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Salesforce ANC-301 Exam - Topic 3 Question 33 Discussion

Actual exam question for Salesforce's ANC-301 exam
Question #: 33
Topic #: 3
[All ANC-301 Questions]

A manager at Cloud Kicks wants to separate and analyze accounts based on numeric information of its opportunity records. The data includes things like amount, quantity of products, contacts, and

quotes.

How should the CRM Analytics consultant accomplish this?

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Suggested Answer: A, D, E

by Tien at May 23, 2024, 02:22 PM

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Talia
3 months ago
How does option A even work? Sounds confusing!
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Janna
3 months ago
Wait, are we really using buckets? That feels outdated.
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Stephaine
3 months ago
Not so sure about C, clustering can get complicated.
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Rhea
4 months ago
I agree, aggregating to account level makes sense!
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Malcom
4 months ago
Option B seems like the best choice for summarizing data.
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Audra
4 months ago
I feel like using global buckets might complicate things, but I can't recall the exact pros and cons we went over.
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Marvel
4 months ago
Clustering sounds familiar from our practice questions, but I’m uncertain about how to select the right metrics for it.
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Wenona
4 months ago
I think aggregating data to the account level could be effective, but I need to double-check if that aligns with the requirements.
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Pearlie
5 months ago
I remember we discussed using buckets to categorize data, but I'm not sure if that's the best approach for this scenario.
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Paz
5 months ago
This looks like a good opportunity to showcase my CRM Analytics skills. I'd start by aggregating the data, then use clustering to identify distinct account segments based on the numeric measures. Shouldn't be too difficult as long as I remember the key steps.
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Tyisha
5 months ago
Okay, I think I've got a plan for this. I'll create a global bucket to segment the accounts, and then I can drill down into each bucket to analyze the specific numeric measures. That should give the manager the insights they're looking for.
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Elke
5 months ago
Hmm, I'm a bit unsure about this one. I know we learned about bucketing and clustering in the course, but I'm not sure which approach would work best here. Maybe I'll try a few different methods and see what gives the most meaningful results.
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Crista
5 months ago
This seems like a straightforward data analysis problem. I'd start by aggregating the data to the account level, then use some clustering techniques to segment the accounts based on the numeric measures.
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Goldie
5 months ago
Okay, let me see here. The Voice of the Customer is all about understanding what's most important to the customer, so I'm guessing the answer is the critical-to-quality attributes. I'll mark that one and move on.
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Frederic
5 months ago
I believe the load issue with broadcasts does make it unsuitable for video, so I might lean toward A or C.
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Sarah
5 months ago
Validation text, datatype, and index - those all sound like they could be used to control database field values. I'll have to re-read the question carefully to determine which one is the best match.
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Melvin
5 months ago
I remember studying the features for headend resiliency, and I think ASA failover is definitely a contender for one of the options.
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Daniela
5 months ago
The primary care case manager part sounds familiar - that makes me lean towards option C as the correct statement.
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Bok
10 months ago
B is the way to go, for sure. Aggregating the data is the best way to get a clear picture of the account information.
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Casandra
9 months ago
B is the way to go, for sure. Aggregating the data is the best way to get a clear picture of the account information.
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Audra
9 months ago
B) Aggregate to summarize related data to account level.
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Merri
9 months ago
A) Bucket for each measure and then use a global bucket to segment accounts.
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Lillian
10 months ago
I'm leaning towards A. Bucketing the numeric data could give some interesting insights into the account segments.
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Rolland
10 months ago
C sounds interesting, but I'm not sure how well clustering would work with the data provided. Might be worth a try, though.
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Toshia
9 months ago
C) Cluster in the recipes and select the metrics used for clusterization.
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Nikita
10 months ago
A) Bucket for each measure and then use a global bucket to segment accounts.
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Lynelle
11 months ago
Hmm, I think B is the way to go. Aggregating the data to the account level seems like the most straightforward approach.
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Halina
11 months ago
I think clustering in the recipes and selecting the metrics used for clusterization would be the best approach.
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Shonda
11 months ago
I disagree, I believe we should aggregate to summarize related data to account level.
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Crissy
11 months ago
I think we should bucket for each measure and then use a global bucket to segment accounts.
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