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NEW QUESTION # 71
You have a Fabric notebook that has the Python code and output shown in the following exhibit.
Which type of analytics are you performing?
Answer: B
Explanation:
The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset. Descriptive analytics answers the question of " what has happened " by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.
References: Descriptive analytics and the use of histograms as a way to visualize data distribution are basic concepts in data analysis, often covered in introductory analytics and Python programming resources.
NEW QUESTION # 72
You have a Fabric tenant.
You need to configure OneLake security for users shown in the following table.
The solution must follow the principle of least privilege.
Which permission should you assign to each user? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 73
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a data pipeline named Pipeline1 and a lakehouse named Lakehouse1.
You perform the following actions:
Create a workspace named Workspace2.
Create a deployment pipeline named DeployPipeline1 that will deploy items from Workspace1 to Workspace2.
Add a folder named Folder1 to Workspace1.
Move Lakehouse1 to Folder1.
Run DeployPipeline1.
Which structure will Workspace2 have when DeployPipeline1 is complete?
Answer: B
Explanation:
Scenario:
Workspace1 contains: Pipeline1 and Lakehouse1.
A folder named Folder1 is created in Workspace1.
Lakehouse1 is moved into Folder1.
A deployment pipeline DeployPipeline1 deploys from Workspace1 # Workspace2.
Key Behavior:
Deployment pipelines in Microsoft Fabric maintain the folder hierarchy of items when deploying between workspaces.
If an item (Lakehouse1) is placed in a folder (Folder1), the same folder structure is replicated in the target workspace.
Items not placed in the folder (Pipeline1) remain at the root level.
Result in Workspace2 after deployment:
Pipeline1 remains at the root.
Lakehouse1 is placed inside Folder1.
NEW QUESTION # 74
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a lakehouse named I.H1 and a warehouse named DW1. I.H1 contains a table named signindata that is in the dho schema.
You need to create a stored procedure in DW1 that deduplicates the data in the signindata table.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Fach correct selection is worth one point.
Answer:
Explanation:
Explanation:
Scenario Recap
Fabric tenant # Workspace1
Contains:
Lakehouse LH1 (with table signindata in schema dho)
Warehouse DW1
Task: Create a stored procedure in DW1 that deduplicates rows in signindata.
Step 1: Stored procedure structure
In T-SQL, stored procedures begin with:
AS
BEGIN
-- logic
END
So the correct option for the first blank is BEGIN.
BEGIN DISTRIBUTED TRANSACTION is not needed because we are not spanning multiple servers or needing distributed transactions.
SET is not the right way to start the logic block.
Step 2: Deduplication logic
To remove duplicates from signindata, the query should return unique rows.
The simplest way is:
SELECT DISTINCT PersonID, FirstName, LastName
FROM dho.signindata;
Thus the correct choice for the second blank is DISTINCT.
GROUP BY could also deduplicate but is less efficient here since no aggregation is requested.
TOP 100 PERCENT WITH TIES is irrelevant.
Step 3: Final T-SQL stored procedure
CREATE PROCEDURE dbo.usp_GetPerson
AS
BEGIN
SELECT DISTINCT PersonID, FirstName, LastName
FROM dho.signindata;
END;
Why this is correct
BEGIN # correct stored procedure structure.
DISTINCT # ensures deduplication of rows from signindata.
References
CREATE PROCEDURE (Transact-SQL)
DISTINCT (Transact-SQL)
NEW QUESTION # 75
Case Study 1 - Contoso
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.
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?
Answer: D
Explanation:
We need to retrieve the maximum OrderID in the destination table to minimize the number of rows added during refresh. this would be an incremental load. can be done with data flows.
NEW QUESTION # 76
......
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