Microsoft DP-600 prep: Fabric Analytics Engineer Associate

<div>This course covers the&nbsp; content required for the DP-600 "Fabric Analytics Engineer Associate" certification exam.</div><div><br></div><div>It is as per the DP-600 requirements as of 15 November 2024, with some updates as of 14 January 2026 and 20 April 2026.</div><div><br></div><div>This course is also useful for the following Microsoft Applied Skills:</div><div><ul><li>APL-3008 "Implement a Real-Time Intelligence solution with Microsoft Fabric"</li><li><span style="font-size: 1rem;">APL-3010 "Implement a data warehouse in Microsoft Fabric"</span></li></ul></div><div><span style="font-size: 1rem;">Please note: This course is not affiliated with, endorsed by, or sponsored by Microsoft.</span></div><div><br></div><div>What do students like you say about this course?</div><div><br></div><div>Andrew says: "I enjoyed this course, and I learned some really useful things. The Calculation Group clarified this relatively new feature for me, so I will use it in the future. I'm taking my DP-600 exam soon. This course has filled in my knowledge of Fabric. I've even activated my Fabric 60 Day Trial because of this course, so I can actually go through the steps of creating items and manipulating data first-hand. The corse was presented in clear and logical manner."</div><div><br></div><div>Saurabh says: "This course is great. Phillip has explained the concepts in details and hands on activities, practice activities and in depth explanation of advanced DAX functions, SQL, PySpark and KQL makes this course stand out. I am going to attempt DP 600 in next four days. Thank you Phillip, I learned new things in each video. Keep bringing such great courses. I love your such in depth hands on courses."</div><div><br></div><div>Jerry says: "Hands down, this is one of the best courses for DP-600 available on Udemy. I’ve purchased several other courses, but none compare to this one. It’s clear, concise, and focused, with no unnecessary content. I’ve received my certification, thanks to this excellent course. Highly recommend!"</div><div><br></div><div><span style="font-size: 1rem;">It comes in four parts:</span></div><div><ul><li>Part 1 - Power BI knowledge you will have gained if you have already studied for the PL-300 exam,</li><li><span style="font-size: 1rem;">Part 2 - additional Power BI knowledge (not part of the PL-300 exam),</span></li><li><span style="font-size: 1rem;">Part 3 - Fabric lakehouses and data warehouses using SQL, and</span></li><li><span style="font-size: 1rem;">Part 4 - eventhouses using KQL.</span></li></ul></div><div><span style="font-size: 1rem;">Part 1 of this course is for you if you have not studied for Microsoft's PL-300 exam. If you have, then please join me in Section 1, and then skip to Part 2 (Section 9). In Part 1, we'll look at:</span></div><div><ul><li>Installing and creating a report in Power BI Desktop and uploading it to Power BI Service,</li><li><span style="font-size: 1rem;">Creating calculated columns and measures using DAX, and</span></li><li><span style="font-size: 1rem;">Other PL-300 exam topics needed for the DP-600 exam.</span></li></ul></div><div><span style="font-size: 1rem;">In Part 2 of this course, we'll start with Power BI. We'll look at:</span></div><div><ul><li><span style="font-size: 1rem;">Developing our design of semantic models, including calculation groups/items and field parameters.</span></li><li><span style="font-size: 1rem;">Expanding our DAX knowledge, with DAX variables and windowing functions.</span></li><li><span style="font-size: 1rem;">Using external apps, such as Tableau Editor 2 and DAX Studio,</span></li><li><span style="font-size: 1rem;">Implementing many-to-many relationships, implementing dynamic strings, and using the Optimize menu.</span></li><li><span style="font-size: 1rem;">The analytics development lifecycle, focusing on version control and deployment solutions,</span></li><li><span style="font-size: 1rem;">Other Analytics topics, such as creating aggregation tables and using the XMLA endpoint.</span></li></ul></div><div><span style="font-size: 1rem;">In Part 3 of this course, we'll query and manipulate data in Fabric lakehouses and data warehouses using SQL.</span></div><div><ul><li><span style="font-size: 1rem;">After a brief look around Fabric, we'll start by ingesting data by using data pipelines and dataflows.</span></li><li><span style="font-size: 1rem;">We'll then create a lakehouse and Data Warehouse and use the SQL Analytics Endpoint to manipulate the data in SQL. We'll learn the 6 principle clauses in the SQL Select statement: SELECT, FROM, WHERE, GROUP BY, HAVING and ORDER BY.</span></li></ul></div><div><span style="font-size: 1rem;">In Part 4 of this course, we'll look at the Eventhouses and KQL:</span></div><div><ul><li><span style="font-size: 1rem;">We'll create an eventhouse, see sample KQL queries, and how you can convert SQL queries to KQL.</span></li><li><span style="font-size: 1rem;">We'll select, filter and aggregate data using KQL.</span></li><li><span style="font-size: 1rem;">We'll expand our KQL queries using string, number, datetime and timespan functions.</span></li><li><span style="font-size: 1rem;">Finally, we'll transform data using KQL, merging and joining data, and identify and resolve duplicate and missing data.</span></li></ul></div><div><span style="font-size: 1rem;">No prior knowledge is assumed. We will start from the beginning for all languages and items, although any prior knowledge of DAX, SQL or KQL is useful.</span></div><div><br></div><div>Once you have completed the course, you will have a good knowledge of maintaining a data analytics solution, preparing data, and implementing and managing semantic models. And with some practice, you could even go for the official Microsoft certification DP-600 - wouldn't the "Microsoft Certified: Fabric Analytics Engineer Associate" certification look good on your CV or resume?</div><div><br></div><div>I hope to see you in the course - why not have a look at what you could learn?</div>

What you'll learn:

• Plan, implement, and manage a solution for data analytics
• Prepare and serve data
• Implement and manage semantic models
• Explore and analyze data