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Pegasystems PEGACPDS88V1 Exam - Topic 7 Question 22 Discussion

Actual exam question for Pegasystems's PEGACPDS88V1 exam
Question #: 22
Topic #: 7
[All PEGACPDS88V1 Questions]

To optimize their customer interactions, U+ Bank routes all emails that are complaints to a specialized department. To identify emails that voice a complaint, the text prediction uses___________

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Suggested Answer: A

To identify emails that voice a complaint, the text prediction usesan entity extraction model.


by Angelo at Sep 19, 2024, 02:03 PM

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Francine
3 months ago
I doubt a sentiment model is the best choice here.
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Elden
3 months ago
Wait, are we sure it's not a language model?
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Kris
3 months ago
A topic model seems more fitting, right?
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Felton
4 months ago
I think it's an entity extraction model.
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Cordelia
4 months ago
Definitely a sentiment model!
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My
4 months ago
I’m leaning towards a sentiment model too, but I wonder if a language model could also help in understanding the context of the complaints.
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Arlen
4 months ago
I feel like an entity extraction model could work, but it seems more focused on specific data points rather than overall sentiment.
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Willis
4 months ago
I remember practicing a question about topic modeling, but that doesn't seem right for identifying complaints.
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Blair
5 months ago
I think it might be a sentiment model since we're trying to identify complaints, but I'm not entirely sure.
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Narcisa
5 months ago
I'm pretty confident that the answer is a sentiment model. Complaints are inherently about negative experiences, so a model that can detect sentiment would be the most effective way to identify them in email text. The other options like entity extraction or topic modeling don't seem as directly relevant to this use case.
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Corazon
5 months ago
Okay, let me think this through step-by-step. The question is asking about how U+ Bank identifies complaint emails, so we need a model that can detect that kind of language. A sentiment model seems like the most direct approach, as it would be able to identify negative or complaint-related sentiment in the text. I'm going to go with option D.
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Teri
5 months ago
Hmm, I'm a bit unsure about this one. I can see the logic behind a sentiment model, but I'm also wondering if an entity extraction model or a language model could be used to identify complaint-related language. I'll need to think this through a bit more carefully.
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Jacquelyne
5 months ago
I think this is a pretty straightforward question. The key is to identify the type of model that would be used to detect complaints in the email text. I'm leaning towards option D, a sentiment model, since that seems most directly relevant to identifying complaints.
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Josephine
5 months ago
I'm pretty sure the answer is C. Search heads are responsible for running the actual searches and returning the results, so that seems like the right component.
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William
1 year ago
I'm going with option C. A language model can probably do a better job of understanding the nuance and context in those complaints. Plus, it adds a touch of linguistic flair to the whole process.
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Denny
1 year ago
I'm leaning towards option B, a topic model, to categorize the complaints effectively.
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Shawnee
1 year ago
I agree with option D, a sentiment model, as it can detect the emotions behind the complaints.
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Nieves
1 year ago
I think option A, an entity extraction model, would be more accurate in identifying complaints.
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Shawn
1 year ago
Hmm, that's interesting. Can you explain why you think it's a sentiment model?
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Stefany
1 year ago
The sentiment model seems like the obvious choice here. I mean, who doesn't love a good old-fashioned emotional roller coaster when dealing with customer complaints?
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Nieves
1 year ago
D) a sentiment model
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Staci
1 year ago
C) a language model
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Demetra
1 year ago
B) a topic model
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Joesph
1 year ago
A) An entity extraction model
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Jerry
1 year ago
I disagree, I believe the answer is D) a sentiment model.
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Shawn
1 year ago
I think the answer is A) An entity extraction model.
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