AWS Certified Solutions Architect – Associate (SAA-C03) — Question 648

A company that uses AWS needs a solution to predict the resources needed for manufacturing processes each month. The solution must use historical values that are currently stored in an Amazon S3 bucket. The company has no machine learning (ML) experience and wants to use a managed service for the training and predictions.

Which combination of steps will meet these requirements? (Choose two.)

Answer options

• A. Deploy an Amazon SageMaker model. Create a SageMaker endpoint for inference.
• B. Use Amazon SageMaker to train a model by using the historical data in the S3 bucket.
• C. Configure an AWS Lambda function with a function URL that uses Amazon SageMaker endpoints to create predictions based on the inputs.
• D. Configure an AWS Lambda function with a function URL that uses an Amazon Forecast predictor to create a prediction based on the inputs.
• E. Train an Amazon Forsecast predictor by using the historical data in the S3 bucket.

Correct answer: A, B

Explanation

Amazon SageMaker provides a fully managed environment that simplifies the machine learning workflow, allowing users to train models without extensive ML expertise. Training a model with historical S3 data (Option B) and deploying it to a SageMaker endpoint (Option A) fulfills the requirement for a managed training and prediction pipeline. Using AWS Lambda functions with function URLs is unnecessary for the core training and hosting requirements specified in the scenario.