Salesforce Certified CRM Analytics and Einstein Discovery Consultant (Analytics-Con-201) Exam - Topic 3 Question 14 Discussion

Actual exam question for Salesforce's Salesforce Certified CRM Analytics and Einstein Discovery Consultant (Analytics-Con-201) exam

Question #: 14
Topic #: 3

[All Salesforce Certified CRM Analytics and Einstein Discovery Consultant (Analytics-Con-201) Questions]

A CRM Analytics consultant is reviewing results from an Einstein Discovery story with a business user. They agree with the findings but notice that none of the fields used in the story have a correlation value greater than 4%. The client is now concerned that the model

may not be good enough to deploy.

Which action should the consultant take?

AA Identify additional data that may have a stronger relationship with the outcome variable.

BEdit the model accuracy settings and rerun it to evaluate the correlation.

CUse the appropriate algorithm and update the model.

Show Suggested Answer

Suggested Answer: A

by Luisa at Oct 24, 2024, 02:03 PM

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Arminda

3 months ago

Using a different algorithm might be the way to go!

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Quinn

3 months ago

Wait, can we really trust a model with such low correlation?

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Ashleigh

3 months ago

I think editing the model settings could help too.

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Josefa

4 months ago

Totally agree, they need to find better data!

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Hannah

4 months ago

Correlation values under 4% are pretty low.

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Sommer

4 months ago

I feel like we should definitely look for more relevant data first, but I’m not confident about the best approach to take next.

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Amos

4 months ago

This reminds me of a practice question where we had to choose the right algorithm. I wonder if using a different algorithm could make a difference here.

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Leonor

4 months ago

I'm not entirely sure, but I think editing the model accuracy settings might not actually improve the correlation.

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Dexter

5 months ago

I remember we discussed how low correlation values can indicate weak predictors, so maybe identifying additional data could help.

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Lilli

5 months ago

Using the appropriate algorithm and updating the model could be a good strategy too. I'll need to weigh the pros and cons of each approach.

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Shenika

5 months ago

I'd go with option A. Bringing in new data that's more relevant to the outcome seems like the best way to strengthen the model.

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Lenna

5 months ago

I'm a bit confused by the low correlation values. I wonder if editing the model accuracy settings could help improve the results?

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Wilda

5 months ago

Okay, let's see. I think the key is to identify additional data that could have a stronger relationship with the outcome variable. That seems like the most logical first step.

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Carmelina

5 months ago

Hmm, this is a tricky one. I'll need to think carefully about the best approach here.

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Essie

1 year ago

Did you guys hear about the data scientist who was so confident in his model's accuracy that he renamed it 'Albert Einstein'? Anyway, I agree with Gail - let's explore a new algorithm and see if that does the trick.

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Gail

1 year ago

Personally, I'd lean towards Option C. Sometimes the algorithm just doesn't fit the data, and we need to try a different approach to unlock the model's full potential.

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Ezekiel

1 year ago

Option B sounds tempting, but I'm not convinced that fiddling with the accuracy settings is the best approach here. Let's try to get to the root of the problem first.

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Tamala

1 year ago

C) Use the appropriate algorithm and update the model.

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Lauryn

1 year ago

A) Identify additional data that may have a stronger relationship with the outcome variable.

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Carmen

1 year ago

I agree, finding stronger relationships is key.

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Vinnie

1 year ago

Hmm, I think Option A is the way to go. We need to dig deeper and find that hidden gem of a dataset that can really move the needle on the model's performance.

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