AIF-C01 Fundamentals of AI and ML Practice Question
A company is using Amazon Rekognition to detect objects in images. They find that the service sometimes mislabels objects. What is the best way to improve accuracy for their specific use case?
⚠ Common exam trap
Watch out — candidates often assume increasing the confidence threshold is a universal fix for accuracy issues, but the AIF-C01 exam tests the understanding that pre-trained services have limitations and that custom training (via SageMaker) is required for domain-specific improvements.
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 SageMaker to build a custom model
Amazon Rekognition is a pre-trained service that may not perform optimally for specialized or domain-specific use cases. By using Amazon SageMaker to build a custom model, you can train a model on your own labeled dataset, which directly addresses the mislabeling issue by tailoring the model to your specific images and objects.
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 a larger image size
Why it's wrong here
Rekognition resizes input internally, so larger images do not change inference; accuracy on domain-specific objects requires Custom Labels training. It is tempting because higher resolution genuinely helps some vision pipelines, but Rekognition's fixed preprocessing means the correct fix is a custom model trained on the company's own labelled images.
- ✗
Contact AWS support
Why it's wrong here
Support cannot retrain Rekognition's shared model on the company's data; mislabelling of domain-specific objects is not a service defect. It is tempting because support genuinely resolves service errors and quota issues, but the correct fix here is Amazon Rekognition Custom Labels trained on the company's own labelled images.
- ✗
Increase the confidence threshold
Why it's wrong here
Raising the confidence threshold only suppresses low-confidence predictions, reducing recall without correcting the underlying misclassifications. It is tempting because it genuinely filters noisy output, but the correct fix is Rekognition Custom Labels, which retrains on the company's own labelled images to improve accuracy for that use case.
- ✓
Use Amazon SageMaker to build a custom model
Why this is correct
Rekognition's pre-trained labels cannot be tuned to niche classes, so mislabelling persists. SageMaker lets you train a custom model on your own annotated images, matching the exact object categories and visual conditions of your use case, which directly addresses the accuracy constraint.
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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.