Question-7: You are working with an NGO who does the various research on the fruit, crop and vegetable for their fruitful usage. There are almost 30 photographers cum researchers are working with the NGO who continuously send the image of the newly or varied version of the fruits, vegetables and crops. All the images received needs to be classified on daily basis as well need to provide proper label to them and this same can be used to train your Machine Learning Model. However, you have to make sure whatever method you are using they must be labeled accurately as well solution should be cost effective, which of the following you would choose?

1. You would be using AWS Rekgonition to label all your images and the images which are completely new and can not be labeled ask researcher to label them.
2. You would be using AWS Ground Truth to automatically label your images and the images which can not be labeled you can use the AWS Ground Truth human labelers to label the images that the can not be labeled by AWS Ground Truth automatically.
3. Get Latest Certification Questions & Answer from this link, which is regularly updated as per recent syllabus.
4. You would be using Natural Language Processing

Ans: B, C

Exp:  AWS SageMaker Ground Truth: " All AWS Certification & Training Material Can be accessed from this link as well "  This help in creating and build highly accurate training datasets for machine learning quickly. With the SageMaker Ground truth offers easy access to labelers using AWS Mechanical trunk and provide to them with the built-in workflows and interfaces for common labeling tasks. You can even use your own labeler or use vendor recommended by AWS Marketplace. And if you want automatic labeling you can do using the AWS SageMaker Ground Truth can reduce the cost by 70%, which itself works by training Ground Truth from the data labeled by humans so that the service learns to label the data independently.

The majority of the models created today require a human to manually label data in a way that allows the model to learn how to make correct decisions. For example, building a computer vision system that is reliable enough to identify the objects such as traffic lights, stop signs, pedestrians-requires thousands of hours of video recordings that consists of hundreds of millions of video frames.

Amazon SageMaker Ground Truth significantly reduces the time and effort required to create datasets for training to reduce costs. These savings are achieved by using machine learning to automatically label data. The model is able to get progressively better over time by continuously learning from labels created by human labelers.

Where the labeling model has high confidence in its results based on what it has learned so far, it will automatically apply labels to the raw data. Where the labeling model has lower confidence in its results, it will pass the data to humans to do the labeling. The human-generated labels are provided back to the labeling model for it to learn from and improve. Over time, SageMaker Ground Truth can label more and more data automatically and substantially speed up the creation of training datasets.

As in the question it is asked that images should be labeled accurately then you can use the human labelers as well.