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

9 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

8 months ago

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

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Amber

8 months ago

Removing outliers can help improve the accuracy of the model.

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Novella

9 months ago

A) Remove the outlier rows.

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Norah

9 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

8 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

8 months ago

C) Change the method of binning to fixed width.

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Mary

8 months ago

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

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Lelia

9 months ago

A) Remove the outlier rows.

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Sherell

9 months ago

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

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King

9 months ago

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

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Viva

9 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

8 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

8 months ago

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

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Daisy

8 months ago

A) Remove the outlier rows.

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Isabelle

9 months ago

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

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Arthur

10 months ago

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

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Chaya

9 months ago

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

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Vivan

9 months ago

I agree, removing outliers is important for accurate results.

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Francisca

10 months ago

I think we should remove the outlier rows.

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