Exam DP-700 topic 2 question 26 discussion

Actual exam question from Microsoft's DP-700

Question #: 26
Topic #: 2

HOTSPOT
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You plan to process the following three datasets by using Fabric:

Dataset1: This dataset will be added to Fabric and will have a unique primary key between the source and the destination. The unique primary key will be an integer and will start from 1 and have an increment of 1.
Dataset2: This dataset contains semi-structured data that uses bulk data transfer. The dataset must be handled in one process between the source and the destination. The data transformation process will include the use of custom visuals to understand and work with the dataset in development mode.
Dataset3: This dataset is in a lakehouse. The data will be bulk loaded. The data transformation process will include row-based windowing functions during the loading process.

You need to identify which type of item to use for the datasets. The solution must minimize development effort and use built-in functionality, when possible.

What should you identify for each dataset? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

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Suggested Answer:

by abdulbasit170 at April 10, 2025, 5:03 p.m.

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zxc01

Highly Voted 2 months, 1 week ago

Dataset1 -> Dataflow Gen2 Dataset2 -> Notebook Dataset3 -> Dataflow Gen2 (the target is Lakehouse, we cannot use T-SQL)

upvoted 10 times

DarioReymago

2 weeks, 6 days ago

I'm agree w u but I change the first option to use all option Dataset1 -> T-Sql Dataset2 -> Notebook Dataset3 -> Dataflow Gen2 (the target is Lakehouse, we cannot use T-SQL)

upvoted 2 times

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PBridge

Highly Voted 3 weeks, 5 days ago

Dataset1: Will be added to Fabric with a unique primary key (auto-incrementing). Answer: Dataflow Gen2 dataflow Dataset2: Contains semi-structured data, uses bulk data transfer, and requires custom visuals during transformation in development mode. Answer: A notebook Dataset3: Stored in a lakehouse, bulk loaded, and uses row-based windowing functions during transformation. Answer: A T-SQL statement

upvoted 7 times

Rull

1 week, 3 days ago

Agree. For dataset3 you need to use T-SQL, which is definitely supported in a lakehouse. You cannot use a Dataflow Gen2 as it does not support advanced T-SQL functions such as windowing.

upvoted 1 times

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407a475

Most Recent 1 week, 2 days ago

Dataset1 -> Dataflow Gen2 (Primary key is just transfers from source to destination) Dataset2 -> Notebook Dataset3 -> T-SQL (Lakehouse is a source, not destination, only T-SQL can work with row-based windowing functions. KQL has row-based windowing functions as well but can't work with Lakehouse)

upvoted 2 times

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