AWS Certified Machine Learning – Specialty — Question 152

An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon
SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.
Which steps would improve the accuracy of the solution? (Choose three.)

Answer options

• A. Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
• B. Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
• C. Augment the images in the dataset. Use open source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
• D. Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.
• E. Use Amazon Rekognition Custom Labels to train a new model.
• F. Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.

Correct answer: C, D, F

Explanation

Option C is correct because augmenting images can significantly improve the model's robustness by providing more varied training data. Option D is also correct as normalizing and scaling pixels can enhance the model's learning process. Option F is valid since addressing class imbalances can lead to better performance in categorizing products. Options A, B, and E are not suitable as they involve changing the model or using different services instead of directly enhancing the existing model's training data.