AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 Azure Custom Vision to build a classifier for defect detection on a manufacturing line. They have labeled images of products with and without defects. Which TWO actions should they take to improve model performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use images with balanced numbers of defect and non-defect samples.
Option B is correct because balanced datasets prevent the model from becoming biased toward the majority class (e.g., non-defect images), which is critical for defect detection where defects are rare. Azure Custom Vision uses a weighted loss function during training, and class imbalance can cause the model to predict the majority class for most inputs, reducing recall for defects. Balanced samples ensure the model learns discriminative features for both classes equally.
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.
✗
Train for more iterations without validation.
Why it's wrong here
No validation increases risk of overfitting.
✓
Use images with balanced numbers of defect and non-defect samples.
Read the scenario before looking for a memorised answer.
✗
Set the learning rate manually using the Custom Vision API.
Why it's wrong here
Custom Vision does not expose learning rate for manual tuning.
✓
Increase the number of images per tag, including variations in lighting and angle.
Why this is correct
More diverse data improves robustness.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the number of images per tag to avoid overfitting.
Why it's wrong here
Reducing data harms generalization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think reducing images prevents overfitting (Option E) or that manual learning rate tuning (Option C) is possible in Custom Vision, but the service abstracts hyperparameter tuning and requires sufficient, varied data for robust defect detection.
Detailed technical explanation
How to think about this question
Azure Custom Vision uses a transfer learning approach based on a pre-trained deep neural network (e.g., ResNet or MobileNet) and fine-tunes it on the user's dataset. The service automatically applies data augmentation (e.g., random crops, flips, color jitter) during training to improve generalization, but this cannot compensate for severe class imbalance. In real-world manufacturing, defects may occur in only 1% of images, so techniques like oversampling the minority class or using weighted loss functions are essential; Custom Vision's built-in handling of imbalanced data is limited, making balanced input data critical.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use images with balanced numbers of defect and non-defect samples. — Option B is correct because balanced datasets prevent the model from becoming biased toward the majority class (e.g., non-defect images), which is critical for defect detection where defects are rare. Azure Custom Vision uses a weighted loss function during training, and class imbalance can cause the model to predict the majority class for most inputs, reducing recall for defects. Balanced samples ensure the model learns discriminative features for both classes equally.
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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