AWS Forecast vs SageMaker for Time-Series Prediction

SAA-C03 By islever Editorial Last updated

Design secure workloads and applications. Determine high-performing data ingestion and transformation solutions.

Short answer

DE Configure an AWS Lambda function to use an Amazon Forecast predictor and train the predictor using historical data from S3.

Question

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.)
  1. Deploy an Amazon SageMaker model. Create a SageMaker endpoint for inference.
  2. Use Amazon SageMaker to train a model by using the historical data in the S3 bucket.
  3. Configure an AWS Lambda function with a function URL that uses Amazon SageMaker endpoints to create predictions based on the inputs.
  4. Configure an AWS Lambda function with a function URL that uses an Amazon Forecast predictor to create a prediction based on the inputs. Correct Answer
  5. Train an Amazon Forsecast predictor by using the historical data in the S3 bucket. Correct Answer

Key Takeaways

The question explicitly states 'no machine learning (ML) experience' and asks for a 'managed service for training and predictions', pointing to Amazon Forecast's fully managed nature over SageMaker's developer-centric approach.

This question tests the selection of a managed time-series forecasting service for manufacturing resource prediction using historical S3 data. The correct answer leverages Amazon Forecast, which requires no ML experience, unlike Amazon SageMaker.

Common mistake: Many learners choose AB (SageMaker) because it is a more general-purpose ML tool, but they overlook the specific constraint that the company has no ML experience, making SageMaker's manual training/endpoint setup too complex.

Expert Analysis

Why the Answer Is Correct

Option E is correct because Amazon Forecast is a fully managed service specifically designed for time-series forecasting (like monthly resource needs) that automatically handles model training using historical data from S3, requiring no ML expertise. Option D is correct because Amazon Forecast provides a simple API/action to create predictors based on inputs, fitting the requirement for a managed prediction service without deploying endpoints.

Why the Other Options Are Wrong

Options A and B involve Amazon SageMaker, which is a platform for building, training, and deploying custom ML models. It requires significant ML knowledge to prepare data, select algorithms, tune hyperparameters, and deploy endpoints, violating the 'no ML experience' constraint. Option C also relies on SageMaker endpoints, which is an unnecessary infrastructure layer for a simple forecasting task compared to Forecast's direct predictor actions.

Community Comment Notes

Community discussion highlights a critical exam update: Amazon Forecast is no longer available to new customers as of July 2024 (as noted by user MatAlves). However, for the purpose of this legacy question, DE remains the technically correct answer based on the original service capabilities described in the AWS documentation referenced by user TwinSpark.

Official Reference

https://docs.aws.amazon.com/forecast/latest/dg/getting-started.html

This link opens the official vendor documentation in a new tab.

Exam Strategy

When a question specifies 'no ML experience' and 'time-series forecasting', always look for Amazon Forecast first. If Forecast is not an option or excluded due to deprecation, consider Amazon SageMaker Canvas for low-code options, but avoid standard SageMaker unless the scenario implies technical capability.

Frequently Asked Questions

Why is SageMaker not suitable here?

SageMaker requires ML expertise to train models and deploy endpoints, conflicting with the 'no ML experience' requirement.

Is Amazon Forecast still available?

No, it is closed to new customers as of July 2024, but it remains the correct answer for this specific legacy exam question.

More SAA-C03 FAQ →

Community Votes

AB
53%
DE
47%

53% of anonymous learners picked answer AB. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 17 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 17

  1. MatAlves 👍 10
    "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.
  2. betttty 👍 5
    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.
  3. bujuman 👍 5
    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
  4. JoeTromundo 👍 5
    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/
  5. 1Alpha1 👍 4
    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/
Show 12 more comments
  1. Ravan 👍 4
    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.
  2. bodakrishna 👍 3
    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.
  3. anikolov 👍 3
    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/
  4. TwinSpark 👍 3
    Amazon forecast can be trained by using data from S3: https://docs.aws.amazon.com/forecast/latest/dg/getting-started.html
  5. XXXXXlNN 👍 3
    so what about AB since the Forcase is no logner available to new customers?
  6. LeonSauveterre 👍 2
    Amazon Forecast is no longer available to new customers as of July 29, 2024. However, for the AWS Certified Solutions Architect – Associate (SAA-C03) exam, Amazon Forecast may still appear as a valid service since the exam content often lags behind recent announcements. Since the company has no ML expertise, A B C are all out, because they require prior SageMaker knowledge. That would make the answers: D & E. But IRL (in real life, not a web service), you must consider SageMaker Canvas, because it "includes the key capabilities found in Forecast, including the ability to train an ensemble of forecasting models using both statistical and neural network algorithms. It creates the best model for your dataset by generating base models for each algorithm, evaluating their performance, and then combining the top-performing models into an ensemble".
  7. hilker1983 👍 2
    D. Configure an AWS Lambda function with a function URL that uses an Amazon Forecast predictor to create a prediction based on the inputs. Amazon Forecast is a managed service specifically designed for time series forecasting and can be used without extensive ML knowledge. This option allows for easy integration with Lambda for making predictions. E. Train an Amazon Forecast predictor by using the historical data in the S3 bucket. This option is essential for creating the forecasting model using the historical data stored in S3. Amazon Forecast is designed to handle this type of task and is user-friendly for those without ML experience.
  8. lenotc 👍 1
    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
  9. Andy_09 👍 3
    BE looks correct
  10. Cali182 👍 2
    B & D is the right choice
  11. TheLaPlanta 👍 2
    A + B dude
  12. Hung23 👍 1
    BE from CHATGPT

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