Review the Microsoft Power BI Data Analyst (PL-300) scope, data preparation, modeling, DAX, visualization, report design, deployment, governance, and analytics traps before practicing.
PL-300 is a Power BI data analyst exam. Use this cheat sheet to keep analytics work tied to clean data, sound models, DAX logic, useful visuals, deployment, governance, and performance.
Use this with practice. Review the Power BI analyst checkpoints, then return to the PL-300 page for sample questions and update tracking.
| Field | Detail |
|---|---|
| Issuer | Microsoft |
| Certification lane | Power BI Data Analyst |
| Exam code | PL-300 |
| Main scope | Data preparation, modeling, DAX, visualization, deployment, governance, and analytics performance |
| IT Mastery status | Sample questions available |
| Area | What to know | Common trap |
|---|---|---|
| Data preparation | Cleaning, shaping, data types, missing values, transformations, and refresh | Building visuals on dirty data |
| Data modeling | Star schema, relationships, cardinality, tables, columns, measures, and performance | Using one wide flat table for every report |
| DAX | Measures, filter context, row context, time intelligence, and calculations | Writing calculated columns when a measure is needed |
| Visualization | Chart choice, accessibility, layout, drillthrough, filters, and storytelling | Using visuals that obscure the business question |
| Deployment | Workspaces, datasets, reports, apps, refresh, and permissions | Publishing without ownership and refresh planning |
| Governance | Sensitivity, endorsement, security, lineage, and content lifecycle | Treating a report as trusted without data governance |
| Distinction | How to decide |
|---|---|
| Measure vs calculated column | Measures calculate at query time; calculated columns store row-level results. |
| Row context vs filter context | Row context evaluates row by row; filter context comes from report filters and relationships. |
| Report vs dashboard | Reports contain pages and interactions; dashboards summarize pinned visuals. |
| Dataset/model vs visualization | The model defines logic; visuals present it. |
| Import vs DirectQuery | Import stores data in the model; DirectQuery queries the source at interaction time. |
For PL-300 misses, name the analytics layer first: data prep, model, DAX, visual, deployment, security, or governance. Then decide whether the issue affects correctness, performance, usability, or trust.