Machine Learning Specialty (Page 12)

A machine learning engineer is building a bird classification model.The engineer randomly separates a dataset into a training dataset and a validation dataset.During the training phase, the model achieves very high accuracy.However, the model did not generalize well during validation of the validation dataset.The engineer realizes that the original dataset was imbalanced.What should the engineer do to improve the validation accuracy of the model?Read More →

An online delivery company wants to choose the fastest courier for each delivery at the moment an order is placed.The company wants to implement this feature for existing users and new users of its application.Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3.There is one model for each city where the company operates.Operation engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory.The operation engineers want to avoid managing unnecessary resources.Which solution will enable the company to achieve its goal with the LEAST operational overhead?Read More →

A machine learning (ML) specialist is using the Amazon SageMaker DeepAR forecasting algorithm to train a model on CPU-based Amazon EC2 On-Demand instances.The model currently takes multiple hours to train.The ML specialist wants to decrease the training time of the model.Which approaches will meet this requirement? (Choose two.)Read More →

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production.The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.New one model can serve user requests at a time.The company must measure the performance of the new experimental model without affecting the current live traffic.Which solution will meet these requirements?Read More →

A company’s data scientist has trained a new machine learning model that performs better on test data than the company’s existing model performs in the production environment.The data scientist wants to replace the existing model that runs on an Amazon SageMaker endpoint in the production environment.However, the company is concerned that the new model might not work well on the production environment data.The data scientist needs to perform A/B testing in the production environment to evaluate whether the new model performs well on production environment data.Which combination of steps must the data scientist take to perform the A/B testing? (Choose two.)Read More →

A healthcare company wants to create a machine learning (ML) model to predict patient outcomes.A data science team developed an ML model by using a custom ML library.The company wants to use Amazon SageMaker to train this model.The data science team creates a custom SageMaker image to train the model.When the team tries to launch the custom image in SageMaker Studio, the data scientists encounter an error within the application.Which service can the data scientists use to access the logs for this error?Read More →

A car company is developing a machine learning solution to detect whether a car is present in an image.The image dataset consists of one million images.Each image in the dataset is 200 pixels in height by 200 pixels in width.Each image is labeled as either having a car or not having a car.Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?Read More →

A machine learning (ML) specialist is training a multilayer perceptron (MLP) on a dataset with multiple classes.The target class of interest is unique compared to the other classes in the dataset, but it does not achieve an acceptable recall metric.The ML specialist varies the number and size of the MLP’s hidden layers, but the results do not improve significantly.Which solution will improve recall in the LEAST amount of time?Read More →

A manufacturing company has a large set of labeled historical sales data.The manufacturer would like to predict how many units of a particular part should be produced each quarter.Which machine learning approach should be used to solve this problem?Read More →

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?Read More →