You are building a data repository for your company’s social media website that allows users to upload photos and videos to their personal stream. These photos and videos need to be labelled and classified so your company can use them to build direct marketing capabilities into your application based on machine learning. The direct marketing capability will be used to send targeted advertisements to users who have uploaded videos or photos of content related to a given product.
You are using Amazon SageMaker Ground Truth to label your user’s photos and videos. Sometimes your Ground Truth human workers mislabel images and/or videos. Which SageMaker Ground Truth feature helps you continue to get high-quality labelling in an automated way even when your workers occasionally mislabel?

Answer options:

A.Chaining labeling jobs
B.Label verification and adjustment
C.Batches for labeling tasks
D.Annotation consolidation

Answer: D
Option A is incorrect. Ground Truth chaining labelling jobs allows you to reuse datasets from previous labelling jobs. This feature would not help you address mislabeled images or videos.
Option B is incorrect. The Ground Truth label verification and adjustment feature allows you to have workers verify and correct mislabeled labels. This would help you correct mislabeled items, but it is not an automated process. It is manual.
Option C is incorrect. The Ground Truth batches for labelling tasks feature is used to send objects to your workers in batches. This would not help you correct mislabeled objects.
Option D is correct. The Ground Truth annotation consolidation feature allows you to combine the annotations of multiple workers to produce an automated probabilistic estimate of what the correct label should be.
Reference:
Please see the Amazon SageMaker developer guide titled Data Labeling, and the Amazon Machine Learning blog titled Use the wisdom of crowds with Amazon SageMaker Ground Truth to annotate data more accurately.