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NEW QUESTION # 186
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:
Topic 2, Contoso, ltd.
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.
Identity Environment
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroupi and ReseachReviewefsGfoup2.
Data Environment
Contoso has the following data environment
* The Sales division uses a Microsoft Power B1 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 OrderlD 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 Producthne1. 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.
Planned Changes
Contoso plans to make the following changes:
* Enable support for Fabric in the Power Bl 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 Producttmelws and Productline2ws.
* in Productlinelws. create a lakehouse named LakehouseV
* 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 ResearchRevtewersGroupl 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 Producthne2 must be retrieved from Lakehouset 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.
NEW QUESTION # 187
You have a Fabric tenant that contains a warehouse named WH1. You run the following T-SQL query against WH1.
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:
We are analyzing the query:
SELECT e.[WWI Employee ID],
e.Employee,
e.[Preferred Name],
gdr.[WWI Employee ID] AS [Direct Report ID] ,
gdr.Employee AS [Direct Report]
FROM Dimension.Employee AS e
OUTER APPLY Dimension.GetDirectReports(e.[Employee Key]) AS gdr;
Key concepts:
APPLY operator: Used to join a table with a table-valued function.
OUTER APPLY ensures all rows from the left (Employee) are returned, even if the function returns no rows (similar to LEFT JOIN).
Table-valued function (TVF): Returns a set of rows (unlike scalar functions, which return a single value).
The function executes once per row from the left table, not once for the whole query.
Evaluating each statement:
" Dimension.GetDirectReports is a scalar T-SQL function. "
No # It must be a table-valued function, because OUTER APPLY requires a table expression.
" The Dimension.GetDirectReports function will run only once when the query runs. " No # With APPLY, the function runs for each row of Dimension.Employee.
" The output rows will include at least one row for each row in the Dimension.Employee table. " Yes # Because of OUTER APPLY, every employee row is included, even if there are no direct reports (in that case, the columns from gdr will be NULL).
Final Answer:
Scalar function # No
Runs once per query # No
At least one row per Employee # Yes
References:
APPLY operator in T-SQL
Table-valued functions
NEW QUESTION # 188
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 # 189
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?
Answer: C
NEW QUESTION # 190
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 mode! 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 (UPNJ of a user.
Solution: You add user objects to the list of synced objects in Microsoft Entra Connect.
Does this meet the goal?
Answer: A
Explanation:
The issue: USERNAME() returns blank because Fabric uses Microsoft Entra (Azure AD) identities, not on- premises AD DS identities.
Adding user objects to sync via Entra Connect does not solve the issue by itself.
The correct fix is to ensure the UPN is synchronized and properly mapped (usually via Entra Connect attribute mappings).
What is needed is to ensure the userPrincipalName attribute is synced, not just "user objects." Correct answer: No.
Reference: Dynamic RLS in Power BI with USERNAME()
NEW QUESTION # 191
......
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