehouse is best for Spark-based files and Delta tables, and a semantic model is a modelling layer for reporting, not a store for ingesting telemetry.
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Question 3. A nightly process must update rows in several tables and roll everything back if any step fails, using T-SQL. The result will be the source for a star schema. Which item should you use?
- A. A warehouse
- B. The SQL analytics endpoint of a lakehouse
- C. An eventhouse
- D. A Dataflow Gen2 with no destination
Show answer
Answer: A
A Fabric warehouse supports full T-SQL data modification and multi-table transactions. The SQL analytics endpoint of a lakehouse is read-only for tables, so UPDATE statements are not available there. An eventhouse uses KQL, and a dataflow does not give you transactional T-SQL across tables.
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Implement and manage semantic models
Question 4. A Direct Lake semantic model must never silently switch to DirectQuery when a query cannot be served from Delta files in OneLake. Which design should you choose?
- A. Import mode with a nightly refresh
- B. Direct Lake on the SQL analytics endpoint with the default fallback
- C. DirectQuery against the warehouse
- D. Direct Lake on OneLake
Show answer
Answer: D
Direct Lake on OneLake reads Delta tables directly and does not fall back to DirectQuery, so failures are visible rather than silent. Direct Lake on the SQL analytics endpoint can fall back to DirectQuery by default. Import mode is a different storage mode with scheduled refresh, and DirectQuery is itself what the requirement wants to avoid.
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Question 5. Dozens of measures need the same time calculations, such as year to date and prior year, without writing each variant by hand. What should you use?
- A. A field parameter with one field per measure
- B. A copy of every measure for each time calculation
- C. A calculation group with calculation items
- D. A calculated column in the date table
Show answer
Answer: C
A calculation group holds calculation items that use SELECTEDMEASURE to apply one pattern to any measure. Field parameters let users switch which fields appear in a visual, they do not rewrite measure logic. Copying measures multiplies the maintenance work, and a calculated column is evaluated per row at refresh time, not per query context.
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After checking your answers, see the exam format for the domain weights, then work through the domain pages: maintaining a solution, preparing data and semantic models.