Prepare Data
25-30%of exam
Model Data
25-30%of exam
Visualize + Analyze
25-30%of exam
ReportsBookmarksAI VisualsCopilotAccessibility
Manage + Secure
15-20%of exam
WorkspacesAppsRefreshRLSSensitivity Labels
Quick Facts
- Exam
- PL-300
- Credential
- Data Analyst
- Time
- 100 min
- Pass
- 700/1000
- Product
- Power BI
- Renewal
- 12 months
- Blueprint
- Apr 20 2026
Mode Memory
Import caches; DirectQuery asks source
Import: fastDirectQuery: freshDirect Lake: Fabric
Import vs DirectQuery
Import
- Cached data
- Fast visuals
- Scheduled refresh
DirectQuery
- Live source
- Source load
- Query limits
Speed vs freshness
Mode Picker
- Need fastest visuals→Import
- Need live source→DirectQuery
- Fabric lakehouse→Direct Lake
- Mixed mode needed→Composite model
- Large history→Incremental refresh
- On-prem source→Gateway
Data Sources
- SQL Server
- Relational source
- Excel
- File source
- SharePoint
- Cloud files
- Dataflow
- Reusable ETL
- Dataverse
- Business data
- Semantic model
- Shared model
- Gateway
- On-prem bridge
DirectQuery vs Direct Lake
DirectQuery
- Queries source
- Many sources
- SQL generated
Direct Lake
- Fabric lake
- Delta tables
- No import
Source query vs lake
Transform Picker
- Combine columns→Merge queries
- Stack same schema→Append queries
- Wide repeated columns→Unpivot
- Rows become columns→Pivot
- Shared base query→Reference
- Independent branch→Duplicate
Power Query
- M
- Query language
- Applied steps
- Transform history
- Merge
- Join columns
- Append
- Stack rows
- Pivot
- Rows to columns
- Unpivot
- Columns to rows
- Reference
- Dependent query
- Duplicate
- Independent copy
Merge vs Append
Merge
- Adds columns
- Join keys
- Lookup style
Append
- Adds rows
- Same schema
- Union style
Columns vs rows
Storage Modes
- Import
- In-memory cache
- DirectQuery
- Source query
- Direct Lake
- Lakehouse tables
- Dual
- Import or DirectQuery
- Composite
- Mixed sources
- Incremental refresh
- Partitioned refresh
- Query folding
- Source executes
Model Flow
Facts measure; dimensions filter
Facts: eventsDimensions: attributesStar: preferred
Measure vs Column
Measure
- Aggregates
- Filter context
- Visual time
Column
- Row value
- Stored data
- Model size
Dynamic vs stored
DAX Picker
- Aggregate at visual→Measure
- Store row value→Calculated column
- Change filter context→CALCULATE
- Iterate rows→X function
- Avoid divide errors→DIVIDE
- Time shift→DATEADD
Model Design
- Fact table
- Events/measures
- Dimension table
- Descriptive attributes
- Star schema
- Facts plus dimensions
- Cardinality
- Relationship shape
- Cross-filter
- Filter direction
- Date table
- Time intelligence
- Hierarchy
- Drill path
- Role-playing
- Repeated dimension
DAX Context
Measures live in filter context
Row contextFilter contextCALCULATE changes
DAX
- Measure
- Query-time calc
- Column
- Row-time calc
- CALCULATE
- Modify filters
- SUMX
- Row iterator
- ALL
- Remove filters
- RELATED
- Lookup related
- DATEADD
- Shift dates
- DIVIDE
- Safe division
Performance
- Cardinality
- Distinct values
- Granularity
- Detail level
- Aggregations
- Precomputed summaries
- Performance Analyzer
- Visual timings
- DAX query view
- Query testing
- Unused columns
- Remove them
- Bi-directional
- Use sparingly
Visuals
- Bar chart
- Category compare
- Line chart
- Trend over time
- Card
- Single KPI
- Matrix
- Grouped table
- Slicer
- User filter
- Tooltip
- Hover detail
- Theme
- Report styling
Analysis
- Analyze
- Explain increase
- Key influencers
- Driver analysis
- Decomposition tree
- Drill drivers
- Q&A
- Natural language
- Forecast
- Future trend
- Outliers
- Unusual points
- Copilot
- Narrative/report help
RLS vs OLS
RLS
- Filters rows
- User-specific data
- Role rules
OLS
- Hides objects
- Tables/columns
- Metadata security
Rows vs objects
Workspace Assets
- Workspace
- Collaboration container
- App
- Packaged distribution
- Report
- Interactive pages
- Dashboard
- Pinned tiles
- Semantic model
- Dataset model
- Subscription
- Email schedule
- Alert
- Threshold notification
Workspace vs App
Workspace
- Build content
- Role access
- Collaboration
App
- Consume content
- Packaged view
- Audience targeting
Author vs distribute
Security
- Viewer
- Read content
- Contributor
- Edit content
- Member
- Publish app
- Admin
- Full workspace
- RLS
- Row filtering
- OLS
- Object hiding
- Sensitivity label
- Data classification
- Endorsement
- Promote/certify
Common Traps
Measure vs column
Measure aggregates ≠ Column stores rows
Merge vs append
Merge adds columns ≠ Append adds rows
RLS vs OLS
RLS filters rows ≠ OLS hides objects
Workspace vs app
Workspace authors ≠ App distributes
DirectQuery tradeoff
Fresh data ≠ Slower visuals
Gateway need
On-prem refresh ≠ Cloud bridge
Last Minute
- 1.Weights: 25-30 / 25-30 / 25-30 / 15-20
- 2.Power Query = prepare data
- 3.Star schema beats snowflake
- 4.Measures aggregate at query time
- 5.Columns increase model size
- 6.Merge columns; append rows
- 7.Import fast; DirectQuery fresh
- 8.RLS rows; OLS objects
- 9.Workspace builds; app distributes
- 10.Gateway bridges on-prem sources
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