MLS-C01 (Page 30)

A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily.The company uses this information to process loan applications automatically.Some of the documents fail business validation and are returned to human reviewers, who investigate the errors.This activity increases the time to process the loan applications.What should the company do to reduce the processing time of loan applications?Read More →

A healthcare company is using an Amazon SageMaker notebook instance to develop machine learning (ML) models.The company’s data scientists will need to be able to access datasets stored in Amazon S3 to train the models.Due to regulatory requirements, access to the data from instances and services used for training must not be transmitted over the internet.Which combination of steps should an ML specialist take to provide this access? (Choose two.)Read More →

A global financial company is using machine learning to automate its loan approval process.The company has a dataset of customer information.The dataset contains some categorical fields, such as customer location by city and housing status.The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.The company’s data scientists are using a gradient boosting regression model to infer the credit score for each customer.The model has a training accuracy of99% and a testing accuracy of 75%.The data scientists want to improve the model’s testing accuracy.Which process will improve the testing accuracy the MOST?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 →

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 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 →

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 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 →

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 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 →