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Salesforce ANC-301 Exam - Topic 8 Question 38 Discussion

Actual exam question for Salesforce's ANC-301 exam

Question #: 38
Topic #: 8

[All ANC-301 Questions]

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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Serina

9 months ago

Changing the binning method could help too, but not always necessary.

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Kami

10 months ago

Nothing wrong with keeping the field if it's not the outcome variable.

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Tamra

10 months ago

Wait, what if the outliers are actually important?

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Glendora

10 months ago

Agree, outliers can skew results a lot!

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Mira

10 months ago

I think removing outlier rows is usually a good idea.

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Brandon

10 months ago

Removing the field seems drastic, especially if it has some useful information. I feel like we should explore other options first.

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Adelina

11 months ago

Changing the binning method could help with skewness, but I can't recall if fixed width is the right choice here.

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Cora

11 months ago

I think if the field isn't the outcome variable, then maybe we don't need to worry about the outliers too much? That sounds familiar.

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Yuki

11 months ago

I remember we discussed how outliers can skew results, but I'm not sure if removing them is always the best option.

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Alaine

11 months ago

I'm pretty confident the right answer is A - remove the outlier rows. Leaving in skewed data with outliers could really throw off the model, so cleaning that up first is the way to go.

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Lauran

11 months ago

Okay, I think I've got this. Since the question is about preparing the dataset for an Einstein Discovery story, the outliers are likely in the input data, not the target variable. In that case, I'd go with option A and remove the outlier rows.

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Jeffrey

11 months ago

Hmm, I'm a bit confused. The question mentions the dataset is skewed with outliers, but it's not clear which field that applies to. I'll need to think through the implications of each answer choice.

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Carla

11 months ago

This question seems straightforward, but I want to make sure I understand the context. Is the skewed dataset the input data or the target variable?

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Christiane

11 months ago

This seems straightforward, but I want to be careful not to overlook anything. I'll methodically go through each of the listed settings and make sure they're all properly configured.

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Domingo

11 months ago

I studied something similar about configuring Cisco ISE, and I think option B might be relevant, but I might be mixing it up with something else.

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Trevor

11 months ago

I'm leaning towards A, but I want to double-check my understanding. The RADIUS server is responsible for authenticating users and devices, right? That's a key component of WPA2 Enterprise.

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Nan

2 years 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

2 years ago

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

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Amber

2 years ago

Removing outliers can help improve the accuracy of the model.

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Novella

2 years ago

A) Remove the outlier rows.

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Norah

2 years 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

2 years 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

2 years ago

C) Change the method of binning to fixed width.

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Mary

2 years ago

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

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Lelia

2 years ago

A) Remove the outlier rows.

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Sherell

2 years ago

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

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King

2 years ago

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

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Viva

2 years 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

2 years 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

2 years ago

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

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Daisy

2 years ago

A) Remove the outlier rows.

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Isabelle

2 years ago

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

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Arthur

2 years ago

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

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Chaya

2 years ago

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

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Vivan

2 years ago

I agree, removing outliers is important for accurate results.

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Francisca

2 years ago

I think we should remove the outlier rows.

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