Browse Certification Practice Tests by Exam Family

Microsoft DP-600 Cheat Sheet: Fabric Analytics

May 1, 2026

Review Microsoft Fabric Analytics Engineer Associate (DP-600) semantic models, lakehouses, warehouses, DAX, governance, optimization, and analytics-engineering traps before using the DP-600 practice page.

On this page

DP-600 is an analytics-engineering route inside Microsoft Fabric. Use this cheat sheet to review semantic model, data preparation, security, performance, and business-facing analytics decisions before trying the DP-600 sample questions.

Use this with practice. Review the Fabric analytics checklist, then open the DP-600 page for sample questions, current-exam notes, and related data-platform practice paths.

Open DP-600 practice page Compare Microsoft Fabric routes

Exam snapshot

FieldDetail
IssuerMicrosoft
Certification nameFabric Analytics Engineer Associate
Exam codeDP-600
Product familyMicrosoft Fabric
Status in IT MasterySample questions with Notify me form

Topic map

AreaWhat to knowCommon trap
Analytics solution maintenanceWorkspace governance, access, deployment, lineage, endorsement, and lifecycle managementTreating a report fix as the only governance control
Data preparationLakehouse, warehouse, SQL, KQL, transformations, and reusable data assetsModeling visuals before validating source and transformation logic
Semantic modelsRelationships, measures, DAX, filter context, calculation groups, and performanceFixing report visuals when the metric definition is wrong

Must-know distinctions

DistinctionHow to decide
Lakehouse vs warehouseLakehouses fit flexible file/table analytics; warehouses fit relational SQL analytics and warehouse patterns.
Semantic model vs reportThe semantic model defines metrics and relationships; reports visualize those definitions.
Measure vs calculated columnMeasures calculate at query time under filter context; calculated columns persist row-level values.
Import vs Direct Lake or live accessChoose based on performance, freshness, model behavior, and operational constraints.
Workspace role vs item permissionWorkspace roles affect broad collaboration; item permissions control specific assets.
DAX logic vs visual formattingDAX changes metric meaning; formatting changes only presentation.
Performance issue vs correctness issueSlow queries need optimization; wrong totals need model, relationship, or filter-context review.

High-yield checklist

  • Start with the business metric definition before changing model logic.
  • Confirm relationships, cardinality, cross-filter direction, and inactive relationships.
  • Use measures for context-sensitive metrics and calculated columns only when row-level persistence is appropriate.
  • Check security at the workspace, item, semantic model, and data-source levels.
  • Use deployment and lifecycle controls when analytics assets move across environments.
  • Optimize model size, query patterns, aggregations, and measure logic.
  • Validate data preparation before troubleshooting report visuals.
  • Keep certified or endorsed assets controlled so users trust the right semantic model.
  • Review refresh, incremental processing, and dependency failures when data is stale.
  • Use lineage and impact analysis before changing shared assets.

Common traps

  • Creating duplicate metrics in multiple reports instead of governing a shared semantic model.
  • Ignoring filter context when a DAX measure returns unexpected totals.
  • Granting broad workspace access when item-level or model-level permission is enough.
  • Optimizing visuals while the warehouse, lakehouse, or model query is the real bottleneck.
  • Treating Fabric as only Power BI and missing data-engineering assets in the workflow.
  • Changing a shared model without checking downstream reports and consumers.

Practice strategy

Use the DP-600 page to test whether your misses come from modeling, data preparation, governance, or performance. If semantic-model misses dominate, drill relationships, measures, and filter context before returning to mixed Fabric questions.

Official source

Revised on Monday, May 25, 2026