AWS Certified Machine Learning – Specialty — Question 247

A company is creating an application to identify, count, and classify animal images that are uploaded to the company’s website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common.

The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker.

Which combination of steps should the ML engineer take to train the model? (Choose two.)

Answer options

• A. Use a ResNet model. Initiate full training mode by initializing the network with random weights.
• B. Use an Inception model that is available with the SageMaker image classification algorithm.
• C. Create a .lst file that contains a list of image files and corresponding class labels. Upload the .lst file to Amazon S3.
• D. Initiate transfer learning. Train the model by using the images of less common species.
• E. Use an augmented manifest file in JSON Lines format.

Correct answer: C, D

Explanation

The correct steps involve creating a .lst file with the labeled images and class labels, and then initiating transfer learning to effectively train the model with the less common species images. Options A and B suggest using different models without addressing the need for labeled images, while option E does not align with the requirement to use a .lst file for training.