Which AWS Service Builds, Trains, and Deploys ML Models?
A company wants to build, train, and deploy machine learning (ML) models. Which AWS service can the company use to meet this requirement?
Community Votes
100% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests recognition of a general-purpose ML platform versus domain-specific AI services; the trap is picking a purpose-built service like Personalize or Forecast because it mentions machine learning.
This CLF-C02 question asks which AWS service supports the full machine learning lifecycle of building, training, and deploying ML models. Amazon SageMaker is the only option that provides an end-to-end managed environment for that entire workflow, so the correct choice is D.
Learners most often choose Amazon Forecast or Amazon Personalize, because both are AWS machine learning services—but they only deliver prebuilt predictive outputs for forecasting and recommendation use cases, not the ability to build and train your own models.
Community Discussion (3 comments)
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Expert Analysis
Why the Answer Is Correct
Amazon SageMaker is AWS's fully managed machine learning service that supplies every tool needed to build, train, and deploy ML models at scale. It includes hosted notebooks, debuggers, training pipelines, MLOps tooling, and human-in-the-loop features, covering the entire lifecycle the question names. Because the company wants to build, train, and deploy its own models, SageMaker is the only option that supports all three stages. As ShaiTay noted, it "provides every tool and workflow needed to build, train, and deploy machine learning (ML) models at scale."Why the Other Options Are Wrong
Amazon Personalize is a recommendation engine that delivers personalized rankings and real-time personalization without letting you build or train a custom model. Amazon Comprehend is a natural language processing service for sentiment, entities, and key phrases, which returns pre-trained insights rather than a trainable model. Amazon Forecast produces time-series demand forecasts from historical data, but it is a single-purpose predictive service, not an environment for developing and deploying arbitrary ML models. None of the three offers the integrated development, training, and deployment workflow the question requires.Community Comment Notes
Every learner comment selected option D, with alokdixit2006 highlighting the "Ability to build, train, and deploy machine learning (ML) models quickly." a77c459 expanded that SageMaker supplies "tools and infrastructure to build, train, and deploy ML models at scale, including large foundation models for generative AI," plus notebooks, MLOps capabilities, and governance controls. The unanimous community consensus matches the technical analysis, so D is unambiguous here.Official Reference
Exam Strategy
Scan the question for lifecycle verbs such as build, train, and deploy—if it asks for a complete ML workflow, the answer is SageMaker. Reserve Personalize, Comprehend, and Forecast for questions naming a specific output such as recommendations, sentiment analysis, or time-series forecasts.
Frequently Asked Questions
Why aren't Amazon Forecast or Amazon Personalize correct for building ML models?
Both are domain-specific managed services that return ready-made predictions for forecasting and recommendations. They do not let you create, train, or deploy your own custom models.
What does Amazon Comprehend actually do in AWS ML services?
Amazon Comprehend is a natural language processing service that extracts sentiment, entities, and key phrases from text using pre-trained models rather than letting you train your own.
Related Analysis
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