Machine Learning Specialty

A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena.The dataset contains more than 800,000 records stored as plaintext CSV files.Each record contains 200 columns and is approximately 1.5 MB in size.Most queries will span 5 to 10 columns only.How should the Machine Learning Specialist transform the dataset to minimize query runtime?Read More →

Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors.While exploring the data, theSpecialist notices that the magnitude of the input features vary greatly.The Specialist does not want variables with a larger magnitude to dominate the model.What should the Specialist do to prepare the data for model training?Read More →

A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social media.The company needs to be able to evaluate the sentiment expressed in social media posts, and visualize trends and configure alarms based on various thresholds.The company needs to implement this solution quickly, and wants to minimize the infrastructure and data science resources needed to evaluate the messages.The company already has a solution in place to collect posts and store them within an Amazon S3 bucket.What services should the data science team use to deliver this solution?Read More →

A retail company wants to update its customer support system.The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.Currently, an operator manually performs the category assignment and routing.After the operator classifies and routes the claim, the company stores the claim’s record in a central database.The claim’s record includes the claim’s category.The company has no data science team or experience in the field of machine learning (ML).The company’s small development team needs a solution that requires no ML expertise.Which solution meets these requirements?Read More →

A real-estate company is launching a new product that predicts the prices of new houses.The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket.The data has a header, some categorical fields, and some missing values.The company’s data scientists have used Python with a common open-source library to fill the missing values with zeros.The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.The accuracy of the predictions with the current model is below 50%.The company wants to improve the model performance and launch the new product as soon as possible.Which solution will meet these requirements with the LEAST operational overhead?Read More →

A data scientist is reviewing customer comments about a company’s products.The data scientist needs to present an initial exploratory analysis by using charts and a word cloud.The data scientist must use feature engineering techniques to prepare this analysis before starting a natural language processing (NLP) model.Which combination of feature engineering techniques should the data scientist use to meet these requirements? (Choose two.)Read More →

An energy company has wind turbines, weather stations, and solar panels that generate telemetry data.The company wants to perform predictive maintenance on these devices.The devices are in various locations and have unstable internet connectivity.A team of data scientists is using the telemetry data to perform machine learning (ML) to conduct anomaly detection and predict maintenance before the devices start to deteriorate.The team needs a scalable, secure, high-velocity data ingestion mechanism.The team has decided to use Amazon S3 as the data storage location.Which approach meets these requirements?Read More →

A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification.Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of “Sample Yogurt” in the document of customer comments, then “Sample Yogurt” should be classified as “yogurt,” “snack,” and “dairy product.”The team is using Amazon Comprehend to train the model and must complete the project as soon as possible.Which functionality of Amazon Comprehend should the team use to meet these requirements?Read More →

A newspaper publisher has a table of customer data that consists of several numerical and categorical features, such as age and education history, as well as subscription status.The company wants to build a targeted marketing model for predicting the subscription status based on the table data.Which Amazon SageMaker built-in algorithm should be used to model the targeted marketing?Read More →

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