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Salesforce ANC-201 Exam - Topic 2 Question 29 Discussion

Actual exam question for Salesforce's ANC-201 exam
Question #: 29
Topic #: 2
[All ANC-201 Questions]

A customer has a dataset consisting of over 300 unique product names. They request a prediction model with the product names included.

Which action should the Einstein Consultant take?

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Suggested Answer: D

by Vincenza at Mar 06, 2024, 10:16 PM

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Julio
3 months ago
Default variables might not capture all the nuances.
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Cassi
3 months ago
Wait, can you really predict with over 300 unique names?
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Hollis
3 months ago
Not sure about that, product names are important for context.
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Marylyn
4 months ago
I think splitting into multiple models could work too.
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Dalene
4 months ago
Using SKU numbers sounds like a smart move!
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Ines
4 months ago
Eliminating extreme values sounds familiar, but I can't recall if that applies directly to this scenario with product names.
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Vernell
4 months ago
I practiced a similar question where we had to decide on model variables, and I feel like running the model with default variables could be a safe option.
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Adaline
4 months ago
I think using SKU numbers might help with clarity, but I’m not completely confident if that’s the right choice for a prediction model.
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Jaime
5 months ago
I remember something about handling large datasets, but I'm not sure if splitting the analysis is the best approach.
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Earlean
5 months ago
Using SKU numbers instead of product names could be an interesting approach. That might help increase clarity and reduce noise in the model. I'll have to weigh the pros and cons of that strategy.
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Casie
5 months ago
I'm a bit confused on the best way to approach this. Adjusting the model to eliminate extreme values in the outcome variable could be an option, but I'm not sure if that's the most appropriate solution here. I'll have to think this through carefully.
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Susy
5 months ago
Hmm, this seems like a tricky one. With over 300 unique product names, I'm not sure running a single model would be the best approach. Splitting it into multiple models might be a good way to handle the high dimensionality.
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Diane
5 months ago
Okay, I think the key here is to avoid overfitting the model. Running it with the default variables in the Product object might be a good starting point, but I'd want to explore feature engineering and selection to optimize performance.
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Chaya
5 months ago
Based on Roberta's findings about inadequate access rules and outdated data, I'd say the most likely cause is mishandling of information due to those security weaknesses. That seems to be the core problem the company was facing.
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Judy
5 months ago
Okay, let's see. Adding a nameserver or changing the VRF could help with connectivity, but I'm not sure if that's the root cause here. I'll have to think it through.
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Louis
5 months ago
I'm a bit confused by the terminology here. I'll need to review my notes on linked clones and caching before attempting this.
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Fabiola
5 months ago
This is a tricky one. I'm tempted to say ClearPass Guest, since that's where you'd manage guest devices. But the question is specifically asking about detailed OS and status info, so I'm thinking it's probably ClearPass OnGuard. I'll mark that as my answer for now, but I'll double-check my understanding just to be sure.
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Alease
5 months ago
Okay, I've got this. Capacity is all about the maximum output a system or resource can produce in a given time period. I think the answer is A - Capacity available.
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Flo
2 years ago
Option C is tempting, but I have a feeling that the default variables in the Product object won't be enough to get a good prediction model. Gotta go with A on this one.
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Jacquelyne
2 years ago
Haha, this is a tough one. I bet the Einstein Consultant is just sitting there scratching their head, wondering how to tackle this monster dataset!
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Janet
2 years ago
B) Adjust the model to eliminate extreme values in the outcome variable.
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Melita
2 years ago
A) Split the analysis into multiple models will each having fewer products
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Tanja
2 years ago
I don't know, D seems like the way to go. Using SKU numbers instead of product names just makes more sense to me.
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Dana
2 years ago
D does seem like a practical solution, it would simplify the prediction model.
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Rene
2 years ago
I agree, it would be more efficient to use SKU numbers instead of product names.
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Sarah
2 years ago
I think D is a good idea, using SKU numbers would definitely make things clearer.
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Geoffrey
2 years ago
That's a good point, using SKU numbers would definitely increase clarity.
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Johnna
2 years ago
I suggest using SKU numbers instead of product names, that would simplify the model.
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Jacquline
2 years ago
It might, but it will make it easier to handle a large number of unique product names.
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Bernardine
2 years ago
Option A makes sense, splitting the analysis into multiple models would be more manageable with such a large dataset.
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Helaine
2 years ago
That's a good idea, it would definitely make the analysis more manageable.
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Garry
2 years ago
A) Split the analysis into multiple models will each having fewer products
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Sheridan
2 years ago
Agreed, splitting the analysis into multiple models will make it more manageable.
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Ira
2 years ago
I think option A is the best approach for handling a dataset with over 300 unique product names.
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Geoffrey
2 years ago
But wouldn't that increase complexity?
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Jacquline
2 years ago
I think we should split the analysis into multiple models.
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