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Salesforce ANC-201 Exam - Topic 2 Question 33 Discussion

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?
B) Cluster and C) Predict Missing Values
A) Timeseries Forecasting

Salesforce ANC-201 Exam - Topic 2 Question 33 Discussion

Actual exam question for Salesforce's ANC-201 exam
Question #: 33
Topic #: 2
[All ANC-201 Questions]

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?

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Suggested Answer: B, C

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

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Vallie
8 months ago
Wait, are we really using Cluster for this? Sounds a bit off.
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Whitney
8 months ago
I agree, Cluster makes the most sense here!
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Kasandra
8 months ago
Timeseries Forecasting? That doesn't fit this scenario at all.
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Youlanda
9 months ago
I think Predict Missing Values could work too, but not sure.
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Farrah
9 months ago
Cluster is definitely the way to go for this!
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Hui
9 months ago
I vaguely remember that clustering can help identify patterns in customer purchases, which seems relevant here.
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Lashanda
9 months ago
I feel like Timeseries Forecasting is more about trends over time, so it probably isn't the best fit for this whitespace analysis.
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Charlette
9 months ago
I remember practicing a question about predicting customer behavior, but I can't recall if that was related to missing values or clustering.
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Ozell
9 months ago
I think the Cluster transformation might be the right choice since it groups similar customers together, but I'm not entirely sure.
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Natalie
9 months ago
Predict missing values could be useful too, if there are any gaps in the customer data. But I think cluster analysis is the most straightforward solution for this dashboard requirement.
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Pamella
9 months ago
Cluster analysis is definitely the way to go. We can use that to identify groups of similar customers, and then look at their purchase histories to find common trends.
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Stefania
9 months ago
Cluster analysis seems like the most relevant approach here. We want to group similar customers together, so we can see what they've bought in common.
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Chantell
10 months ago
Hmm, this seems like a tricky one. I'm not sure if I'd go with timeseries forecasting or cluster analysis. I'll have to think it through carefully.
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Estrella
10 months ago
I'm a bit confused by this question. What exactly do they mean by "whitespace analysis"? I'm not sure which recipe transformation would be best for that.
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Lynna
10 months ago
Hmm, I'm not sure about the difference between all these types of trusts. I'll have to think this through carefully.
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Glennis
10 months ago
It seems like having complete control over the storage might be an option, but I recall our instructor saying that cloud solutions often come with some level of management.
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Delmy
10 months ago
I'm a little confused on this one. I know ICMP, IGMP, PPP, and UDP are all important network protocols, but I'm not sure which one is specifically for maintenance and error reporting. I'll have to review my notes before answering.
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Helaine
10 months ago
Treatment four might be the best fit since clearer financials can help shareholders make better decisions. But what if they want raw numbers instead?
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Shawnna
1 year 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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Deandrea
1 year ago
Cluster is the way to go, no doubt about it.
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Ardella
1 year ago
C) Predict Missing Values
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Lavonna
1 year ago
B) Cluster
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Leatha
1 year ago
A) Timeseries Forecasting
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Stephaine
1 year 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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Audry
1 year ago
Timeseries Forecasting and Predict Missing Values wouldn't be as useful as Cluster in this case.
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Meaghan
1 year ago
I agree, Cluster would help identify similar customers and their buying patterns.
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Dacia
1 year ago
I think Cluster is the best option for this scenario.
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Elbert
1 year 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
1 year ago
Let's go with Cluster then. It seems like the best option for this analysis.
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Catrice
1 year ago
I agree, clustering will give us valuable insights into customer behavior patterns.
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Shawnna
1 year ago
Cluster is definitely the way to go. It will help us group similar customers together.
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Melissia
1 year 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
1 year 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
1 year ago
Timeseries forecasting might not be the best fit for this analysis. Clustering would definitely be more helpful.
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Rasheeda
1 year ago
I agree, clustering similar customers would give a better insight into what similar customers have bought.
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Twana
1 year ago
Cluster transformation seems to be the most suitable option for this dashboard creation.
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Alfreda
1 year ago
Predicting missing values might not provide the necessary information needed for whitespace analysis.
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Lavonne
1 year ago
Timeseries Forecasting might not be the best fit for this scenario.
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Alida
1 year ago
I agree, clustering similar customers would give a better insight into what similar customers have bought.
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Annelle
1 year 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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Paul
1 year ago
I think Predict Missing Values could also be useful to fill in any gaps in the dataset.
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Aide
1 year ago
I agree with Pamella, Cluster can group similar customers together.
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Pamella
1 year ago
I think Cluster would be helpful for this analysis.
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