Exam DP-600 (beta): Implementing Analytics Solutions Using Microsoft Fabric – Beta is waiting for you with discount code!

Candidates for this exam have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions.

This certification could be a great fit if you have in-depth familiarity with the Fabric solution and you have experience with data modeling, data transformation, Git-based source control, exploratory analytics, and languages, including Structured Query Language (SQL), Data Analysis Expressions (DAX), and PySpark.

This exam is the only requirements for: Microsoft Certified: Fabric Analytics Engineer Associate

Beta Exam Discount – 80% and 100%

80% Discount code

Take advantage of the discounted beta exam offer. The first 300 people who take Exam DP-600 (beta) on or before January 25, 2024, can get 80 percent off market price..

To receive the discount, when you register for the exam and are prompted for payment, use code DP600Winfield. This is not a private access code. The seats are offered on a first-come, first-served basis. As noted, you must  take the exam on or before January 25, 2024. Please note that this beta exam is not available in Turkey, Pakistan, India, or China.

To use the 80% discount, remember to remove the current MCT discount, and then insert manually the exam voucher code.

100% Discount code

Are you a Microsoft Certified Trainer (MCT)? Microsoft give you a 100% discount!

The first 300 people who register (per exam) can take these exams for an 100% discount! The seats are offered on a first-come, first-served basis. As noted, you must  take the exam on or before January 25, 2024. Please note that this beta exam is not available in Turkey, Pakistan, India, or China.

As MCT, you need to visit this page , scroll down to the Current Beta Exam Opportunities table, and in the line of this exam you will find a link to the Beta Code request form (if it is still available).

Your request will be evaluated by Microsoft and you will receive an answer via email.

To use the 100% discount, remember to remove the current MCT discount, and then insert manually the exam voucher code.

Do you want to know more about Beta exams? Read more here.

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

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