A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays. The data scientists are using Amazon Forecast to generate the forecasts. Which algorithm in Forecast should the data scientists use to meet these requirements?
A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail -style application. The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations. The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model. The company needs to ensure that everyone can update their customizations multiple times each hour. Which approach will maximize transcription accuracy during the development phase?
A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance. The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget. Which solution will result in the MOST cost savings?
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near -real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent How should the Specialist frame this business problem'?
A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables: The specialist chose a model that needs numerical input data. Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?
