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  • 5.0

Official Learning Partner

As a candidate for this exam, 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
  • Languages, including Structured Query Language (SQL), Data Analysis Expressions (DAX),

Skills measured

Audience profile

As a candidate for this exam, 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
  • Languages, including Structured Query Language (SQL), Data Analysis Expressions (DAX), and PySpark

Skills at a glance

  • Plan, implement, and manage a solution for data analytics (10–15%)
  • Prepare and serve data (40–45%)
  • Implement and manage semantic models (20–25%)
  • Explore and analyze data (20–25%)

Plan, implement, and manage a solution for data analytics (10–15%)

Plan a data analytics environment

  • Identify requirements for a solution, including components, features, performance, and capacity stock-keeping units (SKUs)
  • Recommend settings in the Fabric admin portal
  • Choose a data gateway type
  • Create a custom Power BI report theme

Implement and manage a data analytics environment

  • Implement workspace and item-level access controls for Fabric items
  • Implement data sharing for workspaces, warehouses, and lakehouses
  • Manage sensitivity labels in semantic models and lakehouses
  • Configure Fabric-enabled workspace settings
  • Manage Fabric capacity

Manage the analytics development lifecycle

  • Implement version control for a workspace
  • Create and manage a Power BI Desktop project (.pbip)
  • Plan and implement deployment solutions
  • Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models
  • Deploy and manage semantic models by using the XMLA endpoint
  • Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models

Prepare and serve data (40–45%)

Create objects in a lakehouse or warehouse

  • Ingest data by using a data pipeline, dataflow, or notebook
  • Create and manage shortcuts
  • Implement file partitioning for analytics workloads in a lakehouse
  • Create views, functions, and stored procedures
  • Enrich data by adding new columns or tables

Copy data

  • Choose an appropriate method for copying data from a Fabric data source to a lakehouse or warehouse
  • Copy data by using a data pipeline, dataflow, or notebook
  • Add stored procedures, notebooks, and dataflows to a data pipeline
  • Schedule data pipelines
  • Schedule dataflows and notebooks

Transform data

  • Implement a data cleansing process
  • Implement a star schema for a lakehouse or warehouse, including Type 1 and Type 2 slowly changing dimensions
  • Implement bridge tables for a lakehouse or a warehouse
  • Denormalize data
  • Aggregate or de-aggregate data
  • Merge or join data
  • Identify and resolve duplicate data, missing data, or null values
  • Convert data types by using SQL or PySpark
  • Filter data

Optimize performance

  • Identify and resolve data loading performance bottlenecks in dataflows, notebooks, and SQL queries
  • Implement performance improvements in dataflows, notebooks, and SQL queries
  • Identify and resolve issues with Delta table file sizes

Implement and manage semantic models (20–25%)

Design and build semantic models

  • Choose a storage mode, including Direct Lake
  • Identify use cases for DAX Studio and Tabular Editor 2
  • Implement a star schema for a semantic model
  • Implement relationships, such as bridge tables and many-to-many relationships
  • Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
  • Implement calculation groups, dynamic strings, and field parameters
  • Design and build a large format dataset
  • Design and build composite models that include aggregations
  • Implement dynamic row-level security and object-level security
  • Validate row-level security and object-level security

Optimize enterprise-scale semantic models

  • Implement performance improvements in queries and report visuals
  • Improve DAX performance by using DAX Studio
  • Optimize a semantic model by using Tabular Editor 2
  • Implement incremental refresh

Explore and analyze data (20–25%)

Perform exploratory analytics

  • Implement descriptive and diagnostic analytics
  • Integrate prescriptive and predictive analytics into a visual or report
  • Profile data

Query data by using SQL

  • Query a lakehouse in Fabric by using SQL queries or the visual query editor
  • Query a warehouse in Fabric by using SQL queries or the visual query editor
  • Connect to and query datasets by using the XMLA endpoint

SQL SCHOOL

24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.

Technical FAQs

Who is SQL School? How far you have been in the training services ?

SQL School is a registered training institute, established in February 2008 at Hyderabad, India. We offer Real-time trainings and projects including Job Support exclusively on Microsoft SQL ServerT-SQLSQL Server DBA and MSBI (SSISSSASSSRS) Courses. All our training services are completely practical and real-time.CREDITS of SQL School Training Center

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  • One of the few institutes consistently delivering the trainings for more than 8+ Years online as inhouse
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Please Note: Slot Confirmation Subject to Availability Of Seats.

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We have been providing the Trainings – Online, Video and Classroom for the last EIGHT years – effectively and efficiently for more than 100000 (1 lakh) students and professionals across USA, India, UK, Australia and other countries. We are dedicated to offer realtime and practical project oriented trainings exclusively on SQL Server and related technologies. We do provide 24×7 Lab and Assistance with Job Support – even aftrer the course! To make sure you are gaining confidence on our trainings, participans are requested to attend for a free LIVE demo based on the schedules posted @ Register.html. Alternatively, participants may request for video demo by mailing us to contact@sqlschool.com Registration process to take place once you are happy with the demo session. Further, payments accepted in installments (via Paypal / Online Banking) to ensure trusted services from SQL School™

YES, We use Enterprise Edition Evaluation Editions (Full Version with complete feature support valid for SIX months) for our trainings. Software and Installation Guidance would be provided for T-SQL, SQL DBA and MSBI / DW courses.

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