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Azure DP-700 Cheat Sheet

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Manage Analytics Solution

  • 30-35% of exam
  • Workspace Settings Lifecycle Security Orchestration Security Picker

Ingest + Transform Data

  • 30-35% of exam
  • Stores + Patterns Batch Tools Streaming Tools Data Quality Storage Picker

Monitor + Optimize

  • 30-35% of exam
  • Monitor + Alerts Error Triage Performance Tuning Query + Spark Troubleshooting Picker

Quick Facts

  • Exam DP-700
  • Credential Fabric Data Engineer
  • Time 100 min
  • Pass 700/1000
  • Level Intermediate
  • Product Microsoft Fabric
  • Role Data Engineer
  • Blueprint April 20 2026

Domain Balance MA

  • Manage, Ingest, Monitor split evenly
  • Manage: secure Ingest: load Monitor: tune

Pipeline vs Notebook MA

Pipeline

  • Orchestrates activities
  • Copy data
  • Schedules runs

Notebook

  • Transforms data
  • Spark session
  • Code execution
  • Coordinate vs compute

Security Picker MA

  • Workspace administration → Admin ( Control plane )
  • Create content → Contributor ( Workspace role )
  • Read workspace only → Viewer ( No write )
  • Secure lake data → OneLake roles ( Data plane )
  • Filter rows → RLS ( User context )
  • Mask values → Dynamic masking ( Sensitive data )

Workspace Settings MA

  • Spark pool Workspace compute default
  • Starter pools Faster session startup
  • High concurrency Shared notebook session
  • Resource profile Read/write tuning preset
  • Item compute Per-item Spark sizing
  • Domain Business grouping
  • OneLake cache Shortcut read acceleration
  • Dataflow scale Gen2 compute setting

Security Plane MA

  • Workspace acts; OneLake reads
  • Workspace: actions Item: sharing OneLake: data RLS: rows

Workspace vs OneLake Roles MA

Workspace role

  • Control plane
  • Item visibility
  • Content actions

OneLake role

  • Data plane
  • Folder/table access
  • Viewer data access
  • Actions vs data

Lifecycle MA

  • Git integration Branch-based versioning
  • Azure DevOps Supported Git provider
  • GitHub Supported Git provider
  • Database project Warehouse schema source
  • Deployment pipeline Dev-test-prod promotion
  • Deployment rules Stage-specific bindings
  • Credentials Not copied
  • Data refresh Required after deploy

Security + Governance MA

  • Admin Full workspace control
  • Member Manage content/users
  • Contributor Create/edit items
  • Viewer Read workspace content
  • Item sharing Item-level access
  • OneLake roles Data-plane access
  • DefaultReader ReadAll data access
  • Sensitivity label Information protection
  • Endorsement Trusted content signal
  • Audit logs Activity trail

Access Controls MA

  • RLS Row filter
  • CLS Column filter
  • OLS Object hiding
  • Folder security Path-level access
  • File security Object-level access
  • Dynamic masking Sensitive value masking
  • Entra ID Identity provider
  • Service principal App identity

Orchestration MA

  • Pipeline Activity orchestration
  • Dataflow Gen2 Low-code transformation
  • Notebook Code transformation
  • Schedule Time-based trigger
  • Event trigger Event-based run
  • Parameter Runtime input
  • Expression Dynamic value
  • Notebook activity Pipeline-called code

Tool Stack IN

  • Flow, Pipe, Note, SQL, KQL
  • Flow: low-code Pipe: orchestrate Note: Spark SQL/KQL: query

Lakehouse vs Warehouse IN

Lakehouse

  • Delta files
  • Spark friendly
  • Flexible schema

Warehouse

  • Relational SQL
  • T-SQL serving
  • Structured modeling
  • Files vs SQL

Transformation Picker IN

  • Low-code shaping → Dataflow Gen2 ( Power Query )
  • Custom Spark logic → Notebook ( PySpark )
  • Warehouse SQL → T-SQL ( Relational )
  • Eventhouse query → KQL ( Real-time )
  • Move then transform → Pipeline ( Activities )
  • Reusable code job → Spark job ( Batch )

Stores + Patterns IN

  • Lakehouse Delta lake analytics
  • Warehouse Relational SQL analytics
  • Eventhouse Real-time KQL store
  • KQL database Time-series analytics
  • Semantic model BI consumption layer
  • Full load Reload everything
  • Incremental load Load changes only
  • Streaming load Continuous event ingestion

Streaming Path IN

  • Events flow, land, query, window
  • Eventstream: flow Eventhouse: land KQL: query Window: time

Dataflow vs Notebook IN

Dataflow Gen2

  • Low-code
  • Power Query
  • Many connectors

Notebook

  • Code-first
  • PySpark/Scala
  • Custom logic
  • Low-code vs code

Storage Picker IN

  • Delta analytics → Lakehouse ( Files/tables )
  • SQL serving → Warehouse ( Relational )
  • Telemetry analytics → Eventhouse ( KQL )
  • External data → Shortcut ( No copy )
  • Operational replica → Mirroring ( Copied changes )
  • Report layer → Semantic model ( BI )

Batch Tools IN

  • Copy activity Move source data
  • Dataflow Gen2 Power Query shaping
  • Notebook PySpark/custom code
  • T-SQL Warehouse transformations
  • KQL Eventhouse transformations
  • Shortcut Virtual data reference
  • Mirroring Near-real-time replica
  • Fast copy Dataflow ingestion boost

Shortcut vs Mirroring IN

Shortcut

  • Virtual pointer
  • No copy
  • Target permissions

Mirroring

  • Replicated data
  • Source changes
  • Fabric copy
  • Reference vs replicate

Transforms + Modeling IN

  • Denormalize Flatten for analytics
  • Fact table Measures/events
  • Dimension table Descriptive context
  • Star schema Facts plus dimensions
  • Aggregate Summarize groups
  • Surrogate key Warehouse identifier
  • Late dimension Fact arrives first
  • Inferred member Temporary dimension row

Eventstream vs Eventhouse IN

Eventstream

  • Ingest events
  • Route/process
  • Stream pipeline

Eventhouse

  • Store events
  • KQL query
  • Real-time analytics
  • Flow vs store

Data Quality IN

  • Duplicate rows Deduplicate by key
  • Missing data Impute or reject
  • Late events Handle watermark lag
  • Schema drift Source shape changed
  • Checkpoint Streaming resume state
  • Watermark Late-data boundary
  • CDC Change data capture
  • Idempotent load Safe rerun

Full vs Incremental IN

Full load

  • Reload all
  • Simple logic
  • Longer window

Incremental

  • Changed rows
  • Needs watermark
  • Shorter window
  • All vs changes

Streaming Tools IN

  • Eventstream Event routing/processing
  • Eventhouse Real-time analytics store
  • Native table KQL-owned storage
  • Shortcut table Referenced OneLake data
  • Query acceleration Shortcut query boost
  • Spark streaming Micro-batch processing
  • Window function Time-bucket computation
  • KQL Streaming query language

Tune Order OP

  • Prune, compact, cache, scale
  • Prune scans Compact files Cache hot Scale compute

OPTIMIZE vs V-Order OP

OPTIMIZE

  • Compacts files
  • Delta maintenance
  • Write cleanup

V-Order

  • Read layout
  • Parquet optimization
  • Power BI speed
  • Compact vs layout

Troubleshooting Picker OP

  • Pipeline failed → Run history ( Activities )
  • Dataflow failed → Refresh details ( Mashup )
  • Notebook failed → Spark logs ( Driver )
  • Shortcut fails → Credentials ( Target access )
  • Model stale → Refresh history ( Semantic )
  • Capacity saturated → Metrics app ( Utilization )

Monitor + Alerts OP

  • Monitoring hub Fabric run status
  • Pipeline run Activity execution
  • Dataflow refresh Gen2 execution
  • Notebook run Spark execution
  • Semantic refresh Model data update
  • Alerts Condition notifications
  • Capacity metrics Resource utilization
  • Audit events User activity

Error Triage OP

  • Pipeline error Activity failure
  • Dataflow error Mashup/load issue
  • Notebook error Spark/code failure
  • Eventhouse error KQL store issue
  • Eventstream error Event route issue
  • T-SQL error SQL statement issue
  • Shortcut error Target/credential issue
  • Refresh error Semantic update failure

Performance Tuning OP

  • OPTIMIZE Compact Delta files
  • V-Order Parquet read optimization
  • Partitioning Prune scanned data
  • Z-order Cluster related values
  • Statistics Query planning metadata
  • Indexes Warehouse access paths
  • Materialization Precomputed results
  • Query acceleration KQL shortcut speed

Query + Spark OP

  • Predicate pushdown Filter near source
  • Projection pruning Read needed columns
  • Broadcast join Small table join
  • Shuffle Network data exchange
  • Skew Uneven partition load
  • Caching Reuse hot data
  • Concurrency Parallel user pressure
  • Explain plan Query execution shape

Common Traps

Workspace vs data access

  • Viewer sees item ≠ OneLake role reads data

Deployment copies metadata

  • Definitions promote ≠ Data stays behind

Shortcut vs copy

  • Shortcut references target ≠ Mirroring replicates changes

Pipeline vs transform

  • Pipeline orchestrates ≠ Notebook transforms

Low-code vs custom

  • Dataflow uses Power Query ≠ Notebook uses code

Streaming resume

  • Checkpoint preserves state ≠ Watermark bounds lateness

Telemetry store

  • Eventstream routes events ≠ Eventhouse stores events

Optimization scope

  • OPTIMIZE compacts files ≠ V-Order improves reads

Last Minute

  • 1. Weights: three equal 30-35 bands
  • 2. Know SQL, PySpark, KQL
  • 3. Workspace roles govern actions
  • 4. OneLake roles govern data
  • 5. Deployment pipelines skip data
  • 6. Credentials remain stage-specific
  • 7. Shortcuts reference; mirroring copies
  • 8. Dataflow low-code; notebooks code
  • 9. Pipeline orchestrates; notebook computes
  • 10. Eventstream flows; eventhouse stores
  • 11. Incremental loads need watermarks
  • 12. OPTIMIZE compacts Delta files

Azure DP-700

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