The answer is to add images with different lighting conditions to the training set. This is correct because the model suffers from a data distribution mismatch—it learned features from well-lit images but lacks exposure to low-light patterns, so enriching the dataset with diverse lighting directly enables the model to generalize to darker scenes. On the AI-102 exam, this tests your understanding that Custom Vision models are fundamentally limited by training data quality, not by model architecture or hyperparameters; a common trap is to suggest adjusting the probability threshold or applying preprocessing filters, which only mask the root cause. For improving Custom Vision low light data augmentation, remember that the model learns what it sees—if you want it to detect vehicles at night, you must show it night images. Memory tip: “Garbage in, garbage out—feed it the dark, and it will see in the dark.”
AI-102 Plan and manage an Azure AI solution Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
You deploy a Custom Vision object detection model to classify vehicles. The model works well in good lighting but fails in low-light conditions. What is the most appropriate action?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add images with different lighting conditions to the training set
Option A is correct because the core issue is a data distribution mismatch: the model was trained primarily on well-lit images and lacks exposure to low-light examples. Adding images with diverse lighting conditions directly addresses this by enriching the training dataset, enabling the model to learn robust features for low-light scenarios. This aligns with the fundamental principle that Custom Vision models are only as good as the training data they receive.
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.
✓
Add images with different lighting conditions to the training set
Why this is correct
Including low-light images trains the model to handle such conditions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the probability threshold
Why it's wrong here
Threshold adjustments affect confidence, not model robustness to lighting.
✗
Increase the number of training iterations
Why it's wrong here
More iterations do not compensate for lack of diverse training data.
✗
Use a domain-specific model for vehicles
Why it's wrong here
Domain-specific models are pre-trained but may still need diverse lighting data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse model performance tuning (threshold, iterations) with data quality issues, mistakenly believing that adjusting hyperparameters can compensate for missing training scenarios.
Detailed technical explanation
How to think about this question
Under the hood, Custom Vision uses transfer learning from a pre-trained convolutional neural network (e.g., ResNet or MobileNet). The model's feature extractors learn to respond to edges, textures, and shapes, but if the training set lacks low-light images, the network never learns to normalize for low contrast or high noise. In real-world deployments, adding synthetic augmentation (e.g., brightness reduction, Gaussian noise) can supplement real low-light images, but the most reliable approach is to include actual low-light captures to avoid artifacts from augmentation.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add images with different lighting conditions to the training set — Option A is correct because the core issue is a data distribution mismatch: the model was trained primarily on well-lit images and lacks exposure to low-light examples. Adding images with diverse lighting conditions directly addresses this by enriching the training dataset, enabling the model to learn robust features for low-light scenarios. This aligns with the fundamental principle that Custom Vision models are only as good as the training data they receive.
What should I do if I get this AI-102 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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Question Discussion
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