Microsoft Exam DP-100 Topic 8 Question 91 Discussion

Actual exam question for Microsoft's DP-100 exam

Question #: 91
Topic #: 8

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.

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You use Azure Machine Learning designer to load the following datasets into an experiment:

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

Solution: Use the Apply Transformation module.

Does the solution meet the goal?

AYes

BNo

Show Suggested Answer

Suggested Answer: B

by Jina at Oct 20, 2023, 12:24 PM

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Chantay

8 months ago

Hmm, I'm not so sure. Doesn't the question say these are 'unique solutions'? I wonder if there might be another approach we're missing here. Maybe there's a more elegant way to combine the datasets that we're overlooking. *scratches head* Ah well, I'll go with the Apply Transformation option for now, but I'll keep an open mind.

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Vinnie

8 months ago

I'm with you guys on this one. The only thing I'd caution is to make sure the data types and formats are aligned between the two input datasets. Otherwise, you might run into some hiccups when trying to combine them. But overall, the Apply Transformation module seems like the way to go.

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Lore

8 months ago

Good point, but I think the 'unique solution' bit is referring to the overall series of questions, not this specific one. I'm pretty confident the Apply Transformation module is the best way to handle this task. *chuckles* Unless, of course, one of us suddenly has a brilliant epiphany and comes up with some sort of mind-blowing alternative approach!

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Shenika

8 months ago

Hmm, this seems like a straightforward question. I think the solution of using the Apply Transformation module to create a combined dataset is a good approach. It should meet the goal of having the same columns and header row as the input datasets, and including all rows from both.

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