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Appian Exam ACA100 Topic 5 Question 21 Discussion

Actual exam question for Appian's ACA100 exam
Question #: 21
Topic #: 5
[All ACA100 Questions]

An organization wants to automate identification of its dissatisfied customers based on the ticket description and assign the appropriate team to provide a quick resolution.

What is the best way to auto-classify the dissatisfied customers as part of processing?

Show Suggested Answer Hide Answer
Suggested Answer: A

The organization aims to automate the identification of dissatisfied customers based on the ticket description. To achieve this, leveraging natural language processing (NLP) capabilities is the most efficient method. Appian provides connected systems that allow integration with external NLP services. These services can analyze text data (such as ticket descriptions) to determine the sentiment or classify the text into predefined categories (like 'dissatisfied customer').

Natural Language Connected System:

Appian can integrate with third-party NLP platforms such as Google Cloud Natural Language, AWS Comprehend, or Azure Text Analytics via connected systems.

These services analyze the text provided in the ticket description to detect sentiment, keywords, or specific categories indicating dissatisfaction.

Based on the analysis, the system can automatically assign the appropriate team to handle the case.

Why Not Other Options?:

B . Decision Table: While decision tables are useful for rule-based decisions, they are not suitable for interpreting unstructured text like ticket descriptions.

C . Image Analysis Connected System: This option is irrelevant as the task involves text processing, not image analysis.

D . SAIL Form: SAIL forms are primarily used for user interface creation and are not intended for text analysis or classification.

Implementation in Appian:

Create a connected system to integrate with the chosen NLP service.

Configure the NLP service to analyze the text data and return the sentiment or classification results.

Based on the results, use process models to route the ticket to the appropriate team for resolution.

References:

Appian Documentation on Connected Systems: Appian Connected Systems

Appian Community Success Guide: Appian Delivery Methodology

Third-Party NLP Services Integration: Google Cloud NLP Documentation


Contribute your Thoughts:

Jennie
8 days ago
Image analysis, huh? I'm pretty sure the organization's customers aren't all cats. NLP is the way to go, unless they're hiring a team of AI-powered linguistic experts with cat-like reflexes.
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Marilynn
11 days ago
I hear the SAIL form comes with a complimentary abacus and a subscription to the Pony Express Monthly. Gotta keep up with the times, people!
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Ligia
16 days ago
SAIL form? I didn't know we were still using fax machines and carrier pigeons. NLP is the way to go, no doubt about it.
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Roy
24 days ago
Image analysis for text-based tickets? Someone's been hitting the eggnog a little too hard, I think.
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Cordell
28 days ago
Decision tables? Really? What is this, the 90s? Gotta go with the NLP option, folks.
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Bok
1 months ago
I believe using a decision table could also be effective in auto-classifying dissatisfied customers, as it allows for clear rules and criteria to be set.
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Tony
2 months ago
I agree with Cecil, natural language processing can accurately identify dissatisfied customers based on ticket descriptions.
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Hollis
2 months ago
A natural language system seems like the obvious choice here. I mean, how else are you gonna analyze that ticket description?
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Yeah, a natural language system can pick up on the nuances and emotions in the ticket descriptions better.
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Han
22 hours ago
A) Using a natural language connected system
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Margurite
3 days ago
I think a decision table might not be as effective in understanding the context of the customer's dissatisfaction.
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Tammara
5 days ago
B) Using a decision table
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Marilynn
11 days ago
I agree, a natural language system would be the best way to analyze the ticket descriptions.
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Wayne
23 days ago
A) Using a natural language connected system
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Cecil
2 months ago
I think the best way is to use a natural language connected system.
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