Machine Learning Specialty (Page 19)

An analytics company has an Amazon SageMaker hosted endpoint for an image classification model.The model is a custom-built convolutional neural network (CNN) and uses the PyTorch deep learning framework.The company wants to increase throughput and decrease latency for customers that use the model.Which solution will meet these requirements MOST cost-effectively?Read More →

A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features.The Marketing team has not provided any insight about which features are relevant for churn prediction.The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome.While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy.Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team’s needs? (Choose two.)Read More →

A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS).The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3.Which solution will meet these requirements?Read More →

A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices.The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models.There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.Which techniques should the company use for feature selection? (Choose three.)Read More →

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.)Read More →

A company is building a new supervised classification model in an AWS environment.The company’s data science team notices that the dataset has a large quantity of variables.All the variables are numeric.The model accuracy for training and validation is low.The model’s processing time is affected by high latency.The data science team needs to increase the accuracy of the model and decrease the processing time.What should the data science team do to meet these requirements?Read More →

A company hosts a public web application on AWS.The application provides a user feedback feature that consists of free-text fields where users can submit text to provide feedback.The company receives a large amount of free-text user feedback from the online web application.The product managers at the company classify the feedback into a set of fixed categories including user interface issues, performance issues, new feature request, and chat issues for further actions by the company’s engineering teams.A machine learning (ML) engineer at the company must automate the classification of new user feedback into these fixed categories by using Amazon SageMaker.A large set of accurate data is available from the historical user feedback that the product managers previously classified.Which solution should the ML engineer apply to perform multi-class text classification of the user feedback?Read More →

A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign.The majority of data is sensitive customer data.The data must be encrypted at rest.The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged.Which implementation will meet these requirements?Read More →

A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat.The ML Specialist performs some tests and records the following results for a neural network-based image classifier:Total number of images available = 1,000Test set images = 100 (constant test set)The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.Which techniques can be used by the ML Specialist to improve this specific test error?Read More →

A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes.The labeled data is divided into a training set of 1,000 images and a test set of 200 images.The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.What changes should the Specialist consider to solve this issue? (Choose three.)Read More →