DP-600 CASE STUDIES

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CASE STUDY-1

Overview -
Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions
named Sales and Research. The Sales division contains two departments named
Online Sales and Retail Sales. The Research division assigns internally developed
product lines to individual teams of researchers and analysts.

Existing Environment

Identity Environment -
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two
groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.

Data Environment -
Contoso has the following data environment:
The Sales division uses a Microsoft Power BI Premium capacity.
The semantic model of the Online Sales department includes a fact table named
Orders that uses Import mode. In the system of origin, the OrderID value represents
the sequence in which orders are created.
The Research department uses an on-premises, third-party data warehousing
product.
Fabric is enabled for contoso.com.
An Azure Data Lake Storage Gen2 storage account named storage1 contains
Research division data for a product line named Productline1. The data is in the delta
format.
A Data Lake Storage Gen2 storage account named storage2 contains Research
division data for a product line named Productline2. The data is in the CSV format.

Requirements

Planned Changes -
Contoso plans to make the following changes:
Enable support for Fabric in the Power BI Premium capacity used by the Sales
division.
Make all the data for the Sales division and the Research division available in Fabric.
For the Research division, create two Fabric workspaces named Productline1ws and
Productine2ws.
In Productline1ws, create a lakehouse named Lakehouse1.
In Lakehouse1, create a shortcut to storage1 named ResearchProduct.
Data Analytics Requirements -
Contoso identifies the following data analytics requirements:
All the workspaces for the Sales division and the Research division must support all
Fabric experiences.
The Research division workspaces must use a dedicated, on-demand capacity that
has per-minute billing.
The Research division workspaces must be grouped together logically to support
OneLake data hub filtering based on the department name.
For the Research division workspaces, the members of ResearchReviewersGroup1
must be able to read lakehouse and warehouse data and shortcuts by using SQL
endpoints.
For the Research division workspaces, the members of ResearchReviewersGroup2
must be able to read lakehouse data by using Lakehouse explorer.
All the semantic models and reports for the Research division must use version
control that supports branching.

Data Preparation Requirements –

Contoso identifies the following data preparation requirements:
The Research division data for Productline1 must be retrieved from Lakehouse1 by
using Fabric notebooks.
All the Research division data in the lakehouses must be presented as managed
tables in Lakehouse explorer.

Semantic Model Requirements -

Contoso identifies the following requirements for implementing and managing
semantic models:
The number of rows added to the Orders table during refreshes must be minimized.
The semantic models in the Research division workspaces must use Direct Lake
mode.

General Requirements -
Contoso identifies the following high-level requirements that must be considered for
all solutions:
Follow the principle of least privilege when applicable.
Minimize implementation and maintenance effort when possible.
Question-1
You need to ensure that Contoso can use version control to meet the data analytics
requirements and the general requirements.
What should you do?

A. Store at the semantic models and reports in Data Lake Gen2 storage.

B. Modify the settings of the Research workspaces to use a GitHub repository.

C. Modify the settings of the Research division workspaces to use an Azure

Repos repository.

D. Store all the semantic models and reports in Microsoft OneDrive.

Question-2
You need to recommend a solution to group the Research division workspaces.
What should you include in the recommendation? To answer, select the appropriate
options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Ans: Domain, The Fabric Admin portal

Question 3:
You need to refresh the Orders table of the Online Sales department. The solution
must meet the semantic model requirements.
What should you include in the solution?

A. an Azure Data Factory pipeline that executes a Stored procedure activity to

retrieve the maximum value of the OrderID column in the destination lakehouse

B. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve
the minimum value of the OrderID column in the destination lakehouse
C. an Azure Data Factory pipeline that executes a dataflow to retrieve the minimum
value of the OrderID column in the destination lakehouse

D. an Azure Data Factory pipeline that executes a dataflow to retrieve the

maximum value of the OrderID column in the destination lakehouse

Question 4:
Which syntax should you use in a notebook to access the Research division data for
Productline1?

A. spark.read.format(“delta”).load(“Tables/productline1/ResearchProduct”)

B. spark.sql(“SELECT * FROM Lakehouse1.ResearchProduct ”)

C. external_table(‘Tables/ResearchProduct)

D. external_table(ResearchProduct)

Question 5:
Which workspace role assignments should you recommend for
ResearchReviewersGroup1 and ResearchReviewersGroup2? To answer, select the
appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Correct Answer: Viewer, Contributor

Question 6:
What should you use to implement calculation groups for the Research division
semantic models?

A. Microsoft Power BI Desktop

B. the Power BI service

C. DAX Studio

D. Tabular Editor

Question 7:
You need to migrate the Research division data for Productline1. The solution must
meet the data preparation requirements.

How should you complete the code? To answer, select the appropriate options in the
answer area.

NOTE: Each correct selection is worth one point.

Correct Ans: Delta, Tables/productline1

Question 8:
You need to recommend which type of Fabric capacity SKU meets the data analytics
requirements for the Research division.

What should you recommend?

A. A
B. EM
C. P
D. F

Case Study-2
Overview -
Litware, Inc. is a manufacturing company that has offices throughout North America.
The analytics team at Litware contains data engineers, analytics engineers, data
analysts, and data scientists.
Existing Environment -

Fabric Environment -
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT
enabled any Fabric capacities and features.

Available Data -
Litware has data that must be analyzed as shown in the following table.

The Product data contains a single table and the following columns.

The customer satisfaction data contains the following tables:

Survey, Question, Response

For each survey submitted, the following occurs:

One row is added to the Survey table.
One row is added to the Response table for each question in the survey.
The Question table contains the text of each survey question. The third question in
each survey response is an overall satisfaction score. Customers can submit a survey
after each purchase.

User Problems -
The analytics team has large volumes of data, some of which is semi-structured. The
team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This
logic is implemented in several databases and semantic models, but the logic does
NOT always match across implementations.
Requirements -

Planned Changes -
Litware plans to enable Fabric features in the existing tenant. The analytics team will
create a new data store as a proof of concept (PoC). The remaining Liware users will
only get access to the Fabric features once the PoC is complete. The PoC will be
completed by using a Fabric trial capacity
The following three workspaces will be created:
AnalyticsPOC: Will contain the data store, semantic models, reports pipelines,
dataflow, and notebooks used to populate the data store
DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to
populate OneLake
DataSciPOC: Will contain all the notebooks and reports created by the data scientists

The following will be created in the AnalyticsPOC workspace:

A data store (type to be decided)

A custom semantic model

A default semantic model

Interactive reports -
The data engineers will create data pipelines to load data to OneLake either hourly or
daily depending on the data source. The analytics engineers will create processes to
ingest, transform, and load the data to the data store in the AnalyticsPOC workspace
daily. Whenever possible, the data engineers will use low-code tools for data
ingestion. The choice of which data cleansing and transformation tools to use will be
at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data
store as the sole data source.

Technical Requirements -
The data store must support the following:
Read access by using T-SQL or Python
Semi-structured and unstructured data
Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to
Data will be loaded without transformation in one area of the AnalyticsPOC data
store. The data will then be cleansed, merged, and transformed into a dimensional
model
The data load process must ensure that the raw and cleansed data is updated
completely before populating the dimensional model
OneLake will be stored in the Parquet format and will meet Delta Lake specifications.

The dimensional model must contain a date dimension. There is no existing data
source for the date dimension. The Litware fiscal year matches the calendar year. The
date dimension must always contain dates from 2010 through the end of the current
year.
The product pricing group logic must be maintained by the analytics engineers in a
single location. The pricing group data must be made available in the data store for
T-SOL. queries and in the default semantic model. The following logic must be used:
List prices that are less than or equal to 50 are in the low pricing group.
List prices that are greater than 50 and less than or equal to 1,000 are in the medium
pricing group.
List prices that are greater than 1,000 are in the high pricing group.

Security Requirements -
Only Fabric administrators and the analytics team must be able to see the Fabric
items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the
AnalyticsPOC workspace:
Fabric administrators will be the workspace administrators.
The data engineers must be able to read from and write to the data store. No access
must be granted to datasets or reports.
The analytics engineers must be able to read from, write to, and create schemas in
the data store. They also must be able to create and share semantic models with the
data analysts and view and modify all reports in the workspace.
The data scientists must be able to read from the data store, but not write to it. They
will access the data by using a Spark notebook
The data analysts must have read access to only the dimensional model objects in
the data store. They also must have access to create Power BI reports by using the
semantic models created by the analytics engineers.
The date dimension must be available to all users of the data store.
The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from
the dimensional model in the data store. Litware already has the following Microsoft
Entra security groups:
FabricAdmins: Fabric administrators
AnalyticsTeam: All the members of the analytics team
DataAnalysts: The data analysts on the analytics team
DataScientists: The data scientists on the analytics team
DataEngineers: The data engineers on the analytics team
AnalyticsEngineers: The analytics engineers on the analytics team

Report Requirements -
The data analysts must create a customer satisfaction report that meets the following
requirements:
Enables a user to select a product to filter customer survey responses to only those
who have purchased that product.
Displays the average overall satisfaction score of all the surveys submitted during the
last 12 months up to a selected dat.
Shows data as soon as the data is updated in the data store.
Ensures that the report and the semantic model only contain data from the current
and previous year.
Ensures that the report respects any table-level security specified in the source data
store.
Minimizes the execution time of report queries.
You need to assign permissions for the data store in the AnalyticsPOC workspace.
The solution must meet the security requirements.

Question-1
Which additional permissions should you assign when you share the data store? To
answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer: C,A,B

Question-2
What should you recommend using to ingest the customer data into the data store
in the AnalyticsPOC workspace?

A. a stored procedure

B. a pipeline that contains a KQL activity

C. a Spark notebook

D. a dataflow

Question 3:
You need to create a DAX measure to calculate the average overall satisfaction score.
How should you complete the DAX code? To answer, select the appropriate options
in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer: A,C

Question 4:
You need to resolve the issue with the pricing group classification.
How should you complete the T-SQL statement? To answer, select the appropriate
options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Ans: D,A,C

Question 5:
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

Question 6:
You need to ensure the data loading activities in the AnalyticsPOC workspace are
executed in the appropriate sequence. The solution must meet the technical
requirements.
What should you do?

A. Create a dataflow that has multiple steps and schedule the dataflow.

B. Create and schedule a Spark notebook.

C. Create and schedule a Spark job definition.

D. Create a pipeline that has dependencies between activities and schedule the
pipeline.

Question 7:
You need to implement the date dimension in the data store. The solution must meet
the technical requirements. What are two ways to achieve the goal? Each correct
answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Populate the date dimension table by using a dataflow.

B. Populate the date dimension table by using a Copy activity in a pipeline.
C. Populate the date dimension view by using T-SQL.
D. Populate the date dimension table by using a Stored procedure activity in a
pipeline.

Question 8:
You need to recommend a solution to prepare the tenant for the PoC. Which two
actions should you recommend performing from the Fabric Admin portal? Each
correct answer presents part of the solution.

NOTE: Each correct answer is worth one point.

A. Enable the Users can try Microsoft Fabric paid features option for the entire
organization.

B. Enable the Users can try Microsoft Fabric paid features option for specific
security groups.

C. Enable the Allow Azure Active Directory guest users to access Microsoft Fabric
option for specific security groups.

D. Enable the Users can create Fabric items option and exclude specific security
groups.
E. Enable the Users can create Fabric items option for specific security groups.

Question 9:
You need to design a semantic model for the customer satisfaction report.

Which data source authentication method and mode should you use? To answer,
select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Correct Ans: SSO, Direct Lake