AI-102 Implement computer vision solutions Practice Question
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?
⚠ Common exam trap
Test-takers frequently 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.
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
✓
The training data does not adequately represent peak-hour scenarios.
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model is not being batch-processed for inference.
Why it's wrong here
Batch processing affects throughput and request grouping, not per-image detection accuracy; inference mode does not change how many people the model detects in a frame. It is tempting because batching is a genuine latency and cost optimisation, and would be correct when tuning endpoint throughput rather than diagnosing detection accuracy.
- ✓
The training data does not adequately represent peak-hour scenarios.
Why this is correct
Persistent underestimation despite retraining indicates the training data under-represents peak-hour conditions such as crowding and occlusion, so the model never learns those patterns. The cause is a data representation gap, not model architecture or inference configuration.
- ✗
The model is overfitting to the training data.
Why it's wrong here
Overfitting would degrade generalisation across all conditions, not selectively during peak occupancy; retraining with more data would typically worsen or not fix a systematic underestimation tied to crowd density. It is tempting because overfitting is a common cause of poor model accuracy, and would be correct if validation loss diverged from training loss.
- ✗
The Computer Vision API version is outdated.
Why it's wrong here
An outdated API version would affect all requests uniformly, not specifically peak-hour counts, and version drift does not explain density-dependent underestimation. It is tempting because version mismatches do cause unexpected behaviour, and would be correct if a newer API version introduced the detection capability the model requires.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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