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NEW QUESTION # 64
You have a Fabric tenant tha1 contains a takehouse 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. Yes
- B. No
Answer: B
Explanation:
No, the EXPLAIN TABLE statement does not identify whether maintenance tasks were performed on a table. It shows the execution plan for a query. Reference = The usage and output of the EXPLAIN command can be found in the Spark SQL documentation.
NEW QUESTION # 65
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 semantic model named Model1.
You discover that the following query performs slowly against Model1.
You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:
Does this meet the goal?
- A. Yes
- B. No
Answer: B
NEW QUESTION # 66
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 semantic model named Model1.
You discover that the following query performs slowly against Model1.
You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:
NOT ISEMPTY ( CALCULATETABLE ( 'Order Item ' ) )
Does this meet the goal?
- A. Yes
- B. No
Answer: A
Explanation:
CALCULATETABLE will accept the row context for each of the rows returned by VALUES, and in turn NOT ISEMPTY will check if the calculated table has rows. This is like using EXISTS in T- SQL. It will check if any rows exists, but doesn't return rows, thus improving performance.
NEW QUESTION # 67
You have a KQL database that contains a table named Readings.
You need to query Readings and return the results shown in the following table.
How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 68
You have a Microsoft Power Bl report named Report1 that uses a Fabric semantic model.
Users discover that Report1 renders slowly.
You open Performance analyzer and identify that a visual named Orders By Date is the slowest to render. The duration breakdown for Orders By Date is shown in the following table.
What will provide the greatest reduction in the rendering duration of Report1?
- A. Reduce the number of visuals in Report1.
- B. Change the visual type of Orders By Dale.
- C. Enable automatic page refresh.
- D. Optimize the DAX query of Orders By Date by using DAX Studio.
Answer: A
Explanation:
Based on the duration breakdown provided, the major contributor to the rendering duration is categorized as
"Other," which is significantly higher than DAX Query and Visual display times. This suggests that the issue is less likely with the DAX calculation or visual rendering times and more likely related to model performance or the complexity of the visual. However, of the options provided, optimizing the DAX query can be a crucial step, even if "Other" factors are dominant. Using DAX Studio, you can analyze and optimize the DAX queries that power your visuals for performance improvements. Here's how you might proceed:
Open DAX Studio and connect it to your Power BI report.
Capture the DAX query generated by the Orders By Date visual.
Use the Performance Analyzer feature within DAX Studio to analyze the query.
Look for inefficiencies or long-running operations.
Optimize the DAX query by simplifying measures, removing unnecessary calculations, or improving iterator functions.
Test the optimized query to ensure it reduces the overall duration.
References: The use of DAX Studio for query optimization is a common best practice for improving Power BI report performance as outlined in the Power BI documentation.
NEW QUESTION # 69
You have the source data model shown in the following exhibit.
The primary keys of the tables are indicated by a key symbol beside the columns involved in each key.
You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 70
You have a Fabric tenant that contains a data warehouse named DW1. DW1 contains a table named DimCustomer. DimCustomer contains the fields shown in the following table.
You need to identify duplicate email addresses in DimCustomer. The solution must return a maximum of
1,000 records.
Which four T-SQL statements should you run in sequence? To answer, move the appropriate statements from the list of statements to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 71
You have a Fabric tenant that contains a lakehouse named Lakehouse1. You plan to use Dataflow Gen2 to ingest and transform data from an Azure SQL Database into Lakehouse1.
Which language should you use to transform the data in the dataflow?
- A. XML
- B. SQL
- C. M
- D. DAX
Answer: C
NEW QUESTION # 72
You have a Fabric tenant that contains two workspaces named Workspace1 and Workspace2 and a user named User1.
You need to ensure that User1 can perform the following tasks:
* Create a new domain.
* Create two subdomains named subdomain1 and subdomain2.
* Assign Workspace1 to subdomain1.
* Assign Workspace2 to subdomain2.
The solution must follow the principle of least privilege.
Which role should you assign to User1?
- A. Fabric admin
- B. Domain admin
- C. Workspace Admin
- D. Domain contributor
Answer: B
NEW QUESTION # 73
You have a Microsoft Power BI project that contains a file named definition.pbir. definition.pbir contains the following JSON.
Answer:
Explanation:
Explanation:
NEW QUESTION # 74
You have an Azure Data Lake Storage Gen2 account named storage! that contains a Parquet file named sales.
parquet.
You have a Fabric tenant that contains a workspace named Workspace1.
Using a notebook in Workspace1, you need to load the content of the file to the default lakehouse. The solution must ensure that the content will display automatically as a table named Sales in Lakehouse explorer.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Step 1 - Read the Parquet file into a DataFrame
df = spark.read.parquet("abfss://[email protected]/files/sales.parquet") This correctly loads the Parquet data into Spark.
Step 2 - Write into the Lakehouse as a managed table
If we want the result to be registered as a Lakehouse table and automatically appear in Lakehouse Explorer, we must:
Write the data in delta format (because Fabric Lakehouse tables are Delta tables).
Save the table under the tables folder, not files.
So the correct code is:
df.write.mode("overwrite").format("delta").saveAsTable("tables/sales")
Final Answer:
Format: delta
SaveAsTable Path: tables/sales
References:
Lakehouse tables in Microsoft Fabric
Save DataFrame as Delta Table in Spark
# Answer Selection:
First dropdown # delta
Second dropdown # tables/sales
NEW QUESTION # 75
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a warehouse named DW1. DW1 contains two tables named Employees and Sales. All users have read access to DW1.
You need to implement access controls to meet the following requirements:
* For the Sales table, ensure that the users can see only the sales data from their respective region.
* For the Employees table, restrict access to all Personally Identifiable Information (PII).
* Maintain access to unrestricted data for all the users.
What should you use for each table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 76
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?
- A. Update the data Table3.
- B. Update the data in Table1.
- C. ReadTable2.
- D. ReadTable3.
Answer: B
NEW QUESTION # 77
You have a Fabric tenant that contains lakehouse named Lakehousel. Lakehousel contains a Delta table with eight columns. You receive new data that contains the same eight columns and two additional columns.
You create a Spark DataFrame and assign the DataFrame to a variable named df. The DataFrame contains the new data. You need to add the new data to the Delta table to meet the following requirements:
* Keep all the existing rows.
* Ensure that all the new data is added to the table.
How should you complete the code? To answer, select the appropriate options in the answer area.
Answer:
Explanation:
Explanation:
o add new data to the Delta table while meeting the specified requirements:
* You should use the append mode to ensure that all new data is added to the table without affecting the existing rows.
* You should set the mergeSchema option to true to allow the schema of the Delta table to be updated with the new columns found in the DataFrame.
The completed code would look like this:
df.write.format("delta").mode("append")
option("mergeSchema", "true")
saveAsTable("Lakehouse1.TableName")
NEW QUESTION # 78
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 minimum value of the OrderiD column m the
- B. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the maximum value of the OrderlD column in the destination lakehouse
- C. an Azure Data Factory pipeline that executes a dataflow to retrieve the minimum value of the OrderlD column in the destination lakehouse
- D. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderlD column in the destination lakehouse
Answer: D
Explanation:
destination lakehouse
NEW QUESTION # 79
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.
Answer:
Explanation:
Explanation:
* The code embeds an existing Power BI report. - No
* The code creates a Power BI report. - Yes
* 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.
References =
* Introduction to DataFrames - Spark SQL
* Power BI and Azure Databricks
NEW QUESTION # 80
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals.
More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
Your network contains an on-premises Active Directory Domain Services (AD DS) domain named contoso.
com that syncs with a Microsoft Entra tenant by using Microsoft Entra Connect.
You have a Fabric tenant that contains a semantic model.
You enable dynamic row-level security (RLS) for the model and deploy the model to the Fabric service.
You query a measure that includes the username () function, and the query returns a blank result.
You need to ensure that the measure returns the user principal name (UPN) of a user.
Solution: You create a role in the model.
Does this meet the goal?
- A. Yes
- B. No
Answer: B
NEW QUESTION # 81
Hotspot Question
You have a Fabric tenant that contains a workspace named Workspace1 and a user named DBUser. Workspace1 contains a lakehouse named Lakehouse1. DBUser does NOT have access to the tenant.
You grant DBUser access to Lakehouse1 as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 82
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.
Answer:
Explanation:
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.
NEW QUESTION # 83
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.
Answer:
Explanation:
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.
References =
* GRANT Schema Permissions
* Permissions That Can Be Granted on a Schema
NEW QUESTION # 84
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. PySpark
- C. DAX
- D. Spark SQL
Answer: B,D
Explanation:
https://learn.microsoft.com/en-us/azure/synapse-analytics/machine-learning/tutorial-score-model- predict-spark-pool
NEW QUESTION # 85
Your company has a finance department.
You have a Fabric tenant, an Azure Storage account named storagel, and a Microsoft Entra group named Groupl. Groupl contains the users in the finance department.
You need to create a new workspace named Workspacel in the tenant. The solution must meet the following requirements:
* Ensure that the finance department users can create and edit items in Workspace"!.
* Ensure that Workspacel can securely access storagel to read and write data.
* Ensure that you are the only admin of Workspacel.
* Minimize administrative effort.
You create Workspacel.
Which two actions should you perform next? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
- A. Assign the Admin role to yourself.
- B. Create a workspace identity.
- C. Assign the Contributor role to Groupl.
- D. Assign the Contributor role to each finance department user.
Answer: B,C
Explanation:
Finance department users can create and edit items in Workspace1 #
The correct role is Contributor.
To minimize effort, assign this role to the Microsoft Entra group (Group1) instead of assigning it to each user individually.
So answer A is correct, not B.
Workspace1 can securely access storagel (Azure Storage) to read and write data # To connect a Fabric workspace to external resources securely, you use a Workspace identity (a managed identity for the workspace).
This allows Fabric items to authenticate to Azure Storage without embedding credentials.
So answer D is correct.
You are the only admin of Workspace1 #
By default, the workspace creator (you) is the admin. You do not need to explicitly reassign the admin role to yourself (so C is unnecessary).
Minimize administrative effort #
Assigning Contributor role to the group (A) is minimal effort compared to assigning it individually to each user (B).
Final Answer:
A). Assign the Contributor role to Group1
D). Create a workspace identity
References:
Workspace roles in Microsoft Fabric
Workspace identity for secure data access
Topic 2, 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
NEW QUESTION # 86
You have a Fabric tenant that contains a lakehouse named Lakehouse1.
You need to prevent new tables added to Lakehouse1 from being added automatically to the default semantic model of the lakehouse.
What should you configure? (5)
- A. the semantic model settings
- B. the Lakehouse1 settings
- C. the SQL analytics endpoint settings
- D. the workspace settings
Answer: C
Explanation:
To prevent new tables added to Lakehouse1 from being automatically added to the default semantic model, you should configure the semantic model settings. There should be an option within the settings of the semantic model to include or exclude new tables by default. By adjusting these settings, you can control the automatic inclusion of new tables.
References: The management of semantic models and their settings would be covered under the documentation for the semantic layer or modeling features of the Fabric tenant's lakehouse solution.
NEW QUESTION # 87
You have a Fabric workspace named Workspace1 that contains the following items:
A warehouse named Warehouse1
A semantic model named Model1
An interactive report named Report1
You need to allow a user named User1 to access a single table in Warehouse1. The solution must follow the principle of least privilege.
What should you do first?
- A. Assign the Viewer role to User1 for Workspace1.
- B. Share Warehouse1 with User1.
- C. Assign object level permissions to User1 for Warehouse1.
- D. Assign the db_datareader role to User1 for Warehouse1.
Answer: C
Explanation:
Requirement
Fabric workspace: Workspace1
Contains: Warehouse1, Model1, Report1
Task: Allow User1 to access a single table in Warehouse1.
Must follow principle of least privilege # grant only the exact permissions required.
Step 1: Evaluate Options
A). Assign object level permissions to User1 for Warehouse1.
In Fabric warehouses, you can grant permissions at the object level (table, schema, or column).
If the requirement is access to only one table, the correct approach is to grant SELECT permissions on that specific table.
This satisfies least privilege.
Correct. #
B). Assign the db_datareader role to User1 for Warehouse1.
db_datareader provides read access to all tables in the database/warehouse.
This violates least privilege.
Not correct.
C). Share Warehouse1 with User1.
Sharing grants access to the whole warehouse.
Too broad, not least privilege.
Not correct.
D). Assign the Viewer role to User1 for Workspace1.
Viewer role allows seeing all items in the workspace (warehouse, model, reports).
This would expose more than the single table.
Not correct.
Step 2: Correct Action
Use object-level permissions:
GRANT SELECT ON dbo.TableName TO [User1];
This ensures User1 can only query that specific table, nothing else.
References
Manage object-level security in Microsoft Fabric warehouses
Principle of least privilege in Fabric
NEW QUESTION # 88
......
Microsoft DP-600 Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
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| Topic 2 |
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| Topic 3 |
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