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.)

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.






 

Suggested Answer: BD

Community Answer: CD




This question is in MLS-C01 AWS Certified Machine Learning – Specialty Exam
For getting AWS Certified Machine Learning – Specialty Certificate


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