MLS-C01 (Page 9)

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem.The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.Which model describes the underlying data in this situation?Read More →

A geospatial analysis company processes thousands of new satellite images each day to produce vessel detection data for commercial shipping.The company stores the training data in Amazon S3.The training data incrementally increases in size with new images each day.The company has configured an Amazon SageMaker training job to use a single ml.p2.xlarge instance with File input mode to train the built-in Object Detection algorithm.The training process was successful last month but is now failing because of a lack of storage.Aside from the addition of training data, nothing has changed in the model training process.A machine learning (ML) specialist needs to change the training configuration to fix the problem.The solution must optimize performance and must minimize the cost of training.Which solution will meet these requirements?Read More →

A data scientist is training a large PyTorch model by using Amazon SageMaker.It takes 10 hours on average to train the model on GPU instances.The data scientist suspects that training is not converging and that resource utilization is not optimal.What should the data scientist do to identify and address training issues with the LEAST development effort?Read More →

A company wants to segment a large group of customers into subgroups based on shared characteristics.The company’s data scientist is planning to use the Amazon SageMaker built-in k-means clustering algorithm for this task.The data scientist needs to determine the optimal number of subgroups (k) to use.Which data visualization approach will MOST accurately determine the optimal value of k?Read More →

A sports analytics company is providing services at a marathon.Each runner in the marathon will have their race ID printed as text on the front of their shirt.The company needs to extract race IDs from images of the runners.Which solution will meet these requirements with the LEAST operational overhead?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 →

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 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 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 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 →