The answer is overfitting, which is the most likely cause when a custom vision model shows high training accuracy but low test accuracy. This occurs because the model has memorized the specific patterns, noise, and irrelevant details in the training data rather than learning generalizable features, so it fails to perform well on new, unseen images. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of model evaluation and dataset quality—a common trap is assuming high training accuracy always means a good model, when in fact it signals overfitting, especially if the training set is too small, homogeneous, or lacks variation. To avoid this, always validate with a separate test set and use data augmentation in Azure AI Custom Vision to increase diversity. Remember the memory tip: “High train, low test—overfit is the pest.”
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 model using Azure AI Custom Vision. After deployment, you notice the model has high accuracy on training data but low accuracy on new images. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model is overfitted to the training data
High accuracy on training data but low accuracy on new images is the classic symptom of overfitting, where the model has memorized the training examples (including noise and irrelevant patterns) rather than learning generalizable features. In Azure AI Custom Vision, this typically occurs when the training dataset is too small, too homogeneous, or lacks sufficient variation, causing the model to fail on unseen data.
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.
✗
The training time was too short
Why it's wrong here
Short training time usually causes underfitting.
✗
The training dataset has too few images
Why it's wrong here
Too few images typically causes underfitting, not overfitting.
✗
The wrong domain was selected during training
Why it's wrong here
Wrong domain may affect features but not specifically cause overfitting.
✓
The model is overfitted to the training data
Why this is correct
Overfitting leads to high training accuracy but poor generalization.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'too few images' (a contributing factor) with the direct diagnosis of 'overfitting,' but the question asks for the most likely cause of the described symptom, which is the overfitting itself, not its root cause.
Detailed technical explanation
How to think about this question
Overfitting occurs when the model's capacity (number of parameters) exceeds the amount of informative training data, causing it to fit noise. In Custom Vision, the underlying neural network uses transfer learning from a pretrained model; if the training set is small or lacks diversity, the fine-tuning process can overfit to spurious correlations. A real-world scenario is training a defect detector on 50 images all taken under identical lighting—the model will perform perfectly on those images but fail on images from a different factory floor.
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.
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: The model is overfitted to the training data — High accuracy on training data but low accuracy on new images is the classic symptom of overfitting, where the model has memorized the training examples (including noise and irrelevant patterns) rather than learning generalizable features. In Azure AI Custom Vision, this typically occurs when the training dataset is too small, too homogeneous, or lacks sufficient variation, causing the model to fail on unseen data.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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