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Microsoft DP-100 Exam - Topic 1 Question 132 Discussion

Actual exam question for Microsoft's DP-100 exam
Question #: 132
Topic #: 1
[All DP-100 Questions]

You are creating a machine learning model. You have a dataset that contains null rows.

You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.

Which parameter should you use?

Show Suggested Answer Hide Answer
Suggested Answer: B

Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.


https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

by Quentin at Nov 25, 2025, 04:07 PM

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Tonette
3 days ago
B) Remove entire column? Nah, that could lose important info!
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Velda
8 days ago
D) Hot Deck is interesting, but I prefer simpler methods.
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Raina
14 days ago
Wait, can you really just remove the whole column? That seems extreme!
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Tresa
19 days ago
I think C) Remove entire row makes more sense if there are too many nulls.
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Ula
24 days ago
A) Replace with mean is a common approach!
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Maxima
29 days ago
Option B is a bit too extreme for me. Removing the entire column seems like overkill.
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Justine
1 month ago
Hmm, I'm torn between A and D. Replacing with the mean could work, but Hot Deck might be more sophisticated.
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Portia
1 month ago
Option D, Hot Deck, sounds interesting. I wonder how that works in Azure ML Studio.
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Sommer
1 month ago
Removing entire columns seems too drastic unless the column is mostly null, but I guess it depends on the dataset.
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Aja
2 months ago
I feel like using Hot Deck could be useful, but I can't recall exactly how it works in this context.
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Lashandra
2 months ago
I remember practicing a question similar to this, and I think replacing with mean was a common choice for handling missing data.
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Carin
2 months ago
I think we might need to remove entire rows if they have too many null values, but I'm not completely sure if that's the best approach.
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William
2 months ago
I've used the Clean Missing Data module before, and I usually find that removing the entire row is the best approach. That way you don't risk skewing the data by imputing values. But I'll double-check the details on each option just to be sure I'm making the right call.
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Dannette
3 months ago
Hmm, this is a tricky one. I'm leaning towards the Hot Deck method, since that can help preserve the statistical properties of the data. But I'll need to read up on how it works exactly. Definitely don't want to just remove rows or columns willy-nilly.
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Arlie
3 months ago
I'd go with option C. Removing the entire row is the safest way to handle null data in my opinion.
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Reita
3 months ago
Okay, let me think this through. If I replace with the mean, that could distort the distribution of the data. Removing the entire column seems a bit extreme. I think I'll go with option C and remove the rows - that seems like the safest bet to maintain data integrity.
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Cathrine
3 months ago
I'm a little unsure about this one. Should I remove the entire column instead? I don't want to lose too much data, but I also don't want to introduce bias by replacing the nulls. Maybe I'll look into the Hot Deck option too.
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Sabina
3 months ago
Hmm, this one seems pretty straightforward. I'd probably go with option C - remove the entire row if it has any null values. That way I don't risk skewing the data by replacing the nulls.
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Irving
2 months ago
I agree, removing the entire row is a safe choice.
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