Machine Learning Specialty (Page 2)

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 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 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 company hosts a machine learning (ML) dataset repository on Amazon S3.A data scientist is preparing the repository to train a model.The data scientist needs to redact personally identifiable information (PH) from the dataset.Which solution will meet these requirements with the LEAST development effort?Read More →

A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints.To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.Which services are integrated with Amazon SageMaker to track this information? (Choose two.)Read More →

A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker’s built-in seq2seq algorithm with500,000 aligned sentence pairs.While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words.However, the quality becomes unacceptable if the sentence is 100 words long.Which action will resolve the problem?Read More →

A Data Scientist is working on an application that performs sentiment analysis.The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset.Which tool should be used to improve the validation accuracy?Read More →

A Data Scientist wants to gain real-time insights into a data stream of GZIP files.Which solution would allow the use of SQL to query the stream with the LEAST latency?Read More →

A Machine Learning Specialist needs to move and transform data in preparation for training.Some of the data needs to be processed in near-real time, and other data can be moved hourly.There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data.Which of the following services can feed data to the MapReduce jobs? (Choose two.)Read More →

A Data Scientist needs to analyze employment data.The dataset contains approximately 10 million observations on people across 10 different features.During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal.While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also shows a right skew, with fewer older individuals participating in the workforce.Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)Read More →