A retail company uses Azure Computer Vision to analyze customer traffic in stores. They deploy a custom object detection model to count customers and detect occupancy. After deployment, the model consistently underestimates the number of customers during peak hours. The company has retrained the model with more data but the issue persists. What is the most likely cause?
Data drift or lack of representative samples for peak hours leads to underestimation during those times.
Why this answer
The model consistently underestimates customer counts during peak hours, which indicates a distribution shift between the training data and the inference environment. Even after retraining with more data, the issue persists because the additional data likely still lacks sufficient representation of peak-hour scenarios (e.g., high density, occlusion, rapid movement). In Azure Custom Vision, object detection models learn from labeled examples; if the training set does not include diverse peak-hour images with varied lighting, crowd densities, and angles, the model will fail to generalize to those conditions.
Exam trap
The trap here is that candidates may assume retraining with 'more data' automatically fixes the issue, but the key is that the additional data must be representative of the specific failure scenario (peak hours), not just any data.
How to eliminate wrong answers
Option A is wrong because batch processing affects throughput and latency, not the accuracy of individual inference results; the model's underestimation is a precision/recall issue, not a processing mode issue. Option C is wrong because overfitting would cause the model to perform well on training data but poorly on new data in general, not specifically during peak hours; the consistent underestimation only in peak hours points to a data distribution mismatch, not overfitting. Option D is wrong because the Computer Vision API version affects available features and endpoints, not the learned weights of a custom object detection model; the model's behavior is determined by its training data and architecture, not the API version used for deployment.