Machine Learning Specialty (Page 20)

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 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 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 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 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 Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome.Some partial information on claim contents is also provided, but only for a few of the 200 categories.For each outcome category, there are hundreds of records distributed over the past 3 years.The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.What type of machine learning model should be used?Read More →

A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories.A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime.The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories.Even though many factory locations do not have reliable or high- speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.Which deployment architecture for the model will address these business requirements?Read More →

A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign.The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment.Only historical data from before the marketing campaign is available for training.How should a data scientist adjust the solution?Read More →

A data scientist needs to identify fraudulent user accounts for a company’s ecommerce platform.The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.The data scientist is using AWS Glue to cleanse the company’s application logs during ingestion.Which strategy will allow the data scientist to identify fraudulent accounts?Read More →

A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster.The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes.To save on costs, the Specialist will use Spot Instances in the EMR cluster.Which nodes should the Specialist launch on Spot Instances?Read More →