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Free Microsoft DP-700 Exam Questions (page: 4)

Page 4 of 31
PDF with 118 Questions

HOTSPOT (Drag and Drop is not supported)
You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns.

You need to create an output that presents the summarized values of all the order quantities by year and product. The results must include a summary of the order quantities at the year level for all the products.
How should you complete the code? To answer, select the appropriate options in the answer area.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Summarize by year and product: The query needs to group data by both year and product name.
Include a summary of order quantities at the year level: This is achieved using grouping mechanisms like ROLLUP or CUBE.
SELECT YEAR(SO.ModifiedDate)
Since we need to summarize the data by year, extracting the year from the ModifiedDate column using YEAR (SO.ModifiedDate) is the correct choice. Other options like CAST or CONVERT do not specifically extract the year.
ROLLUP(YEAR(SO.ModifiedDate), P.Name)
The ROLLUP function creates subtotals for each grouping combination. In this case:
It will group by YEAR(SO.ModifiedDate) and P.Name (product name).
It will also include a summary for all products for each year, which meets the requirement of summarizing order quantities at the year level for all products.



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.

  1. Date
  2. ProductName
  3. ProductColor
  4. TransactionID
  5. SalesAmount
  6. ProductID

Answer(s): B,C,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:
1. ProductName: The ProductName is an important descriptive attribute of the product, which is needed for analysis and reporting in a dimensional model.
2. 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.
3. 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.



You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1.
In Workspace1, you create a new notebook named Notebook2.
You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1.
What should you do?

  1. Enable high concurrency for notebooks.
  2. Enable dynamic allocation for the Spark pool.
  3. Change the runtime version.
  4. Increase the number of executors.

Answer(s): A

Explanation:

To ensure that Notebook2 can attach to the same Apache Spark session as Notebook1, you need to enable high concurrency for notebooks. High concurrency allows multiple notebooks to share a Spark session, enabling them to run within the same Spark context and thus share resources like cached data, session state, and compute capabilities. This is particularly useful when you need notebooks to run in sequence or together while leveraging shared resources.



You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. Lakehouse1 contains the following tables:
Orders
Customer
Employee
The Employee table contains Personally Identifiable Information (PII).
A data engineer is building a workflow that requires writing data to the Customer table, however, the user does NOT have the elevated permissions required to view the contents of the Employee table.
You need to ensure that the data engineer can write data to the Customer table without reading data from the Employee table.
Which three actions should you perform? Each correct answer presents part of the solution.
Note: Each correct selection is worth one point.

  1. Share Lakehouse1 with the data engineer.
  2. Assign the data engineer the Contributor role for Workspace2.
  3. Assign the data engineer the Viewer role for Workspace2.
  4. Assign the data engineer the Contributor role for Workspace1.
  5. Migrate the Employee table from Lakehouse1 to Lakehouse2.
  6. Create a new workspace named Workspace2 that contains a new lakehouse named Lakehouse2.
  7. Assign the data engineer the Viewer role for Workspace1.

Answer(s): D,E,F



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