Azure Data Scientists Associate Practice Exam

What is an important requirement for the labeling process of images in Azure ML?

1. Each image must have multiple labels

2. All labeling must be done manually

3. Labels should be allowed to change frequently

4. Images must be processed in batches only

The correct answer is: All labeling must be done manually

In the labeling process of images in Azure Machine Learning, one important requirement is that all labeling must be done manually. This is significant because manual labeling ensures a high degree of accuracy and precision in the labeling process, which is crucial for training effective machine learning models. When images are manually labeled, data scientists can apply their expertise and judgment to categorize the images correctly, ensuring that the labels align well with the intended output of the model. This is particularly important in tasks such as object detection, image segmentation, or classification, where the quality of labels directly impacts the model's performance. Manual labeling allows for more nuanced and contextually appropriate labels than automated methods may sometimes provide, particularly in complex scenarios where visual data can be ambiguous or open to interpretation. While automated labeling is possible using various techniques, it typically requires an initial set of manually labeled data to train the model to perform labeling tasks reliably. Additionally, having consistent and high-quality labels from the outset helps avoid problems during model training and evaluation.