
An independent study guide for Microsoft Certified: Fabric Analytics Engineer Associate · by Tony Rough
Know how to secure, prepare and model data in Microsoft Fabric, and why each setting matters.
Due on Amazon in December 2026, in Kindle and paperback editions.
This independent study guide for the Microsoft Certified: Fabric Analytics Engineer Associate exam distils what DP-600 really expects you to understand into the comparisons, configuration choices and traps that analytics engineering decisions turn on, with short T-SQL, KQL, DAX, PySpark and Power Query examples throughout.
Fifteen chapters, each readable on its own and together covering all three DP-600 skill areas:
Microsoft Fabric changes quickly. This edition reflects Microsoft's documentation as of October 2026 and the skills measured from 19 October 2026, and uses the current names and behaviour: semantic models (formerly datasets), Fabric Activator (formerly Data Activator), the OneLake catalog, Direct Lake on OneLake and on SQL, and lakehouses and warehouses without default semantic models.
This book contains no exam questions. It explains the knowledge the exam expects, so you can answer questions you have never seen and apply the same judgement to real analytics solutions.
Written by Tony Rough, a cloud architect with more than twenty years in IT infrastructure who holds the Azure Solutions Architect Expert, Azure Administrator and Azure Security Engineer certifications.
Part of the Ultra Transcenders series from Distilled Press. An independent publication, not affiliated with, sponsored by or endorsed by Microsoft Corporation.
Every skill area in Microsoft's DP-600 outline (as of October 19, 2026), and the chapters that cover it.
| Skill area | Weight | Chapters |
|---|---|---|
| Maintain a data analytics solution | 25–30% | 2, 3, 4, 5, 6 |
| Prepare data | 45–50% | 1, 4, 7, 8, 9, 10, 11, 12, 14 |
| Implement and manage semantic models | 25–30% | 3, 6, 8, 13, 14, 15 |
Plus an appendix glossary of 500+ terms, each linked to Microsoft Learn, with the same terms explained free online for print readers.



Some sections of the book, free to read online:
How the two Direct Lake flavours differ in table discovery, permission checks, fallback and unsupported cases, and which one to choose.
When each table storage mode fits a semantic model, based on data size, latency, source security and capacity.
What each Fabric workspace role can do across Power BI, data engineering, warehousing and real-time items.
Which items and settings a deployment copies or leaves alone, and how data source and parameter rules point each stage at its own data.
How data type, team skills, write needs and transactions decide between Fabric's lakehouse, warehouse, eventhouse and other stores.
What the Warehouse can do that a lakehouse's read-only SQL analytics endpoint can't, and when to use each.
The three many-to-many scenarios in a semantic model and the bridge-table or relationship design each one needs.
How data volume, transformation needs, skills and latency decide which Fabric tool should copy data into OneLake.