Salesforce Exam ANC-201 Topic 2 Question 33 Discussion

Actual exam question for Salesforce's ANC-201 exam

Question #: 33
Topic #: 2

Universal Containers (UC) is looking to create a dashboard for whitespace analysis. UC wants to view a particular customer and see what similar customers have bought.

Which recipe transformation is helpful for the consultant to use while creating the dataset?

ATimeseries Forecasting

BCluster

CPredict Missing Values

Show Suggested Answer

Suggested Answer: B, C

by Luisa at Apr 27, 2024, 10:24 AM

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Shawnna

1 months ago

Hey, is this exam sponsored by a fortune-telling company? Timeseries Forecasting? Really? Cluster is the way to go, no doubt about it.

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Leatha

3 days ago

A) Timeseries Forecasting

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Stephaine

1 months ago

Predict Missing Values? Is this a trick question? Clearly, Cluster is the answer. I bet the consultant has a sixth sense for these things.

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Meaghan

2 days ago

I agree, Cluster would help identify similar customers and their buying patterns.

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Dacia

15 days ago

I think Cluster is the best option for this scenario.

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Elbert

2 months ago

Timeseries Forecasting? What is this, a crystal ball? Cluster is the obvious choice here. I can already see the dashboard lighting up with customer segments.

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Timothy

20 days ago

Let's go with Cluster then. It seems like the best option for this analysis.

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Catrice

27 days ago

I agree, clustering will give us valuable insights into customer behavior patterns.

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Shawnna

1 months ago

Cluster is definitely the way to go. It will help us group similar customers together.

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Melissia

2 months ago

Predict Missing Values? Hmm, I don't think that's the best approach. Clustering similar customers sounds like the perfect recipe transformation for this use case.

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Verdell

7 days ago

I think predicting missing values might not provide the desired outcome. Clustering similar customers seems like the way to go.

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Matthew

8 days ago

Timeseries forecasting might not be the best fit for this analysis. Clustering would definitely be more helpful.

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Rasheeda

9 days ago

I agree, clustering similar customers would give a better insight into what similar customers have bought.

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Twana

15 days ago

Cluster transformation seems to be the most suitable option for this dashboard creation.

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Alfreda

16 days ago

Predicting missing values might not provide the necessary information needed for whitespace analysis.

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Lavonne

1 months ago

Timeseries Forecasting might not be the best fit for this scenario.

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Alida

2 months ago

I agree, clustering similar customers would give a better insight into what similar customers have bought.

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Annelle

2 months ago

Timeseries Forecasting? Are they looking to predict the future? I think Cluster is the way to go here. Grouping similar customers seems more relevant for a whitespace analysis dashboard.

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