Machine Learning Specialty (Page 15)

An online retailer collects the following data on customer orders: demographics, behaviors, location, shipment progress, and delivery time.A data scientist joins all the collected datasets.The result is a single dataset that includes 980 variables.The data scientist must develop a machine learning (ML) model to identify groups of customers who are likely to respond to a marketing campaign.Which combination of algorithms should the data scientist use to meet this requirement? (Choose two.)Read More →

A data scientist has explored and sanitized a dataset in preparation for the modeling phase of a supervised learning task.The statistical dispersion can vary widely between features, sometimes by several orders of magnitude.Before moving on to the modeling phase, the data scientist wants to ensure that the prediction performance on the production data is as accurate as possible.Which sequence of steps should the data scientist take to meet these requirements?Read More →

A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis.Which of the following services would both ingest and store this data in the correct format?Read More →

A library is developing an automatic book-borrowing system that uses Amazon Rekognition.Images of library members’ faces are stored in an Amazon S3 bucket.When members borrow books, the Amazon Rekognition CompareFaces API operation compares real faces against the stored faces in Amazon S3.The library needs to improve security by making sure that images are encrypted at rest.Also, when the images are used with Amazon Rekognition.they need to be encrypted in transit.The library also must ensure that the images are not used to improve Amazon Rekognition as a service.How should a machine learning specialist architect the solution to satisfy these requirements?Read More →

A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates.The time-series data has irregular timestamps and is missing some values.Data scientist must build a dataset to replace the missing values.The data scientist needs a solution that resamples the data daily and exports the data for further modeling.Which solution will meet these requirements with the LEAST implementation effort?Read More →

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images.The Specialist wants to use transfer learning and an existing model trained on images of general objects.The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.What should the Specialist do to initialize the model to re-train it with the custom data?Read More →

A data scientist receives a collection of insurance claim records.Each record includes a claim ID.the final outcome of the insurance claim, and the date of the final outcome.The final outcome of each claim is a selection from among 200 outcome categories.Some claim records include only partial information.However, incomplete claim records include only 3 or 4 outcome categories from among the 200 available outcome categories.The collection includes hundreds of records for each outcome category.The records are from the previous 3 years.The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.Which solution will meet these requirements?Read More →

A global bank requires a solution to predict whether customers will leave the bank and choose another bank.The bank is using a dataset to train a model to predict customer loss.The training dataset has 1,000 rows.The training dataset includes 100 instances of customers who left the bank.A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job.After training, the ML specialist notices that the model returns only false results.The ML specialist must correct the model so that it returns more accurate predictions.Which solution will meet these requirements?Read More →

A car company has dealership locations in multiple cities.The company uses a machine learning (ML) recommendation system to market cars to its customers.An ML engineer trained the ML recommendation model on a dataset that includes multiple attributes about each car.The dataset includes attributes such as car brand, car type, fuel efficiency, and price.The ML engineer uses Amazon SageMaker Data Wrangler to analyze and visualize data.The ML engineer needs to identify the distribution of car prices for a specific type of car.Which type of visualization should the ML engineer use to meet these requirements?Read More →

A machine learning (ML) specialist collected daily product usage data for a group of customers.The ML specialist appended customer metadata such as age and gender from an external data source.The ML specialist wants to understand product usage patterns for each day of the week for customers in specific age groups.The ML specialist creates two categorical features named dayofweek and binned_age, respectively.Which approach should the ML specialist use discover the relationship between the two new categorical features?Read More →