Exam AWS Certified Machine Learning - Specialty topic 1 question 278 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 278
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A company's machine learning (ML) specialist is building a computer vision model to classify 10 different traffic signs. The company has stored 100 images of each class in Amazon S3, and the company has another 10,000 unlabeled images. All the images come from dash cameras and are a size of 224 pixels × 224 pixels. After several training runs, the model is overfitting on the training data.

Which actions should the ML specialist take to address this problem? (Choose two.)

A. Use Amazon SageMaker Ground Truth to label the unlabeled images.
B. Use image preprocessing to transform the images into grayscale images.
C. Use data augmentation to rotate and translate the labeled images.
D. Replace the activation of the last layer with a sigmoid.
E. Use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to label the unlabeled images.

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Suggested Answer: AC 🗳️

by aquanaveen at Dec. 17, 2023, 12:26 p.m.

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xiaoeason

1 year, 2 months ago

Selected Answer: AC

C. Use data augmentation to rotate and translate the labeled images. Data augmentation involves creating new training data by applying transformations such as rotation, translation, scaling, etc. This helps to increase the diversity of the training data and makes the model more robust without requiring additional labeled data. A. Use Amazon SageMaker Ground Truth to label the unlabeled images. Leveraging Amazon SageMaker Ground Truth can help in labeling the unlabeled images to expand the training dataset and reduce overfitting. Adding more labeled data can improve the generalization of the model and reduce overfitting.

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