A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end -to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS Which approach should the Specialist use for training a model using that data?
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available customer -facing application. The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?
A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this dat a. Which solution requires the LEAST effort to be able to query this data?
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?
