Machine Learning Specialty (Page 4)

An ecommerce company has observed that customers who use the company’s website rarely view items that the website recommends to customers.The company wants to recommend items to customers that customers are more likely to want to purchase.Which solution will meet this requirement in the SHORTEST amount of time?Read More →

A company is using a legacy telephony platform and has several years remaining on its contract.The company wants to move to AWS and wants to implement the following machine learning features:• Call transcription in multiple languages• Categorization of calls based on the transcript• Detection of the main customer issues in the calls• Customer sentiment analysis for each line of the transcript, with positive or negative indication and scoring of that sentimentWhich AWS solution will meet these requirements with the LEAST amount of custom model training?Read More →

A media company wants to deploy a machine learning (ML) model that uses Amazon SageMaker to recommend new articles to the company’s readers.The company’s readers are primarily located in a single city.The company notices that the heaviest reader traffic predictably occurs early in the morning, after lunch, and again after work hours.There is very little traffic at other times of day.The media company needs to minimize the time required to deliver recommendations to its readers.The expected amount of data that the API call will return for inference is less than 4 MB.Which solution will meet these requirements in the MOST cost-effective way?Read More →

A developer at a retail company is creating a daily demand forecasting model.The company stores the historical hourly demand data in an Amazon S3 bucket.However, the historical data does not include demand data for some hours.The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case.How should the developer verify the suitability of an ARIMA approach?Read More →

A mining company wants to use machine learning (ML) models to identify mineral images in real time.A data science team built an image recognition model that is based on convolutional neural network (CNN).The team trained the model on Amazon SageMaker by using GPU instances.The team will deploy the model to a SageMaker endpoint.The data science team already knows the workload traffic patterns.The team must determine instance type and configuration for the workloads.Which solution will meet these requirements with the LEAST development effort?Read More →

A company wants to deliver digital car management services to its customers.The company plans to analyze data to predict the likelihood of users changing cars.The company has 10 TB of data that is stored in an Amazon Redshift cluster.The company’s data engineering team is using Amazon SageMaker Studio for data analysis and model development.Only a subset of the data is relevant for developing the machine learning models.The data engineering team needs a secure and cost-effective way to export the data to a data repository in Amazon S3 for model development.Which solutions will meet these requirements? (Choose two.)Read More →

A data scientist is building a new model for an ecommerce company.The model will predict how many minutes it will take to deliver a package.During model training, the data scientist needs to evaluate model performance.Which metrics should the data scientist use to meet this requirement? (Choose two.)Read More →

An ecommerce company discovers that the search tool for the company’s website is not presenting the top search results to customers.The company needs to resolve the issue so the search tool will present results that customers are most likely to want to purchase.Which solution will meet this requirement with the LEAST operational effort?Read More →

A machine learning (ML) engineer is creating a binary classification model.The ML engineer will use the model in a highly sensitive environment.There is no cost associated with missing a positive label.However, the cost of making a false positive inference is extremely high.What is the most important metric to optimize the model for in this scenario?Read More →

A telecommunications company has deployed a machine learning model using Amazon SageMaker.The model identifies customers who are likely to cancel their contract when calling customer service.These customers are then directed to a specialist service team.The model has been trained on historical data from multiple years relating to customer contracts and customer service interactions in a single geographic region.The company is planning to launch a new global product that will use this model.Management is concerned that the model might incorrectly direct a large number of calls from customers in regions without historical data to the specialist service team.Which approach would MOST effectively address this issue?Read More →