Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 687 discussion

so what about AB since the Forcase is no logner available to new customers?

Amazon Forecast is no longer available to new customers. https://aws.amazon.com/blogs/machine-learning/transition-your-amazon-forecast-usage-to-amazon-sagemaker-canvas/

"Amazon Forecast is no longer available to new customers. Existing customers of Amazon Forecast can continue to use the service as normal" "After careful consideration, we have made the decision to close new customer access to Amazon Forecast, effective July 29, 2024." This question will either be removed or reformulated to exclude Forecast as the service is no longer available to new customers.

Amazon forecast can be trained by using data from S3: https://docs.aws.amazon.com/forecast/latest/dg/getting-started.html

Because of these assertions - The company has no machine learning (ML) experience - The comapny wants to use a managed service We could tempted to go for SageMaker that is the core AWS managed service for ML purposes . But, but, if we consider this valuable information: - A company that uses AWS needs a solution to predict the resources needed for manufacturing processes. With a bit research, we will find out that AWS also hold time-series forecasting service based on machine learning (ML). https://aws.amazon.com/forecast/?nc1=h_ls So i understand options DE are the best answers enven thought this service is not mentionned anywhere in current SAA-C03 course version

BE from CHATGPT

SageMaker and Forecast can directly utilize data within an S3 B) E) https://aws.amazon.com/blogs/compute/build-workflows-for-amazon-forecast-with-aws-step-functions/ https://docs.aws.amazon.com/sagemaker/latest/dg/train-model.html

A + B dude

Yes, exactly. Steps B and A together constitute a comprehensive solution: - Step B involves using Amazon SageMaker to train a machine learning model using historical data stored in the S3 bucket. - Step A involves deploying the trained model as a SageMaker endpoint, allowing for real-time inference on new data. This combination leverages Amazon SageMaker's managed services for both training and inference, meeting the company's requirements efficiently.

A & B: B. Amazon SageMaker is a managed service that provides built-in algorithms and tools for training machine learning models. You can use SageMaker to train a model using historical data stored in an S3 bucket. This meets the requirement of utilizing a managed service for training the model without requiring machine learning experience. A. Once the model is trained using SageMaker, you can deploy it by creating a SageMaker endpoint for inference. This endpoint allows you to make predictions based on new data, fulfilling the requirement of predicting resources needed for manufacturing processes each month.

*E*: Amazon Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts without requiring any prior ML experience. Forecast is applicable in a wide variety of use cases, including estimating product demand, energy demand, workforce planning, computing cloud infrastructure usage, traffic demand, supply chain optimization, and financial planning. *D*: Publish demand using AWS Lambda, AWS Step Functions, and Amazon CloudWatch Events rule to periodically (hourly) query the database and write the past X-months (count from the current timestamp) demand data into the source Amazon S3. https://aws.amazon.com/blogs/machine-learning/automating-your-amazon-forecast-workflow-with-lambda-step-functions-and-cloudwatch-events-rule/

B & D is the right choice

My votes are for DE based on statement from AWS site: "Alternatively, if you are looking for a fully managed service to deliver highly accurate forecasts, without writing code, we recommend checking out Amazon Forecast. Amazon Forecast is a time-series forecasting service based on machine learning (ML) and built for business metrics analysis." https://aws.amazon.com/blogs/machine-learning/deep-demand-forecasting-with-amazon-sagemaker/

Explanation: Training the Model with SageMaker (Option B): Use Amazon SageMaker to train a machine learning model based on historical data. SageMaker simplifies the process of training, deploying, and managing machine learning models. Creating Predictions with Amazon Forecast (Option D): Use Amazon Forecast to create a predictor based on historical data. Forecast is designed for time-series forecasting, making it suitable for predicting resources needed for manufacturing processes each month. Combining SageMaker for training and Amazon Forecast for predictions provides a comprehensive solution, and AWS Lambda can be used to integrate these services into your workflow.

BE looks correct