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Salesforce Exam ANC-301 Topic 5 Question 27 Discussion

Actual exam question for Salesforce's ANC-301 exam
Question #: 27
Topic #: 5
[All ANC-301 Questions]

After the initial creation of a story, the first story insight explains 93% of the variation of the outcome variable. This is unusual high?

What is the most likely multiple for this?

Show Suggested Answer Hide Answer
Suggested Answer: A

Contribute your Thoughts:

Ashlee
3 months ago
Too many rows? That's like having too much chocolate cake - it's a good problem to have, but you've gotta pace yourself, you know?
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Melina
3 months ago
Wait, data leakage? That's like the Bermuda Triangle of data science - you never know what's going to happen. I hope they had a good data hygiene regime in place.
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Jess
3 months ago
I bet the dataset has some nasty skewness issues. Gotta watch out for those outliers, they can really mess up your models.
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Rosann
3 months ago
Yeah, outliers can definitely throw things off. Skewness can be a real problem too.
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Shelia
3 months ago
C) The dataset used in the story suffers from too many outlier values.
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Stephanie
3 months ago
A) The dataset contains multiple dominant values.
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Nakisha
3 months ago
A) The dataset contains multiple dominant values.
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Cecily
3 months ago
C) The dataset used in the story suffers from too many outlier values.
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Devora
3 months ago
C) The dataset used in the story suffers from too many outlier values.
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Halina
3 months ago
A) The dataset contains multiple dominant values.
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Felicidad
3 months ago
I think it's possible that the outcome variable is causing data leakage.
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Remedios
4 months ago
It could be because the dataset contains too many outlier values.
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Karl
4 months ago
Hmm, this seems like a case of data overfitting. The outcome variable is probably too tightly coupled with the predictors. Did they try cross-validation?
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Francesco
3 months ago
Definitely, cross-validation is important to avoid overfitting.
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Ayesha
3 months ago
Yes, cross-validation could help in this case.
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Alease
3 months ago
Hmm, it does sound like data overfitting.
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Tammy
3 months ago
D) The outcome variable is causing data leakage.
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Gerry
3 months ago
C) The dataset used in the story suffers from too many outlier values.
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Glory
3 months ago
B) The dataset contains too many rows.
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Merilyn
4 months ago
A) The dataset contains multiple dominant values.
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Ozell
4 months ago
Yeah, that does seem unusually high. I wonder what could be causing it.
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Carylon
4 months ago
Wow, 93% of the variation explained in the first story insight is impressive!
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