This course is designed by Microsoft Certified industry trainers and coaches who enabled 10K + IT students and learners to crack the "Microsoft Fabric Analytics Engineer Associate certification" in their 1st attempt.

This course contains 150+ questions which precisely designed by a team of experts and trainers strictly according to latest exam syllabus and patterns.

Created by expert trainers who have helped over 10K+ IT professionals, these questions cover key topics like Microsoft Fabric, Synapse Analytics, Power BI, and Azure Data Factory. Whether you're a data engineer, BI analyst, Power BI developer, or solution architect, this course will give you the skills and knowledge to pass the exam and advance your career.

Microsoft fabric will be the next generation data integrations, data processing and BI reporting platform by Microsoft and will create new opportunities and get learners well paid.

Getting certified in Microsoft Fabric will bring you out of the current data stream job market and enable you to grab job quickly by show casing the certified badge which will make your profile out of the thousands of candidates in the data market.

"Syllabus: Microsoft Certified: Fabric Analytics Engineer Associate"

As a candidate for this certification, you should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions.

Your responsibilities for this role include transforming data into reusable analytics assets by using Microsoft Fabric components, such as:

• Lakehouses

• Data warehouses

• Notebooks

• Dataflows

• Data pipelines

• Semantic models

• Reports

You implement analytics best practices in Fabric, including version control and deployment.

To implement solutions as a Fabric analytics engineer, you partner with other roles, such as:

• Solution architects

• Data engineers

• Data scientists

• AI engineers

• Database administrators

• Power BI data analysts

In addition to in-depth work with the Fabric platform, you need experience with:

• Data modeling

• Data transformation

• Git-based source control

• Exploratory analytics

• Programming languages (including Structured Query Language (SQL), Data Analysis Expressions (DAX), and PySpark)