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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and maintain a data solution | 10-15% | - Deploy data assets
|
| Topic 2: Secure and monitor data solutions | 15-20% | - Secure data solutions
|
| Topic 3: Clean, transform, and enrich data | 25-30% | - Enrich data
|
| Topic 4: Load and prepare data | 20-25% | - Create and configure items
|
| Topic 5: Design and manage the data model | 20-25% | - Design a data model
|
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NEW QUESTION # 79
You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains an unpartitioned table named Table1.
You plan to copy data to Table1 and partition the table based on a date column in the source data.
You create a Copy activity to copy the data to Table1.
You need to specify the partition column in the Destination settings of the Copy activity.
What should you do first?
Answer: B
Explanation:
Before specifying the partition column in the Destination settings of the Copy activity, you should set Mode to Append (A). This will allow the Copy activity to add data to the table while taking the partition column into account. Reference = The configuration options for Copy activities and partitioning in Azure Data Factory, which are applicable to Fabric dataflows, are outlined in the official Azure Data Factory documentation.
NEW QUESTION # 80
You have a Fabric warehouse that contains two tables named DimDate and Trips.
DimDate contains the following fields.
Trips contains the following fields.
You need to compare the average miles per trip for statutory holidays versus non-statutory holidays.
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:
Comprehensive Detailed Explanation
Step 1: Requirement
We need to compare the average miles per trip for:
Statutory holidays (when IsHoliday = 1)
Non-statutory holidays (when IsHoliday = 0)
Step 2: Formula for average miles per trip
Average miles per trip = total miles ÷ number of trips
Total miles # SUM(t.tripDistance)
Number of trips # COUNT(t.tripID)
So the calculation is:
(SUM(t.tripDistance) / COUNT(t.tripID)) AS MilesPerTrip
Step 3: Grouping
We need a comparison by holiday status.
So we must group the results by:
GROUP BY d.IsHoliday
This ensures we get two rows: one for IsHoliday = 1 and one for IsHoliday = 0.
Step 4: Final Query
SELECT
d.IsHoliday,
(SUM(t.tripDistance) / COUNT(t.tripID)) AS MilesPerTrip
FROM DimDate d
INNER JOIN Trips t ON d.DateID = t.DateID
GROUP BY d.IsHoliday;
Why This is Correct
The formula ensures average miles per trip.
Grouping ensures comparison between holidays vs non-holidays.
Efficient aggregation, minimal computation.
References
Aggregate functions in T-SQL
GROUP BY clause
NEW QUESTION # 81
Hotspot Question
You have a Fabric tenant that contains three users named User1, User2, and User3. The tenant contains a security group named Group1. User1 and User3 are members of Group1.
The tenant contains the workspaces shown in the following table.
The tenant contains the domains shown in the following table.
User1 creates a new workspace named Workspace3.
You assign Domain1 as the default domain of Group1.
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:
Box 1: No
User2 is not a member of Group1 and has no direct association with Domain1 or Workspace3.
There's no indication User2 has inherited any role here.
Box 2: Yes
User3 is a member of Group1, which is associated with Domain1 because it was created by User
1. When Workspace3 is created under Domain1, User3 likely inherits the Viewer role.
Box 3: No
User3 is a member of Group1, but Workspace1 is not linked to Domain1. It's directly tied to User1 as the admin. There's no evidence User3 has any Contributor role in Workspace1.
NEW QUESTION # 82
You have a Fabric tenant that contains a lakehouse named Lakehouse1. You have forecast data stored in Azure Data Lake Storage Gen2. You plan to ingest the forecast data into Lakehouse1.
The data is already formatted, and you do NOT need to apply any further data transformations.
The solution must minimize development effort and costs.
Which method should you recommend to efficiently ingest the data?
Answer: C
NEW QUESTION # 83
You need to design a semantic model for the customer satisfaction report.
Which data source authentication method and mode should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.
Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS.
Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user ' s credentials.
Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.
Based on these considerations, the selections should be:
Authentication method: Service principal authentication
Mode: DirectQuery
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 # 84
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