To annotate each image so the classifier can learn which category the example belongs to is correct.

Labeling images provides the ground truth that a supervised classifier needs so it can learn the relationship between image features and categories. Without those annotations the model cannot be trained to predict the correct class for new images.

Labels are also used for evaluation and quality checks. They let you compute loss and accuracy during training and help you identify class imbalance or mislabeled examples before you deploy a model.

To group files for Cloud Storage lifecycle and access management is incorrect because storage lifecycle rules and access controls are handled by Cloud Storage settings and IAM, and not by dataset labels.

To make it easier to find specific images by adding searchable keywords to filenames or metadata is incorrect because searchable metadata can help with organization, but the question is about assigning labels for model training and not about file discovery.

To generate a label frequency visualization after training is incorrect because visualizations can be created as a byproduct, but that is not the reason labels are assigned. The primary purpose is to provide training targets for the classifier.

Full AWS Practitioner Certification Question