Quick Answer
The answer is to use Amazon Rekognition Custom Labels. This is the correct choice because the service enables you to train a custom machine learning model using your own labeled images, allowing it to detect domain-specific objects that Amazon Rekognition’s built-in label set does not cover. Instead of building a model from scratch, Custom Labels handles the heavy lifting of model training and tuning, making it ideal for niche object detection tasks. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of when to extend Rekognition’s capabilities versus using a fully custom solution—a common trap is confusing Custom Labels with Amazon SageMaker, but the key distinction is that Custom Labels is a managed service within Rekognition that requires no deep ML expertise. A helpful memory tip: think of “Custom Labels” as “custom labels for custom objects”—if the object isn’t in Rekognition’s default list, you need to train your own labels.
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 is using Amazon Rekognition to detect objects in images. They need to detect custom objects that are specific to their domain. What should they do?
Use Amazon Rekognition's built-in labels
Why wrong: Built-in labels only detect predefined categories.
Use Amazon SageMaker Object Detection algorithm
Why wrong: This is possible but not specific to Rekognition; Custom Labels is the managed service.
Use Amazon Rekognition Custom Labels
Custom Labels enables training a custom model with labeled images.
Use Amazon Comprehend
Why wrong: Comprehend is for NLP, not image analysis.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use Amazon Rekognition Custom Labels
Amazon Rekognition Custom Labels allows you to train a custom model using your own labeled images to detect domain-specific objects that are not covered by Rekognition's built-in labels. This is the correct service for custom object detection without needing to build a model from scratch.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use Amazon Rekognition's built-in labels
Why it's wrong here
Built-in labels only detect predefined categories.
Use Amazon SageMaker Object Detection algorithm
Why it's wrong here
This is possible but not specific to Rekognition; Custom Labels is the managed service.
Use Amazon Rekognition Custom Labels
Why this is correct
Custom Labels enables training a custom model with labeled images.
Related concept
Read the scenario before looking for a memorised answer.
Use Amazon Comprehend
Why it's wrong here
Comprehend is for NLP, not image analysis.
Common exam traps
The trap here is that candidates may confuse Amazon Rekognition Custom Labels with SageMaker Object Detection, not realizing that Custom Labels is a managed service specifically designed for custom image analysis without requiring ML expertise.
Detailed technical explanation
Amazon Rekognition Custom Labels uses transfer learning on top of Rekognition's existing deep learning models, allowing you to train a custom model with as few as 10-50 labeled images per label. The service automatically handles model training, tuning, and deployment, and you can evaluate model performance using precision, recall, and F1 scores before production use.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: Use Amazon Rekognition Custom Labels — Amazon Rekognition Custom Labels allows you to train a custom model using your own labeled images to detect domain-specific objects that are not covered by Rekognition's built-in labels. This is the correct service for custom object detection without needing to build a model from scratch.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 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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