Machine Learning Specialty (Page 22)

An automotive company uses computer vision in its autonomous cars.The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN).The company trained the models by using PyTorch through the Amazon SageMaker SDK.The vehicles have limited hardware and compute power.The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy.Which solution will improve the computational efficiency of the models?Read More →

A data scientist wants to improve the fit of a machine learning (ML) model that predicts house prices.The data scientist makes a first attempt to fit the model, but the fitted model has poor accuracy on both the training dataset and the test dataset.Which steps must the data scientist take to improve model accuracy? (Choose three.)Read More →

A financial services company wants to automate its loan approval process by building a machine learning (ML) model.Each loan data point contains credit history from a third-party data source and demographic information about the customer.Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan.The company will use Amazon SageMaker to build the model.Which solution will meet these requirements with the LEAST development effort?Read More →

A machine learning (ML) specialist uploads 5 TB of data to an Amazon SageMaker Studio environment.The ML specialist performs initial data cleansing.Before the ML specialist begins to train a model, the ML specialist needs to create and view an analysis report that details potential bias in the uploaded data.Which combination of actions will meet these requirements with the LEAST operational overhead? (Choose two.)Read More →

A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical data.The data scientist saves the transformations to SageMaker Feature Store.The historical data is periodically uploaded to an Amazon S3 bucket.The data scientist needs to transform the new historic data and add it to the online feature store.The data scientist needs to prepare the new historic data for training and inference by using native integrations.Which solution will meet these requirements with the LEAST development effort?Read More →

An engraving company wants to automate its quality control process for plaques.The company performs the process before mailing each customized plaque to a customer.The company has created an Amazon S3 bucket that contains images of defects that should cause a plaque to be rejected.Low-confidence predictions must be sent to an internal team of reviewers who are using Amazon Augmented AI (Amazon A2I).Which solution will meet these requirements?Read More →

A wildlife research company has a set of images of lions and cheetahs.The company created a dataset of the images.The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah.The company wants to train a model to identify whether new images contain a lion or cheetah.Which Amazon SageMaker algorithm will meet this requirement?Read More →

A data scientist for a medical diagnostic testing company has developed a machine learning (ML) model to identify patients who have a specific disease.The dataset that the scientist used to train the model is imbalanced.The dataset contains a large number of healthy patients and only a small number of patients who have the disease.The model should consider that patients who are incorrectly identified as positive for the disease will increase costs for the company.Which metric will MOST accurately evaluate the performance of this model?Read More →

A machine learning (ML) specialist is training a linear regression model.The specialist notices that the model is overfitting.The specialist applies an L1 regularization parameter and runs the model again.This change results in all features having zero weights.What should the ML specialist do to improve the model results?Read More →

A machine learning (ML) engineer is integrating a production model with a customer metadata repository for real-time inference.The repository is hosted in Amazon SageMaker Feature Store.The engineer wants to retrieve only the latest version of the customer metadata record for a single customer at a time.Which solution will meet these requirements?Read More →