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AIF-C01 Practice Question: Build a system that automatically routes support…
A company wants to build a system that automatically routes support tickets to the appropriate department based on the ticket text. The system must handle new categories that emerge over time without retraining. Which TWO approaches should the company combine to achieve this? (Select TWO.)
⚠ Common exam trap
The trap is that candidates may select Comprehend topic modeling (C) because it sounds like it handles new categories without retraining, but topic modeling does not perform routing to departments; the correct approach combines Comprehend custom classification (B) with Kendra (E) for semantic search-based routing.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon Comprehend for custom classification
Option B is correct because Amazon Comprehend custom classification can categorize ticket text into departments, providing the routing decision. Option E is correct because Amazon Kendra indexes support documents and uses semantic search to match ticket text to the most relevant department knowledge base, allowing new or emerging categories to be handled through indexed content without retraining the classifier. Together, Comprehend custom classification and Kendra enable routing that adapts to new categories by using Kendra to surface relevant department knowledge for unfamiliar tickets. Option A is incorrect because Amazon Forecast predicts time-series values like ticket volume, not ticket content or routing. Option C is incorrect because Comprehend topic modeling and entity detection discover topics and entities but do not directly route tickets to departments. Option D is incorrect because Amazon Rekognition analyzes images and video, not support ticket text.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon Forecast to predict ticket volume
Why it's wrong here
Forecast predicts time-series data, not text classification.
- ✓
Use Amazon Comprehend for custom classification
Why this is correct
Custom classification requires retraining for new categories, which does not meet the requirement.
- ✗
Use Amazon Comprehend for topic modeling and entity detection
Why it's wrong here
Comprehend topic modelling clusters ticket text into emergent themes and entity detection extracts key terms, allowing routing categories to be discovered from data rather than fixed labels. This satisfies the requirement to handle new categories without retraining a custom model.
- ✗
Use Amazon Rekognition to analyze ticket images
Why it's wrong here
Rekognition is for image/video analysis; tickets are text-based.
- ✓
Use Amazon Kendra to index support documents and perform intelligent search
Why this is correct
Kendra indexes support documents and applies semantic search so incoming ticket text can be matched to the most relevant department content. Because relevance is computed at query time from the index, newly emerging categories are handled without retraining any model.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.