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Fabric DP-600 Sample Questions with Explanations

April 25, 2026

Fabric DP-600 sample questions with explanations, traps, topic labels, and IT Mastery route links.

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These original sample questions are designed to help you check how the exam topics appear in decision-style prompts. They are not taken from the live exam.

Use these sample questions as a guided self-assessment for Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) topics such as semantic models, lakehouse and warehouse choices, Power BI modeling, Direct Lake behavior, dataflows, governance, performance, and deployment lifecycle. The prompts focus on analytics engineering decisions in Fabric.

Where these questions fit in the DP-600 guide

The sample set below is part of the Microsoft DP-600 guide path:

DP-600 analytics sample questions

Work through each prompt before opening the explanation. Strong DP-600 answers usually choose the model, storage, and governance pattern that makes analytics reliable for business users.

Question 1

Topic: Semantic model ownership

A company has many reports that define revenue differently. Executives want one governed revenue definition reused across reporting, with controlled changes and consistent measures. What should the analytics engineer prioritize?

  • A. Create separate calculated measures inside each report file.
  • B. Build and govern a shared semantic model with approved measures, documented relationships, and controlled deployment.
  • C. Ask each report author to rename their revenue field manually.
  • D. Export report visuals to spreadsheets and reconcile them once per quarter.

Best answer: B

Explanation: A shared semantic model centralizes business definitions and supports reuse. Governance and deployment control reduce measure drift across reports.

Why the other choices are weaker:

  • A repeats business logic and makes inconsistency likely.
  • C changes labels but not calculation rules.
  • D is manual reconciliation, not a governed analytics design.

What this tests: Using semantic models to standardize measures and reporting behavior.

Related topics: Semantic model; Measures; Governance; Deployment

Question 2

Topic: Lakehouse data for reporting

An analytics team stores curated sales tables in a Fabric lakehouse and wants reporting with minimal data movement. The model should use the curated tables directly where appropriate. Which approach best fits?

  • A. Copy the same tables manually into every report workspace.
  • B. Use a Fabric reporting pattern that connects the semantic model to the curated lakehouse tables and manages model relationships and measures centrally.
  • C. Export lakehouse tables to CSV files for report authors.
  • D. Store all measures only in visual-level calculations.

Best answer: B

Explanation: The strongest design keeps curated tables in Fabric and builds the semantic layer around them. That reduces copies and keeps relationships and measures governed.

Why the other choices are weaker:

  • A creates duplicated data and workspace drift.
  • C breaks the governed platform path and increases manual handling.
  • D hides core business logic inside individual visuals.

What this tests: Choosing a Fabric analytics pattern that minimizes data movement and centralizes model logic.

Related topics: Lakehouse; Semantic model; Reporting; Data movement

Question 3

Topic: Performance troubleshooting

A report page loads slowly after a new many-to-many relationship and several complex measures were added. Users report delays only on pages using those measures. What should the engineer investigate first?

  • A. The semantic model relationships, measure logic, filter context, and query patterns used by the slow visuals.
  • B. The color palette of the report theme.
  • C. The title text on the report page.
  • D. The user’s browser bookmarks.

Best answer: A

Explanation: The slowdown is tied to model and measure changes. Relationships, measure complexity, filter context, and visual query behavior are the first places to inspect.

Why the other choices are weaker:

  • B affects presentation, not query execution.
  • C is not connected to model performance.
  • D does not explain page-specific report latency.

What this tests: Troubleshooting analytics performance from the semantic model outward.

Related topics: Performance; Relationships; DAX; Filter context

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Revised on Sunday, May 10, 2026
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