Maintain Analytics Solution
25-30%of exam
Prepare Data
45-50%of exam
Manage Semantic Models
25-30%of exam
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
Workspace, item, file, row, column, object
WorkspaceItemFileRowColumnObject
RLS vs CLS
RLS
- Filters rows
- User scoped
CLS
- Hides columns
- Schema scoped
Rows need RLS
Security Picker
- 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
- 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
Item access
- Single asset
- Least exposure
Workspace role
- All workspace items
- Broader exposure
Share narrowly first
Lifecycle
- 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
- 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
Lake files, warehouse SQL, eventhouse KQL
LakehouseWarehouseEventhouseOneLake
Lakehouse vs Warehouse
Lakehouse
- Delta files
- Spark friendly
Warehouse
- Relational SQL
- T-SQL friendly
Pick by workload
Store Picker
- 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
- 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
Eventhouse
- Streaming telemetry
- KQL queries
Warehouse
- Structured tables
- T-SQL queries
Telemetry favors KQL
Data Ingestion
- 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
Dataflow Gen2
- Low-code Power Query
- Visual transforms
Notebook
- Code transforms
- Spark control
Code needs notebooks
Transform Data
- 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
- 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
- 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
Facts measure, dimensions describe
FactsDimensionsRelationshipsMeasures
Import vs Direct Lake
Import
- Copied cache
- Scheduled refresh
Direct Lake
- Reads OneLake
- Fast freshness
OneLake enables Direct Lake
Model Picker
- 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
- 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
Store once, reuse often
VARRETURNCALCULATEFILTER
Direct Lake vs DirectQuery
Direct Lake
- Parquet reads
- Fallback possible
DirectQuery
- Source queries
- Latency sensitive
Fallback behaves DirectQuery
DAX Patterns
- 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
Calc group
- Measure variants
- Format logic
Field parameter
- Field switching
- User selection
Measures need calc groups
Semantic Features
- 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
- 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
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