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AWS Certified Machine Learning - Specialty (MLS-C01) Questions 51–55 (page: 12)

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 financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:

•Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
•Support event-driven ETL pipelines
•Provide a quick and easy way to understand metadata

Which approach meets these requirements?

A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.

The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.

What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

A company is running a machine learning prediction service that generates 100 TB of predictions every day. A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?