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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q74-Q79):
NEW QUESTION # 74
You have five Fabric workspaces.
You are monitoring the execution of items by using Monitoring hub.
You need to identify in which workspace a specific item runs.
Which column should you view in Monitoring hub?
- A. Submitter
- B. Location
- C. Job type
- D. Capacity
- E. Activity name
- F. Start time
- G. Item type
Answer: B
Explanation:
To identify in which workspace a specific item runs in Monitoring hub, you should view the Location column. This column indicates the workspace where the item is executed. Since you have multiple workspaces and need to track the execution of items across them, the Location column will show you the exact workspace associated with each item or job execution.
NEW QUESTION # 75
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table. The table contains the following columns.

You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct.
You need to prepare the data.
Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. ProductID
- B. Date
- C. TransactionID
- D. SalesAmount
- E. ProductName
- F. ProductColor
Answer: A,E,F
Explanation:
In a star schema, the DimProduct table serves as a dimension table that contains descriptive attributes about products. It will provide context for the FactSales table, which contains transactional data. The following columns should be included in the DimProduct table:
ProductName: The ProductName is an important descriptive attribute of the product, which is needed for analysis and reporting in a dimensional model.
ProductColor: ProductColor is another descriptive attribute of the product. In a star schema, it makes sense to include attributes like color in the dimension table to help categorize products in the analysis.
ProductID: ProductID is the primary key for the DimProduct table, which will be used to join the FactSales table to the product dimension. It's essential for uniquely identifying each product in the model.
NEW QUESTION # 76
You need to recommend a method to populate the POS1 data to the lakehouse medallion layers.
What should you recommend for each layer? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
A screenshot of a computer Description automatically generated

Bronze Layer: A pipeline Copy activity
The bronze layer is used to store raw, unprocessed data. The requirements specify that no transformations should be applied before landing the data in this layer. Using a pipeline Copy activity ensures minimal development effort, built-in connectors, and the ability to ingest the data directly into the Delta format in the bronze layer.
Silver Layer: A notebook
The silver layer involves extensive data cleansing (deduplication, handling missing values, and standardizing capitalization). A notebook provides the flexibility to implement complex transformations and is well-suited for this task.
NEW QUESTION # 77
Your company has a team of developers. The team creates Python libraries of reusable code that is used to transform data.
You create a Fabric workspace name Workspace1 that will be used to develop extract, transform, and load (ETL) solutions by using notebooks.
You need to ensure that the libraries are available by default to new notebooks in Workspace1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

NEW QUESTION # 78
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 eventstream that loads data into a table named Bike_Location in a KQL database. The table contains the following columns:
You need to apply transformation and filter logic to prepare the data for consumption. The solution must return data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered by No_Bikes in ascending order.
Solution: You use the following code segment:

Does this meet the goal?
- A. Yes
- B. no
Answer: A
Explanation:
Filter Condition: It correctly filters rows where Neighbourhood is "Sands End" and No_Bikes is greater than or equal to 15.
Sorting: The sorting is explicitly done by No_Bikes in ascending order using sort by No_Bikes asc.
Projection: It projects the required columns (BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp), which minimizes the data returned for consumption.
NEW QUESTION # 79
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