Microsoft DP-600 Implementing Analytics Solutions Using Microsoft Fabric Online Training

Topic 1, Litware. Inc. Case Study

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

litware has been using a Microsoft Power Bl tenant for three years. Litware has NOT enabled any Fabric capacities and features.

Fabric Environment

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.

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 Litware 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, dataflows, 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 OneLake will be stored in the Parquet format and will meet Delta Lake specifications.

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.

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-SQL 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 pnces 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 Bl 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

• Data Engineers: The data engineers on the analytics team

• Analytics Engineers: The analytics engineers on the analytics team

Report Requirements

The data analysis 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 date

• 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

HOTSPOT

You to need assign permissions for the data store in the AnalyticsPOC workspace. The solution must meet the security requirements.

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.

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Correct Answer:

Explanation:

Data Engineers: Read All SQL analytics endpoint data

Data Analysts: Read All Apache Spark

Data Scientists: Read All SQL analytics endpoint data

The permissions for the data store in the Analytics POC workspace should align with the principle of least privilege:

Data Engineers need read and write access but not to datasets or reports.

Data Analysts require read access specifically to the dimensional model objects and the ability to create Power BI reports.

Data Scientists need read access via Spark notebooks. These settings ensure each role has the necessary permissions to fulfill their responsibilities without exceeding their required access level.

HOTSPOT

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.

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Correct Answer:

Explanation:

The measure should use the AVERAGE function to calculate the average value.

It should reference the Response Value column from the ‘Survey’ table.

The ‘Number of months’ should be used to define the period for the average calculation.

To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.

HOTSPOT

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.

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Correct Answer:

Explanation:

You should use CREATE VIEW to make the pricing group logic available for T-SQL queries.

The CASE statement should be used to determine the pricing group based on the list price.

The T-SQL statement should create a view that classifies products into pricing groups based on the list price. The CASE statement is the correct conditional logic to assign each product to the appropriate pricing group. This view will standardize the pricing group logic across different databases and semantic models.

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

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

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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.

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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?

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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.

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HOTSPOT

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.

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Correct Answer:

Explanation:

For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.

Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS. Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user’s credentials.

Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.

Based on these considerations, the selections should be:

Authentication method: Service principal authentication

Mode: DirectQuery

Topic 2, Misc. Questions

You have a Fabric warehouse that contains a table named Staging.Sales.

Staging.Sales contains the following columns.

You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName arxl has a summarized Amount that is higher than 10,000.

Which query should you use?

A)

B)

C)

D)

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HOTSPOT

You have a data warehouse that contains a table named Stage. Customers. Stage-Customers contains all the customer record updates from a customer relationship management (CRM) system. There can be multiple updates per customer

You need to write a T-SQL query that will return the customer ID, name, postal code, and the last updated time of the most recent row for each customer ID.

How should you complete the code? To answer, select the appropriate options in the answer area, NOTE Each correct selection is worth one point.

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Correct Answer:

Explanation:

In the ROW_NUMBER() function, choose OVER (PARTITION BY CustomerID ORDER BY LastUpdated DESC).

In the WHERE clause, choose WHERE X = 1.

To select the most recent row for each customer ID, you use the ROW_NUMBER() window function partitioned by CustomerID and ordered by LastUpdated in descending order. This will assign a row number of 1 to the most recent update for each customer. By selecting rows where the row number

(X) is 1, you get the latest update per customer.

Reference = Use the OVER clause to aggregate data per partition Use window functions

HOTSPOT

You have a Fabric tenant.

You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power Bl visuals.

You run the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

The code embeds an existing Power BI report. – No

The code creates a Power BI report. – No

The code displays a summary of the DataFrame. – Yes

The code provided seems to be a snippet from a SQL query or script which is neither creating nor embedding a Power BI report directly. It appears to be setting up a DataFrame for use within a larger context, potentially for visualization in Power BI, but the code itself does not perform the creation or embedding of a report. Instead, it’s likely part of a data processing step that summarizes data.

Reference =

Introduction to DataFrames – Spark SQL

Power BI and Azure Databricks

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

You plan to connect to Lakehouse1 by using its SQL endpoint.

What will you be able to do after connecting to Lakehouse1?

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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?

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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.

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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?

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You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1. Report1 includes a Python visual. Data displayed by the visual is grouped automatically and duplicate rows are NOT displayed. You need all rows to appear in the visual.

What should you do?

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DRAG DROP

You have a Fabric tenant that contains a semantic model. The model contains data about retail

stores.

You need to write a DAX query that will be executed by using the XMLA endpoint The query must return a table of stores that have opened since December 1,2023.

How should you complete the DAX expression? To answer, drag the appropriate values to the correct

targets. Each value may be used once, more than once, or not at all. You may need to drag the split

bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

The correct order for the DAX expression would be:

DEFINE VAR_SalesSince = DATE ( 2023, 12, 01 )

EVALUATE

FILTER (

SUMMARIZE ( Store, Store[Name], Store[OpenDate] ),

Store[OpenDate] >= _SalesSince )

In this DAX query, you’re defining a variable _SalesSince to hold the date from which you want to filter the stores. EVALUATE starts the definition of the query. The FILTER function is used to return a table that filters another table or expression. SUMMARIZE creates a summary table for the stores, including the Store[Name] and Store[OpenDate] columns, and the filter expression Store[OpenDate] >= _SalesSince ensures only stores opened on or after December 1, 2023, are included in the results.

Reference =

DAX FILTER Function

DAX SUMMARIZE Function

You have a Fabric workspace named Workspace 1 that contains a dataflow named Dataflow1. Dataflow! has a query that returns 2.000 rows.

You view the query in Power Query as shown in the following exhibit.

What can you identify about the pickupLongitude column?

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You have a Fabric tenant named Tenant1 that contains a workspace named WS1. WS1 uses a capacity named C1 and contains a dawset named DS1. You need to ensure read-write access to DS1 is available by using the XMLA endpoint.

What should be modified first?

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You have a Fabric tenant that contains a workspace named Workspace^ Workspacel is assigned to a Fabric capacity.

You need to recommend a solution to provide users with the ability to create and publish custom Direct Lake semantic models by using external tools. The solution must follow the principle of least privilege.

Which three actions in the Fabric Admin portal should you include in the recommendation? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

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You are creating a semantic model in Microsoft Power Bl Desktop.

You plan to make bulk changes to the model by using the Tabular Model Definition Language (TMDL)

extension for Microsoft Visual Studio Code.

You need to save the semantic model to a file.

Which file format should you use?

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HOTSPOT

You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains three schemas named schemaA, schemaB. and schemaC

You need to ensure that a user named User1 can truncate tables in schemaA only.

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.

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Correct Answer:

Explanation:

GRANT ALTER ON SCHEMA::schemaA TO User1;

The ALTER permission allows a user to modify the schema of an object, and granting ALTER on a schema will allow the user to perform operations like TRUNCATE TABLE on any object within that schema. It is the correct permission to grant to User1 for truncating tables in schemaA.

Reference =

GRANT Schema Permissions

Permissions That Can Be Granted on a Schema

You need to provide Power Bl developers with access to the pipeline.

The solution must meet the following requirements:

• Ensure that the developers can deploy items to the workspaces for Development and Test.

• Prevent the developers from deploying items to the workspace for Production.

• Follow the principle of least privilege.

Which three levels of access should you assign to the developers? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

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You have a Fabric workspace that contains a DirectQuery semantic model. The model queries a data source that has 500 million rows.

You have a Microsoft Power Bl report named Report1 that uses the model. Report! contains visuals on multiple pages.

You need to reduce the query execution time for the visuals on all the pages.

What are two features that you can use? Each correct answer presents a complete solution. NOTE: Each correct answer is worth one point.

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You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily.

You create a Microsoft Power Bl semantic model named Modell that uses the CSV files as a data source. You configure incremental refresh for Model 1 and publish the model to a Premium capacity in the Fabric tenant.

When you initiate a refresh of Model1, the refresh fails after running out of resources.

What is a possible cause of the failure?

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You have a Fabric tenant that uses a Microsoft tower Bl Premium capacity. You need to enable scale-out for a semantic model.

What should you do first?

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You have a Fabric tenant that contains a warehouse. The warehouse uses row-level security (RLS). You create a Direct Lake semantic model that uses the Delta tables and RLS of the warehouse.

When users interact with a report built from the model, which mode will be used by the DAX queries?

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You have a Fabric tenant that contains a complex semantic model. The model is based on a star schema and contains many tables, including a fact table named Sales. You need to create a diagram of the model. The diagram must contain only the Sales table and related tables.

What should you use from Microsoft Power Bl Desktop?

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You have a Fabric tenant that contains a semantic model. The model uses Direct Lake mode.

You suspect that some DAX queries load unnecessary columns into memory.

You need to identify the frequently used columns that are loaded into memory.

What are two ways to achieve the goal? Each correct answer presents a complete solution. NOTE: Each correct answer is worth one point.

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