Question 2

Explanation

Correct option:

Incremental Training

Over time, you might find that a model generates inferences that are not as good as they were in the past. With incremental training, you can use the artifacts from an existing model and use an expanded dataset to train a new model. Incremental training saves both time and resources.

You can use incremental training to:

Train a new model using an expanded dataset that contains an underlying pattern that was not accounted for in the previous training and which resulted in poor model performance.

Use the model artifacts or a portion of the model artifacts from a popular publicly available model in a training job. You don't need to train a new model from scratch.

Resume a training job that was stopped.

Train several variants of a model, either with different hyperparameter settings or using different datasets.

You can read more on this reference link -

https://docs.aws.amazon.com/sagemaker/latest/dg/incremental-training.html

Incorrect options:

Batch Training - Batch size is a term used in machine learning and refers to the number of training examples utilized in one iteration. The batch size can be one of three options:

batch mode: where the batch size is equal to the total dataset thus making the iteration and epoch values equivalent

mini-batch mode: where the batch size is greater than one but less than the total dataset size. Usually, a number that can be divided into the total dataset size.

stochastic mode: where the batch size is equal to one. Therefore the gradient and the neural network parameters are updated after each sample.

There is no such thing as "batch training" and this option has been added as a distractor.

Beta Testing -  Beta Testing is one of the Acceptance Testing types used in traditional software engineering, which adds value to the product as the end-user (intended real user) validates the product for functionality, usability, reliability, and compatibility.

This option has been added as a distractor.

Transfer Learning - This is a technique used in image classification algorithms. The image classification algorithm takes an image as input and classifies it into one of the output categories. Image classification in Amazon SageMaker can be run in two modes: full training and transfer learning. In full training mode, the network is initialized with random weights and trained on user data from scratch. In transfer learning mode, the network is initialized with pre-trained weights and just the top fully connected layer is initialized with random weights. Then, the whole network is fine-tuned with new data. In this mode, training can be achieved even with a smaller dataset. This is because the network is already trained and therefore can be used in cases without sufficient training data.

Transfer Learning is a general machine learning technique that is not relevant to the SageMaker specific use-case described in the question.