Quick Answer
The answer is random cropping and rescaling, as it is the augmentation technique least likely to preserve the label. This is because random cropping can sever the primary object from its context, isolating only background or a non-discriminative part, while rescaling further distorts proportions—together, these can remove the core features a model relies on for correct classification, effectively changing the semantic meaning of the image. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of label-preserving versus label-altering augmentations, a key distinction when combating overfitting with small datasets; a common trap is assuming all geometric transforms are safe, but cropping uniquely risks discarding the subject entirely. For a memory tip, remember that cropping can “cut out the class”—if you remove the dog’s head, the label ‘dog’ no longer fits, whereas rotation, flipping, or adding noise keep the subject intact.
This AI0-001 practice question tests your understanding of ai models and data engineering. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 deep learning model for image classification is overfitting due to a small dataset. The team decides to apply data augmentation. Which augmentation technique is least likely to preserve the label?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Adding random noise
Why wrong: Noise may degrade quality but typically does not change the object class.
Random rotation
Why wrong: Rotation usually preserves the object's identity unless it's orientation-sensitive (e.g., digits).
Random cropping and rescaling
Cropping might remove the object of interest, rendering the label invalid.
Horizontal flip
Why wrong: Horizontal flip is symmetric for many objects and preserves label.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Random cropping and rescaling
Random cropping and rescaling is least likely to preserve the label because it can cut out the primary object or distort its proportions, potentially removing the discriminative features needed for correct classification. For example, cropping a dog image to show only the background or a leg could change the semantic meaning, making the label 'dog' incorrect. In contrast, other techniques like noise, rotation, or flipping typically retain the core subject and its label.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Adding random noise
Why it's wrong here
Noise may degrade quality but typically does not change the object class.
Random rotation
Why it's wrong here
Rotation usually preserves the object's identity unless it's orientation-sensitive (e.g., digits).
Random cropping and rescaling
Why this is correct
Cropping might remove the object of interest, rendering the label invalid.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Horizontal flip
Why it's wrong here
Horizontal flip is symmetric for many objects and preserves label.
Common exam traps
CompTIA often tests the misconception that all augmentations are equally label-preserving, but the trap here is that random cropping can alter the semantic content by removing the object, while other transformations like rotation or flipping maintain the object's presence and identity.
Detailed technical explanation
Data augmentation works by applying label-preserving transformations to increase dataset diversity and reduce overfitting. Under the hood, random cropping can be dangerous because it may remove the object of interest entirely, especially in small or centered datasets; rescaling then amplifies irrelevant background. In real-world scenarios, such as medical imaging, cropping a tumor region out of a scan would mislabel the image as healthy, leading to catastrophic model errors.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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What to study next
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FAQ
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: Random cropping and rescaling — Random cropping and rescaling is least likely to preserve the label because it can cut out the primary object or distort its proportions, potentially removing the discriminative features needed for correct classification. For example, cropping a dog image to show only the background or a leg could change the semantic meaning, making the label 'dog' incorrect. In contrast, other techniques like noise, rotation, or flipping typically retain the core subject and its label.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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