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Exam Prep DP-600: Microsoft Fabric Analytics Engineer

Exam Prep DP-600: Microsoft Fabric Analytics Engineer is a beginner-level online course from Coursera in Data Analytics. It is structured in 3 modules, over 13h 8m. Learners rate it 3.1/5 from 17 ratings on Coursera.

Part of our Data Analytics courses collection, where we compare it against 9 other courses.

★★★★☆3.1(17 reviews) English

Course highlights

Provider Coursera
Duration 13h 8m
Level Beginner
Mode Self-paced
Language English
Certificate Yes — shareable certificate
Rating 3.1★ (17 reviews)
Price Check price Free

Level and time commitment

Coursera lists this course at Beginner level.
No prior experience in the subject is assumed — this is where the provider expects a newcomer to start.
What the provider says you need first:
  • Candidates for this certification should demonstrate expertise in designing, creating, & managing analytical assets[semantic models, data warehouses.
Time commitment: 13h 8m.
It is self-paced, so that figure is the material, not a fixed schedule.

Skills you’ll gain

Extract, Transform, LoadData IntegrationPerformance TuningReal Time DataData LakesData TransformationMicrosoft AzurePower BIAzure Synapse AnalyticsData ModelingData AnalysisData Warehousing

What this course covers

3 modules

The 3-part outline Coursera publishes for this course, across 13h 8m.

Module 1 · Maintain a data analytics solution
Module 2 · Prepare Data [Get, Transform, Query and Analyze data]
Module 3 · Implement and manage semantic models

What learners rate it

3.1
★★★★☆
17 ratings

Rated 3.1/5 by 17 learners on Coursera (checked 25 Aug 2026).

This rating is collected by Coursera from its own enrolled learners. We reproduce it as reported and do not accept paid or incentivised reviews. See it on Coursera →

How this page is put together

Compiled by the 10courses editorial desk. Fees, ratings, duration and certificate details for the 1 course shown are read directly from Coursera course pages — we never estimate a price or a rating. Last verified 25 August 2026.

How we compare and rank courses · Our review policy · Affiliate disclosure

Compare with alternatives

Same topic, different trade-offs — here's who runs each one and what it's best at.

Course Provider Rating Price Duration Why pick this one
Exam Prep DP-600: Microsoft Fabric Analytics Engineer This Coursera 3.1★ Check price 13h 8m Most popular — 0 learners
Introduction to Data Analytics Coursera 4.8★ Check price 11h 38m Highest rated (4.8★ from 20.8K reviews)
AWS Cloud Practitioner Essentials Coursera 4.8★ Check price 35h 48m Beginner-friendly · 35h 48m
ChatGPT Advanced Data Analysis Coursera 4.8★ Check price 10h 52m Quickest to finish (10h 52m)
See all Data Analytics courses →

Where Data Analytics leads

Roles we map to Data Analytics on 10courses. This is our own mapping of subject to job, not a placement claim by Coursera.

Data Analyst

Browse every course we track for one of these — courses for data analysts.

Common questions

How much does Exam Prep DP-600: Microsoft Fabric Analytics Engineer cost?

Coursera does not publish a price we can read on the course page, so we do not show one. Check the fee on the provider site — we would rather say nothing than quote a number we did not collect.

Does Exam Prep DP-600: Microsoft Fabric Analytics Engineer come with a certificate?

Yes — Coursera lists a certificate on completion. It is a course certificate, not a formal qualification or university credit.

How long does Exam Prep DP-600: Microsoft Fabric Analytics Engineer take?

Coursera lists it at 13h 8m. It is self-paced, so that is the volume of material rather than a deadline — how long it actually takes depends on the hours you put in each week.

Is Exam Prep DP-600: Microsoft Fabric Analytics Engineer suitable for beginners?

Yes. Coursera lists it at Beginner level, so no prior experience in the subject is assumed.

What does Exam Prep DP-600: Microsoft Fabric Analytics Engineer cover?

It is organised into 3 modules, starting with Maintain a data analytics solution and going on to Prepare Data [Get, Transform, Query and Analyze data], Implement and manage semantic models. The full outline is on this page.