PL-300 · Associate
PL-300 Cheat Sheet 2026: Quick Reference
Every objective condensed to a table, plus the storage-mode and calculated-column-vs-measure decisions the exam tests directly.
Exam Snapshot
Questions: 40-60
Duration: 65 minutes
Passing score: 700 / 1000
Cost: $165 USD
Level: Associate
Last updated: 2026-04-20
We built this from Microsoft's published PL-300 skills outline, not a generic template — every reference link below points to the current Microsoft Learn page, verified before this went live.
1. Prepare the Data (25-30%)
| Concept | Area | Quick note |
|---|---|---|
| Connect to data / shared semantic model | Get & connect | Source identification, first step |
| Data source settings (credentials, privacy) | Get & connect | Connection configuration |
| DirectLake vs. DirectQuery vs. Import | Get & connect | See the storage-mode decision table below |
| Parameters | Get & connect | Reusable, editable connection values |
| Data profiling (statistics, column properties) | Profile & clean | Evaluating quality before transforming |
| Resolve inconsistencies, nulls, quality issues | Profile & clean | Data cleaning proper |
| Resolve data import errors | Profile & clean | Troubleshooting load failures |
| Column data types | Transform & load | Basic typing, affects downstream DAX |
| Group/aggregate, pivot/unpivot/transpose | Transform & load | Reshaping data into the right layout |
| Semi-structured to table conversion | Transform & load | Flattening nested data (e.g. JSON) |
| Fact tables and dimension tables | Transform & load | Star schema basics |
| Reference vs. duplicate queries | Transform & load | Two different query-reuse patterns with different impact |
| Merge vs. append queries | Transform & load | See the decision table below |
| Keys for relationships | Transform & load | What makes a relationship possible at all |
2. Model the Data (25-30%)
| Concept | Area | Quick note |
|---|---|---|
| Table/column properties | Data model | Basic model configuration |
| Role-playing dimensions | Data model | One dimension used in multiple roles (e.g. a date table used three ways) |
| Relationship cardinality & cross-filter direction | Data model | One-to-many is the norm in a star schema |
| Common date table | Data model | Required for time intelligence to work correctly |
| Calculated column vs. table vs. measure | Data model | See the decision table below |
| CALCULATE function | DAX | The single most important DAX concept on the exam |
| Time intelligence measures | DAX | Period-over-period calculations |
| Basic statistical functions | DAX | Aggregation beyond SUM/AVERAGE |
| Semi-additive measures | DAX | Aggregates that don't sum across every dimension (e.g. balances) |
| Quick measures | DAX | UI-generated DAX from a dialog, not hand-written |
| Calculation groups | DAX | Reusable calculation logic applied across measures |
| Performance optimization | Model tuning | Remove unneeded rows/columns, use Performance Analyzer, reduce granularity |
3. Visualize and Analyze the Data (25-30%)
| Concept | Area | Quick note |
|---|---|---|
| Visual selection & formatting | Reports | Matching the visual type to the data |
| Copilot narrative visual & page suggestions | Reports | AI-assisted report authoring |
| Themes & conditional formatting | Reports | Visual styling layer |
| Slicing & filtering | Reports | Baseline interactivity |
| Paginated reports | Reports | When a print-perfect fixed layout matters |
| Visual calculations (DAX) | Reports | Calculations scoped to a single visual |
| Bookmarks, custom tooltips, visual interactions | Storytelling | Advanced report UX controls |
| Navigation, sync slicers, Selection pane | Storytelling | Structuring a multi-page report |
| Drillthrough | Storytelling | Page-, filter-, and button-based drill navigation |
| Mobile design, accessibility, personalization | Storytelling | Non-desktop and inclusive design |
| Automatic page refresh | Storytelling | Keeping visuals current without manual action |
| Analyze feature, grouping/binning/clustering | Patterns | Built-in exploration tools |
| AI visuals, forecasting, outlier detection | Patterns | Analytical visual types |
| Copilot semantic model summary | Patterns | AI-generated summary of the underlying model |
4. Manage and Secure Power BI (15-20%)
| Concept | Area | Quick note |
|---|---|---|
| Workspace creation & config | Workspaces | The container for all published content |
| App configuration & update | Workspaces | The consumption layer built on a workspace |
| Publish, import, update items | Workspaces | Content lifecycle actions |
| Dashboards | Workspaces | Pinned-tile summary views, distinct from reports |
| Distribution method choice | Workspaces | App vs. workspace access vs. direct sharing |
| Subscriptions & data alerts | Workspaces | Automated delivery and threshold notifications |
| Promote/certify content | Workspaces | Trust signals for shared content |
| Gateway requirement | Workspaces | Needed for on-premises data access |
| Scheduled refresh | Workspaces | Keeping imported data current |
| Workspace roles & item-level access | Security | Who can do what, at two different levels |
| Semantic model access | Security | A distinct access layer from workspace roles |
| Row-level security (RLS) | Security | See the reference note below |
| Sensitivity labels | Security | Data classification, not an access control by itself |
Storage Mode Quick Decision
| Need | Use this |
|---|---|
| Fast performance, full DAX capability, a refresh schedule is acceptable | Import |
| Real-time data, source too large to import, limited DAX/transforms acceptable | DirectQuery |
| Near-real-time performance without a full import, built on Fabric/OneLake | DirectLake |
| Some tables need Import speed, others need DirectQuery freshness, same model | Dual |
Calculated Column vs. Table vs. Measure
| Need | Use this |
|---|---|
| A value that needs to be sliced or filtered on, computed row-by-row at refresh | Calculated column |
| A reusable virtual table built for a specific analytical purpose | Calculated table |
| An aggregation computed dynamically in the current filter context | Measure |
PL-300 Quick Answers
How is this cheat sheet different from the PL-300 study guide?
The study guide explains the most-tested DAX and Power Query topics in narrative form with a 4-week plan. This page is tables only, for a final scan before the exam.
What's the difference between DirectQuery, Import, and DirectLake?
Import is fast with full DAX but needs a refresh. DirectQuery is live but limited. DirectLake gives near-real-time performance on Fabric/OneLake. Dual mixes both in one model.
What's the difference between a calculated column and a measure?
A calculated column is stored and computed at refresh — use it to slice/filter. A measure is computed dynamically in the current filter context — use it for aggregations.
Can I print this PL-300 cheat sheet?
Yes — every table here is plain HTML, so a browser print or "print to PDF" renders cleanly.
MSCertQuiz sells practice-exam access for PL-300 and other Microsoft certifications; this cheat sheet is written by the same team that builds those questions.
More PL-300 Resources
PL-300 Study Guide
The 4-week plan behind this quick reference.
Free PL-300 Practice Questions
Scenario questions with detailed explanations.
Is PL-300 Worth It?
An honest look at the ROI of this certification.
PL-300 Certification Overview
Full exam details and the complete 500-question practice bank.
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