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Cheat sheet

Azure DP-600 Cheat Sheet

Practice this exam

Maintain Analytics Solution

  • 25-30% of exam
  • Security Lifecycle Version Control Reusable Assets Security Picker

Prepare Data

  • 45-50% of exam
  • Get Data Store Choice Transform Data Query Tools Store Picker

Manage Semantic Models

  • 25-30% of exam
  • Model Design DAX Patterns Direct Lake Optimization Model Picker

Quick Facts

  • Exam DP-600
  • Credential Fabric Analytics Engineer
  • Time 100 min
  • Pass 700/1000
  • Level Intermediate
  • Product Microsoft Fabric
  • Focus Semantic + data
  • Blueprint Apr 20 2026

Access Layers MA

  • Workspace, item, file, row, column, object
  • Workspace Item File Row Column Object

RLS vs CLS MA

RLS

  • Filters rows
  • User scoped

CLS

  • Hides columns
  • Schema scoped
  • Rows need RLS

Security Picker MA

  • Need workspace authors → Contributor ( Least privilege )
  • Need membership control → Member ( Broad role )
  • Need settings control → Admin
  • Share one item → Item access
  • Filter rows → RLS
  • Hide columns → CLS
  • Hide tables → OLS
  • Protect lake folders → File security

Security + Governance MA

  • Admin Full workspace control
  • Member Manage users/content
  • Contributor Create/edit content
  • Viewer Read workspace content
  • Item access Single-item permission
  • RLS Row filtering
  • CLS Column filtering
  • OLS Object hiding
  • File security Path-level access
  • Sensitivity label Information classification
  • Endorsement Trusted content signal
  • Lineage Dependency map

Item vs Workspace MA

Item access

  • Single asset
  • Least exposure

Workspace role

  • All workspace items
  • Broader exposure
  • Share narrowly first

Lifecycle MA

  • Git integration Workspace versioning
  • Branch Isolated changes
  • Commit Saved snapshot
  • Sync Align workspace/Git
  • PBIP Power BI project
  • Pipeline Dev-test-prod promotion
  • Deployment rule Stage binding override
  • Impact analysis Downstream dependencies
  • XMLA endpoint Model management endpoint
  • Workspace settings Environment configuration

Reusable Assets MA

  • PBIT Report template
  • PBIDS Data source shortcut
  • Shared model Reusable semantic layer
  • Certified model Approved enterprise source
  • Promoted item Recommended content
  • Template app Packaged solution
  • Parameter Environment input
  • Connection Reusable credential binding

Store Trio PR

  • Lake files, warehouse SQL, eventhouse KQL
  • Lakehouse Warehouse Eventhouse OneLake

Lakehouse vs Warehouse PR

Lakehouse

  • Delta files
  • Spark friendly

Warehouse

  • Relational SQL
  • T-SQL friendly
  • Pick by workload

Store Picker PR

  • Open Delta files → Lakehouse
  • Relational SQL analytics → Warehouse
  • Real-time telemetry → Eventhouse
  • Reuse external data → Shortcut
  • Low-code shaping → Dataflow Gen2
  • Custom Spark code → Notebook
  • Business semantic layer → Semantic model
  • Discover certified data → OneLake catalog
  • Streaming sources → Real-Time hub

Store Selection PR

  • Lakehouse Open Delta analytics
  • Warehouse Relational SQL analytics
  • Eventhouse Real-time KQL analytics
  • KQL database Telemetry/time-series store
  • OneLake Unified data lake
  • Shortcut Virtual data reference
  • Mirroring Near-real-time replica
  • Semantic model BI consumption layer
  • Real-Time hub Streaming discovery
  • OneLake catalog Data discovery

Eventhouse vs Warehouse PR

Eventhouse

  • Streaming telemetry
  • KQL queries

Warehouse

  • Structured tables
  • T-SQL queries
  • Telemetry favors KQL

Data Ingestion PR

  • Connection Source access binding
  • Copy activity Move data
  • Dataflow Gen2 Low-code shaping
  • Notebook Spark code
  • Pipeline Activity orchestration
  • Eventstream Event routing
  • Full load Reload all rows
  • Incremental load Load changes only
  • CDC Change capture
  • Gateway On-prem connectivity

Dataflow vs Notebook PR

Dataflow Gen2

  • Low-code Power Query
  • Visual transforms

Notebook

  • Code transforms
  • Spark control
  • Code needs notebooks

Transform Data PR

  • View Reusable query
  • Function Reusable logic
  • Stored procedure SQL routine
  • New column Enrichment field
  • New table Enrichment output
  • Denormalize Flatten structure
  • Aggregate Summarize rows
  • Merge Combine columns
  • Join Relate tables
  • Filter Keep rows
  • Type conversion Fix data types
  • Star schema Facts plus dimensions

Query Tools PR

  • Visual Query Graphical select/filter
  • SQL Relational querying
  • KQL Telemetry querying
  • DAX Model calculations
  • SELECT Choose columns
  • WHERE Filter rows
  • GROUP BY Aggregate groups
  • summarize KQL aggregate
  • CALCULATE Modify filter context
  • EVALUATE Return DAX table

Data Quality PR

  • Duplicate rows Deduplicate by key
  • Missing data Impute or reject
  • Null values Handle blanks
  • Bad types Convert columns
  • Schema drift Source changed
  • Outlier Suspicious value
  • Late data Delayed arrival
  • Surrogate key Warehouse identifier
  • Business key Source identifier
  • Validation rule Quality check

STAR Model SE

  • Facts measure, dimensions describe
  • Facts Dimensions Relationships Measures

Import vs Direct Lake SE

Import

  • Copied cache
  • Scheduled refresh

Direct Lake

  • Reads OneLake
  • Fast freshness
  • OneLake enables Direct Lake

Model Picker SE

  • Need import speed → Import
  • Need live source → DirectQuery
  • Need OneLake speed → Direct Lake
  • Mix source modes → Composite model
  • Huge model → Large format
  • Role-based rows → RLS
  • Reusable measure logic → Calc group
  • User dimension switch → Field parameter

Model Design SE

  • Import Cached model data
  • DirectQuery Live source queries
  • Direct Lake OneLake table reads
  • Composite model Mixed storage modes
  • Fact table Measures/events
  • Dimension table Descriptive context
  • Bridge table Many-to-many resolver
  • Relationship Table filter path
  • Cardinality Row matching shape
  • Filter direction Propagation path

DAX VAR SE

  • Store once, reuse often
  • VAR RETURN CALCULATE FILTER

Direct Lake vs DirectQuery SE

Direct Lake

  • Parquet reads
  • Fallback possible

DirectQuery

  • Source queries
  • Latency sensitive
  • Fallback behaves DirectQuery

DAX Patterns SE

  • Measure Dynamic calculation
  • Calculated column Stored row value
  • VAR Reusable expression
  • Iterator Row-by-row evaluation
  • FILTER Table filtering
  • WINDOW Window calculation
  • INFO Metadata function
  • Context transition Row becomes filter
  • Time intelligence Date-aware measures
  • DIVIDE Safe division

Calc Group vs Field Parameter SE

Calc group

  • Measure variants
  • Format logic

Field parameter

  • Field switching
  • User selection
  • Measures need calc groups

Semantic Features SE

  • Calc group Reusable measure variants
  • Calc item Group expression
  • Dynamic format Measure-specific format
  • Field parameter User field switch
  • Large format Large model storage
  • Aggregation table Pre-summarized data
  • Perspective Field subset
  • Hierarchy Drill path
  • Display folder Field organization
  • XMLA write External model changes

Optimization + Refresh SE

  • Fallback DirectQuery downgrade
  • Framing Direct Lake eligibility
  • Refresh Model data update
  • Incremental refresh Partitioned updates
  • Query reduction Fewer visual queries
  • Performance Analyzer Visual timing trace
  • DAX Studio Query performance tool
  • VertiPaq Import storage engine
  • Cardinality Compression pressure
  • Star schema Fast filter design

Common Traps

Viewer is not builder

  • Viewer reads ≠ Contributor builds

Sensitivity is not security

  • Label classifies ≠ Access controls

Endorsement is not deployment

  • Endorse trusts ≠ Pipeline promotes

Direct Lake is not import

  • Reads OneLake ≠ No copied cache

Warehouse is not eventhouse

  • T-SQL relational ≠ KQL telemetry

RLS is not CLS

  • Rows filtered ≠ Columns hidden

PBIT is not PBIDS

  • Template report ≠ Connection shortcut

Last Minute

  • 1. Map three domain weights
  • 2. Pick least privilege role
  • 3. Separate item from workspace
  • 4. Use RLS for rows
  • 5. Use CLS for columns
  • 6. Choose correct Fabric store
  • 7. Query with SQL KQL DAX
  • 8. Prepare star schemas
  • 9. Prefer variables in DAX
  • 10. Know Direct Lake fallback
  • 11. Review deployment pipeline rules
  • 12. Check lineage before changes

Azure DP-600

  • Practice Questions 200 questions Study Guide 22 topics Cheat Sheet 122 facts Flashcards 50 cards Videos 1 video Blogs 1 blog Free CE Podcast