AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is the purpose of training data labeling in computer vision model development?
⚠ Common exam trap
Test-takers frequently 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.
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.
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 overlays a logo or ownership text onto an image to protect intellectual property, not to teach a model anything. Training labels are externally defined metadata that tell the model what object or attribute is actually present in the image. A watermark is irrelevant to the semantics of the scene and would inject visual noise into the training data, so it cannot serve as a ground-truth annotation.
- ✓
Adding ground-truth annotations to training images so the model learns what to predict
Why this is correct
Data labeling consists of adding human-verified ground-truth annotations to each training image, such as an image-level class label or object bounding boxes, so that the model has a clear target output to learn. During training, the model's prediction is compared with these labels to compute a loss, and backpropagation adjusts the model to associate input pixel patterns with the correct semantic meaning. Without such annotations, the images have no supervisory signal and the model cannot learn how to map inputs to desired outputs.
- ✗
Compressing images to reduce storage costs during training
Why it's wrong here
Compressing images reduces their file size to save storage, but it does not add any semantic information to the data. In supervised machine learning, labeling is the process of attaching ground-truth targets (such as class labels or bounding boxes) to each training image. Compression can even introduce artifacts that make the image less faithful to the original scene, but it never supplies the answer the model is supposed to predict.
- ✗
Filtering out low-quality or blurry training images
Why it's wrong here
Filtering out blurry or low-quality images is a dataset curation step that improves the input signal by removing ambiguous examples, but it does not assign any target values. Labeling is an additive operation: every kept image must be given a correct answer before it can be used in supervised training. High-quality images without labels still provide no supervision signal, because the model has no way to know what the expected output is.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Training data
Training data is a set of examples used to teach a machine learning model how to make predictions or decisions.
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
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