SAS Exam A00-240 Topic 1 Question 84 Discussion

Actual exam question for SAS's A00-240 exam

Question #: 84
Topic #: 1

What is a benefit to performing data cleansing (imputation, transformations, etc.) on data after partitioning the data for honest assessment as opposed to performing the data cleansing prior to partitioning the data?

AIt makes inference on the model possible.

BIt is computationally easier and requires less time.

CIt omits the training (and test) data sets from the benefits of the cleansing methods.

DIt allows for the determination of the effectiveness of the cleansing method.

Show Suggested Answer

Suggested Answer: D

by Novella at Apr 13, 2024, 04:13 AM

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Tom

1 days ago

I agree with It's important to know if the cleansing method is actually working.

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Theron

2 days ago

I think performing data cleansing after partitioning allows us to see how effective the cleansing method is.

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Valentin

24 days ago

Exactly! I mean, who cares if it takes a few extra minutes to run the analysis, as long as we're getting reliable results, right? I'd rather spend the time and do it right than rush through it and end up with a flawed model.

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Thaddeus

26 days ago

Haha, yeah, as long as you're not using a Commodore 64 to run your analysis, the computational time shouldn't be a problem. I'm with you, the honest assessment is way more important.

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Nell

27 days ago

That's true, but I think the benefits of getting a more accurate assessment outweigh the computational cost. Plus, modern computers are pretty powerful these days. I'm not too worried about the time it takes.

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Jamie

28 days ago

I agree, but I'm also concerned about the computational expense. Wouldn't it be more efficient to do the cleansing before partitioning? That way, we don't have to do it multiple times for the training and test sets.

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Staci

1 months ago

Yeah, that's a good point. Doing it after partitioning also allows us to see how effective the cleansing methods are. That way, we can make a more informed decision on which techniques to use in the future.

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Wilda

1 months ago

Hmm, this is an interesting question. I think performing data cleansing after partitioning the data allows us to get a more honest assessment of the model's performance. If we do it beforehand, the cleansing methods might give the model an unfair advantage on the test set.

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