How to Deploy an ML Model Serverlessly on AWS?
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?
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
This question tests the ability to identify a fully managed, serverless ML inference solution; the trap is choosing EC2 or EKS, which require manual infrastructure management.
Amazon SageMaker endpoints provide a fully managed, serverless way to deploy machine learning models for real-time inference without provisioning or managing underlying infrastructure. Community consensus is 100% aligned on this answer.
Candidates often choose A (Amazon EC2) because it is a common deployment target, but EC2 requires provisioning, patching, and managing servers, violating the 'no infrastructure management' requirement.
Community Discussion (3 comments)
Comments & Corrections
No comments yet — spotted an error or have a note? Share it below.
Expert Analysis
Why Option D is Correct
Amazon SageMaker is a fully managed machine learning service that allows you to build, train, and deploy ML models at scale. When you deploy a model to a SageMaker endpoint, AWS automatically handles all the underlying infrastructure — including provisioning instances, load balancing, auto-scaling, and health monitoring. This perfectly satisfies the requirement of making predictions without managing servers or infrastructure. As community member aws_Tamilan noted, SageMaker automatically handles the provisioning, making it the ideal serverless ML deployment option.
Why the Other Options Are Wrong
- Option A (Amazon EC2): EC2 provides raw virtual servers. You are responsible for OS patching, runtime installation, scaling, and monitoring — directly contradicting the requirement.
- Option B (Amazon EKS): While EKS manages the Kubernetes control plane, you still manage worker nodes, container orchestration, and scaling policies. It is not a fully managed, serverless ML inference solution.
- Option C (Amazon CloudFront + S3): CloudFront is a CDN and S3 is object storage. Neither can execute ML model inference logic. This combination is used for static content delivery, not real-time predictions.
Community Consensus
The community voted 100% for Option D, with members like Jessiii and may2021_r confirming that SageMaker endpoints are the canonical serverless ML deployment pattern on AWS.
Official Reference
Exam Strategy
When a question emphasizes 'no managing servers or infrastructure,' immediately look for fully managed or serverless AWS services (SageMaker endpoints, Lambda, Fargate). Eliminate options that require you to manage compute, OS, or orchestration layers.
Related Analysis
Practice All AIF-C01 Questions
Access 100 questions with complete answers and detailed explanations.
View Full AIF-C01 Practice Test →