A company must predict monthly resource needs for manufacturing processes using historical data stored in an Amazon S3 bucket. The company has no ML experience and wants a managed service for training and predictions. Which combination of steps will meet these requirements? (Choose two.)

The company needs to predict resource needs using historical data and requires a managed service without ML experience. Amazon SageMaker is a fully managed service for building, training, and deploying ML models. Training a model with SageMaker using S3 data directly addresses the training requirement. Deploying a SageMaker model and creating an endpoint for inference provides the managed prediction service. Amazon Forecast is a time-series forecasting service, which could be suitable for predicting resource needs. However, the question asks for a combination of steps that meet the requirements, and SageMaker offers a complete end-to-end solution for training and inference, aligning with the "no ML experience" and "managed service" criteria. While an AWS Lambda function could invoke a SageMaker endpoint or Forecast predictor, it's not the primary service for training or deploying the model itself, making those options less direct for the core requirements.