- A
Amazon Comprehend
Comprehend can perform custom text classification to categorize emails into predefined topics.
- B
Amazon Transcribe
Why wrong: Transcribe is for converting speech to text, not text classification.
- C
Amazon Polly
Why wrong: Polly is for text-to-speech conversion.
- D
Amazon Rekognition
Why wrong: Rekognition is for image and video analysis.
Question 356 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to automatically categorize customer feedback emails into topics such as 'billing', 'technical support', and 'general inquiry'. Which AWS service is MOST appropriate?
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
✓
Amazon Comprehend
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract insights from text, including custom classification. It can be trained to automatically categorize customer feedback emails into predefined topics like 'billing', 'technical support', and 'general inquiry' by analyzing the text content and assigning it to the most relevant category.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon Comprehend
Why this is correct
Comprehend can perform custom text classification to categorize emails into predefined topics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Transcribe
Why it's wrong here
Transcribe is for converting speech to text, not text classification.
- ✗
Amazon Polly
Why it's wrong here
Polly is for text-to-speech conversion.
- ✗
Amazon Rekognition
Why it's wrong here
Rekognition is for image and video analysis.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between its AI services that process text (Comprehend) versus those that process audio (Transcribe), speech generation (Polly), or images (Rekognition), leading candidates to confuse 'understanding text' with 'converting audio to text' or 'generating speech'.
Detailed technical explanation
How to think about this question
Amazon Comprehend's custom classification uses a supervised learning approach where you provide a training dataset of labeled text examples (e.g., emails tagged as 'billing' or 'support'). Under the hood, it leverages a neural network-based model that learns semantic patterns and key phrases specific to each category, enabling it to classify new, unseen emails with high accuracy. A real-world scenario where this matters is handling high-volume customer support tickets: Comprehend can automatically route emails to the correct team, reducing manual sorting and response times.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon Comprehend — Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract insights from text, including custom classification. It can be trained to automatically categorize customer feedback emails into predefined topics like 'billing', 'technical support', and 'general inquiry' by analyzing the text content and assigning it to the most relevant category.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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