Free DP-600 Exam Practice Questions
Practice for DP-600: Implementing Analytics Solutions Using Microsoft Fabric (beta) exam - disclaimer
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Exam Code: DP-6100
Exam Title: Implementing Analytics Solutions Using Microsoft Fabric (beta) - Exam Provider: Microsoft
- Total Exam Questions: 60
- Last Updated On: 27 May 2024
Exercise : Exam DP 600 Implementing Analytics Solutions Using Microsoft Fabric beta MCQ Questions and Answers
Question 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.explain()
Does this meet the goal?
| A. |
| B. |
Correct Answer : A. No
Description :
No answer description available for this question. Let us discuss.
View Answer
50/50
Workspace
Report
Discuss
Question 2
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
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| B. |
| C. |
| D. |
Correct Answer : D. AC
Description :
No answer description available for this question. Let us discuss.
Question 3
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 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.
A. From the Tenant settings, set Allow XMLA Endpoints and Analyze in Excel with on-premises datasets to Enabled.
B. From the Tenant settings, set Allow Azure Active Directory guest users to access Microsoft Fabric to Enabled.
C. From the Tenant settings, select Users can edit data model in the Power BI service.
D. From the Capacity settings, set XMLA Endpoint to Read Write.
E. From the Tenant settings, set Users can create Fabric items to Enabled.
F. From the Tenant settings, enable Publish to Web.
| A. |
| B. |
| C. |
| D. |
Correct Answer : C. ACD
Description :
No answer description available for this question. Let us discuss.
Question 4
DRAG DROP -
You have a Fabric tenant that contains a lakehouse named Lakehouse1.
Readings from 100 IoT devices are appended to a Delta table in Lakehouse1. Each set of readings is approximately 25 KB. Approximately 10 GB of data is received daily.
All the table and SparkSession settings are set to the default.
You discover that queries are slow to execute. In addition, the lakehouse storage contains data and log files that are no longer used.
You need to remove the files that are no longer used and combine small files into larger files with a target size of 1 GB per file.
What should you do? To answer, drag the appropriate actions to the correct requirements. Each action 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.
| A. |
| B. |
| C. |
| D. |
Correct Answer : D. Run the VACUUM command on a schedule, Run the OPTIMIZE command on a schedule
Description :
Question 5
You have a Fabric workspace named Workspace1 that contains a dataflow named Dataflow1. Dataflow1 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?
| A. |
| B. |
| C. |
| D. |
Correct Answer : D. The column has duplicate values.
Description :
Question 6
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:
REFRESH TABLE customer -
Does this meet the goal?
| A. |
| B. |
Correct Answer : B. No
Description :
No answer description available for this question. Let us discuss.
Question 7
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.
| A. |
| B. |
| C. |
| D. |
Correct Answer : A. Query the $System.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS dynamic management view (DMV)., B. Use the Analyze in Excel feature.
Description :
No answer description available for this question. Let us discuss.
Question 8
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 and has a summarized Amount that is higher than 10,000.
Which query should you use?
| A. |
| B. |
| C. |
| D. |
Correct Answer : D. 
Description :
No answer description available for this question. Let us discuss.
Question 9
HOTSPOT -
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 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.
| A. |
| B. |
| C. |
| D. |
Correct Answer : A. AVERAGE('Survey'[Response Value]), Period
Description :
Question 10
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:
EXPLAIN TABLE customer -
Does this meet the goal?
| A. |
| B. |
Correct Answer : A. No
Description :
No answer description available for this question. Let us discuss.