DP-600 Implementing Analytics Solutions Using Microsoft Fabric Exam

About the exam
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Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.

Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.

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

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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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Sample Questions and Answers

QUESTION 1
What should you recommend using to ingest the customer data into the data store in the
AnatyticsPOC workspace?

A. a stored procedure
B. a pipeline that contains a KQL activity
C. a Spark notebook
D. a dataflow

Answer: D

QUESTION 2
Which type of data store should you recommend in the AnalyticsPOC workspace?

A. a data lake
B. a warehouse
C. a lakehouse
D. an external Hive metaStore

Answer: C

QUESTION 3
You are the administrator of a Fabric workspace that contains a lakehouse named Lakehouse1.
Lakehouse1 contains the following tables:
Table1: A Delta table created by using a shortcut
Table2: An external table created by using Spark
Table3: A managed table

Answer: A

QUESTION 4
You plan to connect to Lakehouse1 by using its SQL endpoint. What will you be able to do after connecting to Lakehouse1?

A. ReadTable3.
B. Update the data Table3.
C. ReadTable2.
D. Update the data in Table1.

Answer: D

QUESTION 5
You have a Fabric tenant that contains a warehouse.
You use a dataflow to load a new dataset from OneLake to the warehouse.
You need to add a Power Query step to identify the maximum values for the numeric columns.
Which function should you include in the step?

A. Table. MaxN
B. Table.Max
C. Table.Range
D. Table.Profile

Answer: B

QUESTION 6
You have a Fabric tenant that contains a machine learning model registered in a Fabric workspace.
You need to use the model to generate predictions by using the predict function in a fabric notebook.
Which two languages can you use to perform model scoring? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

A. T-SQL
B. DAX EC.
C. Spark SQL
D. PySpark

Answer: C, D

QUESTION 7
You are analyzing the data in a Fabric notebook.
You have a Spark DataFrame assigned to a variable named df.
You need to use the Chart view in the notebook to explore the data manually.
Which function should you run to make the data available in the Chart view?

A. displayMTML
B. show
C. write
D. display

Answer: D