Salesforce Exam ANC-301 Topic 8 Question 38 Discussion

Actual exam question for Salesforce's ANC-301 exam

Question #: 38
Topic #: 8

A training dataset Is being prepared for a Einstein Discovery story. One skewed with outliers. at action should the Einstein consultant take?

ARemove the outlier rows.

BNothing, because the field is not the outcome variable.

CChange the method of binning to fixed width.

DRemove the field because it has bad data

Show Suggested Answer

Suggested Answer: C

by Marguerita at Jul 16, 2024, 11:42 PM

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Nan

11 months ago

Removing outliers? Pffft, that's like trying to tame a wild Einstein. I say we just let the data do its thing!

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Kristofer

10 months ago

B) Nothing, because the field is not the outcome variable.

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Amber

10 months ago

Removing outliers can help improve the accuracy of the model.

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Novella

10 months ago

A) Remove the outlier rows.

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Norah

11 months ago

Option D? Nah, that's just throwing the baby out with the bathwater. Let's fix the data, not discard it!

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Corinne

10 months ago

Option D? Nah, that's just throwing the baby out with the bathwater. Let's fix the data, not discard it!

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Ona

10 months ago

C) Change the method of binning to fixed width.

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Mary

10 months ago

B) Nothing, because the field is not the outcome variable.

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Lelia

10 months ago

A) Remove the outlier rows.

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Sherell

11 months ago

I agree with Francisca, removing the outlier rows would be the best option.

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King

11 months ago

I'd go with C. Changing the binning method could help handle the skewed data better.

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Viva

11 months ago

I'm not sure, B seems reasonable too. If the field isn't the outcome variable, why bother with the outliers?

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Glendora

10 months ago

I'm not sure, B seems reasonable too. If the field isn't the outcome variable, why bother with the outliers?

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Cammy

10 months ago

B) Nothing, because the field is not the outcome variable.

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Daisy

10 months ago

A) Remove the outlier rows.

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Isabelle

11 months ago

I disagree, I believe we should change the method of binning to fixed width.

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Arthur

11 months ago

Definitely option A. Removing outliers is crucial for a clean and reliable training dataset.

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Chaya

11 months ago

Outliers can really skew the data, so it's best to get rid of them.

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Vivan

11 months ago

I agree, removing outliers is important for accurate results.

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Francisca

12 months ago

I think we should remove the outlier rows.

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