microsoft dp-600 practice test

Question 1

You have a Microsoft Fabric tenant that contains a dataflow.

You are exploring a new semantic model.

From Power Query, you need to view column information as shown in the following exhibit.



Which three Data view options should you select? Each correct answer presents part of the solution.

• A. Show column value distribution
• B. Enable details pane
• C. Enable column profile Most Votes
• D. Show column quality details Most Votes
• E. Show column profile in details pane

Question 2

HOTSPOT You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_row contains the following table:

You create a Fabric notebook and attach it to Lakehouse1.
You need to use PySpark code to transform the data. The solution must meet the following requirements:
Add a column named pickupDate that will contain only the date portion of pickupDateTime.
Filter the DataFrame to include only rows where fareAmount is a positive number that is less than 100.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 3

You have a Fabric tenant.

You plan to create a data pipeline named Pipeline1. Pipeline1 will include two activities that will execute in sequence.

You need to ensure that a failure of the first activity will NOT block the second activity.

Which conditional path should you configure between the first activity and the second activity?

• A. Upon Failure
• B. Upon Completion
• D. Upon Skip

Question 4

You have a Fabric tenant that uses a Microsoft Power BI Premium capacity.
You need to enable scale-out for a semantic model.
What should you do first?

• A. At the semantic model level, set Large dataset storage format to Off.
• B. At the tenant level, set Create and use Metrics to Enabled.
• C. At the semantic model level, set Large dataset storage format to On.
• D. At the tenant level, set Data Activator to Enabled.

Question 5

Case study This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

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 made. 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.
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 6

You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table that has one million Parquet files.

You need to remove files that were NOT referenced by the table during the past 30 days. The solution must ensure that the transaction log remains consistent, and the ACID properties of the table are maintained.

What should you do?

• A. From OneLake file explorer, delete the files.
• B. Run the OPTIMIZE command and specify the Z-order parameter.
• C. Run the OPTIMIZE command and specify the V-order parameter.
• D. Run the VACUUM command.

Question 7

You have a Fabric tenant that contains a semantic model.
You need to prevent report creators from populating visuals by using implicit measures.
What are two tools that you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

• A. Microsoft Power BI Desktop
• B. Tabular Editor
• C. Microsoft SQL Server Management Studio (SSMS)
• D. DAX Studio

Question 8

You have a Fabric tenant that contains a Microsoft Power BI report.

You are exploring a new semantic model.

You need to display the following column statistics:

Count
Average
Null count
Distinct count
Standard deviation

Which Power Query function should you run?

• A. Table.schema
• B. Table.view
• C. Table.FuzzyGroup
• D. Table.Profile

Question 9

You have a Fabric tenant that contains a semantic model. The model contains 15 tables.

You need to programmatically change each column that ends in the word Key to meet the following requirements:

Hide the column.
Set Nullable to False
Set Summarize By to None.
Set Available in MDX to False.
Mark the column as a key column.

What should you use?

• A. Microsoft Power BI Desktop
• B. ALM Toolkit
• C. Tabular Editor
• D. DAX Studio

Question 10

Case study
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.

To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.

At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.


To start the case study To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.


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 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-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 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.