- 🎯 Goal: Earn the Microsoft Certified: Fabric Analytics Engineer Associate badge
- 📅 Notes Version: 2026
- 🌐 Published site: 📘 DP-600 Study Notes
- ✍️ Author: Marco Grimaldi
- 🔗 Related repos: 📘 AZ-305 Study Notes · 🥽 AZ-305: Data & Analytics Services — Deep Dive · 📘 DP-700 Study Notes
| Detail | Info |
|---|---|
| 🏅 Certification | Microsoft Certified: Fabric Analytics Engineer Associate |
| 📝 Passing Score | 700 / 1000 |
| 💶 Exam Price | ~€26 EUR (varies by country; VAT may apply) |
| ⏱️ Duration | 100 minutes (120 min seat time incl. check-in) |
| ❓ Question Types | MCQ, multi-select, drag-and-drop, case studies |
| 🔁 Renewal | Annual via free online assessment on Microsoft Learn |
| 🛡️ Prerequisite | None (recommended: hands-on Fabric & Power BI experience) |
⚠️ Official ranges from the Microsoft study guide (updated April 2026)
pie title Exam Domain Weights of the DP-600 (official ranges)
"Prepare Data (45–50%)" : 50
"Maintain a Data Analytics Solution (25–30%)" : 25
"Implement & Manage Semantic Models (25–30%)" : 25
| # | Domain | Official Weight | Key Services |
|---|---|---|---|
| 1 | Maintain a Data Analytics Solution | 25–30% | Security, governance, version control, deployment pipelines, XMLA endpoint |
| 2 | Prepare Data | 45–50% | Lakehouses, warehouses, Dataflow Gen2, notebooks, SQL, KQL, DAX, star schemas |
| 3 | Implement & Manage Semantic Models | 25–30% | Storage modes, DAX, Direct Lake, relationships, calculation groups, composite models |
🔑 Domain 2 (Prepare Data) carries nearly half the exam weight — prioritize data preparation and transformation skills.
flowchart LR
PL300["📊 PL-300\nPower BI\nData Analyst\n(Recommended)"]
DP600["📈 DP-600\nImplementing Analytics\nSolutions Using\nMicrosoft Fabric\n(This Exam)"]
BADGE["🏅 Fabric Analytics\nEngineer Associate"]
PL300 -->|Foundation| DP600
DP600 --> BADGE
dp-600-study-notes/
├── README.md ← 📍 You are here
├── 00-fabric-prerequisites.md ← Microsoft Fabric fundamentals
├── 01-maintain-data-analytics-solution.md ← Domain 1 (25–30%)
├── 02-prepare-data.md ← Domain 2 (45–50%)
├── 03-implement-manage-semantic-models.md ← Domain 3 (25–30%)
├── 04-quick-reference-cheatsheet.md ← Last-minute review & exam traps
├── 05-appendix-kql-reference.md ← Appendix A — KQL syntax & exam caveats
├── 06-appendix-dax-reference.md ← Appendix B — DAX syntax & exam caveats
├── 07-appendix-sql-reference.md ← Appendix C — T-SQL syntax & exam caveats
├── 08-appendix-spark-sql-reference.md ← Appendix D — Spark SQL syntax & exam caveats
└── 09-appendix-pyspark-reference.md ← Appendix E — PySpark syntax & exam caveats
| Resource | Link |
|---|---|
| 📚 Microsoft's DP-600 Certification Learning Paths | Certification Learning Paths |
| 📄 Official Exam Page | DP-600 Exam |
| 📋 Skills Measured / Study Guide | Official Study Guide |
| 🧪 Free Practice Assessment | Practice Assessment |
| 🎬 Exam Readiness Videos | Exam Readiness Zone |
| 📚 Microsoft Fabric Documentation | Fabric Docs |
| 🎓 Instructor-Led Course | DP-600T00-A (4 days) |
| 💶 EU Exam Pricing | Pearson VUE Microsoft |
- 🎯 The exam tests "which analytics approach?" not just "what does it do?" — think in trade-offs and constraints
- 📊 Know semantic model storage modes deeply — Import, DirectQuery, Direct Lake, and composite models
- 🔄 Know Dataflow Gen2 vs Notebook vs Pipeline boundaries — the exam tests which tool fits which scenario
- 🔒 Study security layers deeply — workspace, item, row-level (RLS), column-level (CLS), and object-level (OLS) security
- 📐 DAX is heavily tested — know CALCULATE, iterators, table filtering, variables, and calculation groups
- ⚡ Understand Direct Lake mode — fallback behavior, V-Order, framing, and when it falls back to DirectQuery
- 📖 For case studies: read business requirements and constraints first, then eliminate answers
| File | Topics Covered |
|---|---|
| 📘 00 — Fabric Prerequisites | OneLake, workspaces, lakehouses, warehouses, capacities, Delta Lake |
| 🔒 01 — Maintain Analytics Solution | Security, governance, version control, deployment pipelines, XMLA |
| 🔄 02 — Prepare Data | Data connections, star schemas, transforms, SQL, KQL, DAX queries |
| 📐 03 — Semantic Models | Storage modes, DAX, Direct Lake, relationships, optimization |
| ⚡ 04 — Quick Reference Cheatsheet | Key numbers, decision tables, exam traps, final checklist |
| 🔎 Appendix A — KQL Reference | KQL syntax, operators, aggregations, time-series, joins, exam caveats |
| 📐 Appendix B — DAX Reference | DAX evaluation contexts, CALCULATE, iterators, time intelligence, exam caveats |
| 🧮 Appendix C — SQL Reference | T-SQL: Warehouse vs SQL endpoint, CTAS, COPY INTO, views, CTEs, window functions, security |
| ✨ Appendix D — Spark SQL Reference | Lakehouse Delta tables, MERGE, OPTIMIZE/VACUUM, time travel, temp views |
| 🐍 Appendix E — PySpark Reference | DataFrame API, lazy evaluation, reading/writing Delta, joins, save modes, merges |
These notes are hosted on GitHub Pages and published as a searchable website on this URL:
The site includes full-text search, Mermaid diagram rendering, and mobile-friendly navigation for on-the-go review.
These notes are designed to be a structured, exam-focused summary of the most important concepts and services based on the official Microsoft DP-600 Study Guide and its criteria.
Additional resources and study notes maintained by me, such as the 📘 AZ-305 Study Notes, the 📘 DP-700 Study Notes, and more, are also available for those pursuing the Microsoft and Azure certifications at the following Landing Page:
👉 🧑🏫 Microsoft Study Notes: Central Hub
Maintained by Marco Grimaldi — Cloud Consultant, Language Trainer & Lifelong Learner.
🏠 Find more certification guides, study tips, and tech content at 🌐 marcogrimaldi29.com
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