AWS Certified Cloud Practitioner
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A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure. Which solution meets these requirements?
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RRitesh
A
Deploy the model on an Amazon EC2 instance.
B
Deploy the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
C
Deploy the model by using Amazon CloudFront with an Amazon S3 integration.
D
Deploy the model by using an Amazon SageMaker endpoint.
Explanation:
Explanation
Amazon SageMaker is the correct solution because:
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Serverless ML Deployment: Amazon SageMaker endpoints provide a fully managed service for deploying machine learning models without requiring infrastructure management.
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No Server Management: Unlike EC2 (Option A) and EKS (Option B) which require server provisioning, scaling, and maintenance, SageMaker handles all infrastructure management automatically.
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Purpose-built for ML: SageMaker is specifically designed for the complete ML lifecycle, including model deployment and inference, while CloudFront with S3 (Option C) is for content delivery, not ML model serving.
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Automatic Scaling: SageMaker endpoints automatically scale based on traffic patterns, ensuring high availability without manual intervention.
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Integration with AWS ML Services: SageMaker integrates seamlessly with other AWS services for data preprocessing, training, and monitoring.
Why other options are incorrect:
- A. Amazon EC2: Requires manual server provisioning, configuration, scaling, and maintenance
- B. Amazon EKS: Requires Kubernetes cluster management, node provisioning, and infrastructure oversight
- C. Amazon CloudFront with S3: Designed for content delivery, not for serving ML models with inference capabilities
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