- A
Adding watermarks to images for copyright protection
Why wrong: Watermarking is content protection — labeling adds ground-truth annotations for model training.
- B
Adding ground-truth annotations to training images so the model learns what to predict
Labeling provides correct answers for each training example — the model learns to predict those labels from the images.
- C
Compressing images to reduce storage costs during training
Why wrong: Image compression affects storage — labeling adds semantic meaning to images for supervised learning.
- D
Filtering out low-quality or blurry training images
Why wrong: Image quality filtering is data preprocessing — labeling assigns semantic annotations to images for training.
Question 95 of 1,020
The Role of Training Data Labeling in Computer Vision Model Development
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. 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.
What is the purpose of training data labeling in computer vision model development?
Quick Answer
The answer is that training data labeling adds ground-truth annotations to training images so the model learns what to predict. This process is essential in supervised learning for computer vision because it provides the algorithm with the correct output for each input—whether bounding boxes for object detection, segmentation masks for pixel-level classification, or simple class labels for image categorization. Without these labeled examples, the model cannot learn the mapping from image features to the desired predictions, making training impossible. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how supervised learning differs from unsupervised approaches; a common trap is confusing labeling with data augmentation or preprocessing. Remember the memory tip: “Labels are the answer key—without them, the model is just guessing.”
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
✓
Adding ground-truth annotations to training images so the model learns what to predict
Training data labeling is the process of adding ground-truth annotations (e.g., bounding boxes, segmentation masks, or class labels) to each training image. This supervised learning step provides the model with the correct answer for each example, enabling it to learn the mapping from image features to the desired output during training. Without labeled data, the model cannot be trained to recognize objects, classify scenes, or detect anomalies in computer vision tasks.
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.
- ✗
Adding watermarks to images for copyright protection
Why it's wrong here
Watermarking is content protection — labeling adds ground-truth annotations for model training.
- ✓
Adding ground-truth annotations to training images so the model learns what to predict
Why this is correct
Labeling provides correct answers for each training example — the model learns to predict those labels from the images.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Compressing images to reduce storage costs during training
Why it's wrong here
Image compression affects storage — labeling adds semantic meaning to images for supervised learning.
- ✗
Filtering out low-quality or blurry training images
Why it's wrong here
Image quality filtering is data preprocessing — labeling assigns semantic annotations to images for training.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse data cleaning (filtering bad images) or data preprocessing (compression) with the core supervised learning requirement of providing ground-truth annotations, leading them to select options that describe peripheral data management tasks rather than the essential labeling step.
Detailed technical explanation
How to think about this question
In Azure Custom Vision or Computer Vision services, labeling typically involves drawing bounding boxes around objects or assigning image-level tags via the Azure Machine Learning data labeling tool. The labeled dataset is then used to train a model using supervised learning algorithms such as convolutional neural networks (CNNs), where the loss function (e.g., cross-entropy) measures the difference between the model's prediction and the ground-truth label. A real-world scenario is training a defect detection model on a factory assembly line, where each image must be labeled with the exact pixel region of a scratch or dent to teach the model to localize defects.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Adding ground-truth annotations to training images so the model learns what to predict — Training data labeling is the process of adding ground-truth annotations (e.g., bounding boxes, segmentation masks, or class labels) to each training image. This supervised learning step provides the model with the correct answer for each example, enabling it to learn the mapping from image features to the desired output during training. Without labeled data, the model cannot be trained to recognize objects, classify scenes, or detect anomalies in computer vision tasks.
What should I do if I get this AI-900 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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Last reviewed: Jun 11, 2026
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