- A
Use Amazon Rekognition Custom Labels to train a custom model with additional labeled images
Custom Labels fine-tunes the model on your data, improving precision for specific categories.
- B
Increase the confidence threshold to 99%
Why wrong: Raising the threshold reduces false positives but may also reduce true positives – it does not improve the underlying model.
- C
Submit a request to AWS to retrain the base Rekognition model
Why wrong: AWS does not retrain base models on customer demand; the service provides Custom Labels for this purpose.
- D
Use Amazon SageMaker to build a new object detection model from scratch
Why wrong: This is possible but more complex and time-consuming than using Rekognition's built-in custom labeling capability.
Question 125 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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 uses Amazon Rekognition to detect objects in images. The model is producing a high number of false positives for a specific category. Which action should be taken to improve the model's precision for that category?
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 Rekognition Custom Labels to train a custom model with additional labeled images
Amazon Rekognition Custom Labels allows you to train a custom model using your own labeled dataset, which directly addresses the false positives for a specific category by fine-tuning the model on domain-relevant examples. This improves precision because the base Rekognition model is pre-trained on general data and may not capture the nuances of your specific category, whereas a custom model learns from your labeled images to reduce incorrect detections.
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.
- ✓
Use Amazon Rekognition Custom Labels to train a custom model with additional labeled images
Why this is correct
Custom Labels fine-tunes the model on your data, improving precision for specific categories.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the confidence threshold to 99%
Why it's wrong here
Raising the threshold reduces false positives but may also reduce true positives – it does not improve the underlying model.
- ✗
Submit a request to AWS to retrain the base Rekognition model
Why it's wrong here
AWS does not retrain base models on customer demand; the service provides Custom Labels for this purpose.
- ✗
Use Amazon SageMaker to build a new object detection model from scratch
Why it's wrong here
This is possible but more complex and time-consuming than using Rekognition's built-in custom labeling capability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume increasing the confidence threshold is a quick fix for false positives, but they overlook that this approach does not improve the model's discriminative ability and can harm recall, whereas Custom Labels directly addresses the root cause by retraining on domain-specific data.
Detailed technical explanation
How to think about this question
Amazon Rekognition Custom Labels uses transfer learning, where the base model's weights are fine-tuned on your labeled dataset, allowing the model to adapt to specific visual features while retaining general knowledge. The training process automatically handles data augmentation and hyperparameter tuning, and the resulting model can be deployed with a dedicated endpoint for real-time or batch inference. In practice, this is ideal for niche categories like rare wildlife species or specialized industrial defects where the base model's false positive rate is high due to lack of representative training data.
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 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
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: Use Amazon Rekognition Custom Labels to train a custom model with additional labeled images — Amazon Rekognition Custom Labels allows you to train a custom model using your own labeled dataset, which directly addresses the false positives for a specific category by fine-tuning the model on domain-relevant examples. This improves precision because the base Rekognition model is pre-trained on general data and may not capture the nuances of your specific category, whereas a custom model learns from your labeled images to reduce incorrect detections.
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
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