Pegasystems PEGACPDS88V1 Exam - Topic 7 Question 45 Discussion

Actual exam question for Pegasystems's PEGACPDS88V1 exam

Question #: 45
Topic #: 7

A telecom company is interested in improving customer engagement on social medi

a. However, there are hundreds of relevant messages posted every day, and it is not practical for customer service representatives (CSRs) to review and respond to all messages. Instead, CSRs should focus on negative messages. What do you need to analyze the incoming messages?

CText categorization model

DText extraction model

BAdaptive model

APredictive model

Show Suggested Answer

Suggested Answer: C

A text categorization model is a type of text analytics model that can analyze the incoming messages and assign them to predefined categories, such as positive, negative, or neutral sentiment. This way, CSRs can focus on negative messages that require immediate attention or escalation. Reference: https://academy.pega.com/module/text-analytics/topic/creating-text-categorization-model

by Lashaunda at Jan 22, 2026, 05:09 PM

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Stefania

4 hours ago

Definitely! Prioritizing responses can improve customer satisfaction.

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Yvonne

5 days ago

We also need a good tagging system for categorizing issues.

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Katie

10 days ago

Agreed! They can filter out negative messages quickly.

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Elsa

16 days ago

I think sentiment analysis tools are essential.

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Annamaria

2 months ago

Wait, are they really only going to ignore positive comments?

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Keneth

2 months ago

Machine learning can help prioritize responses effectively.

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An

2 months ago

But what if they miss positive messages that could help?

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Ria

3 months ago

Totally agree, focusing on negative feedback is key!

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Brandon

3 months ago

They should use sentiment analysis tools to filter messages.

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Veda

3 months ago

Sentiment analysis is the obvious choice. Gotta identify those unhappy customers and give 'em some attention.

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Tamar

3 months ago

Forget the tech, just hire a bunch of interns to read through everything. Cheap labor is the solution!

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Luis

3 months ago

Machine learning algorithms are the key here. Train 'em on a dataset of messages and let the AI do the work.

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Olga

3 months ago

Definitely need some natural language processing to sort through all those messages. Can't have the CSRs going through them one by one.

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Lashon

4 months ago

Sentiment analysis would be the way to go. Gotta focus on those negative messages!

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Lisandra

4 months ago

I practiced a similar question about prioritizing customer feedback. It seems like categorizing messages could help CSRs focus on the most urgent issues.

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Eliz

4 months ago

We might also need to analyze the volume of messages over time to see if there are specific trends or spikes in negative feedback.

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Joana

5 months ago

I'm not entirely sure, but I remember something about using keywords to identify issues. Maybe we could create a list of common complaints?

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Lili

5 months ago

I think we need to implement some sort of sentiment analysis tool to filter out the negative messages from the positive ones.

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Edmond

5 months ago

This is a great opportunity to apply some of the concepts we've learned. I'm feeling pretty confident I can come up with a solid approach to tackle this problem.

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Aileen

5 months ago

Sounds like we need a robust text analytics solution to handle this. I'll need to research the latest techniques in natural language processing and machine learning.

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