MLS-C01 Modeling Practice Question
Which THREE of the following are valid approaches for deploying a machine learning model to an Amazon SageMaker endpoint for real-time inference?
⚠ Common exam trap
The trap here is that candidates might confuse Amazon EMR's model serving capabilities (e.g., using Spark MLlib) with SageMaker's managed inference, or assume Lambda can handle large model artifacts despite its payload and timeout constraints.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a SageMaker Inference Pipeline with multiple containers
SageMaker Inference Pipelines allow you to chain multiple containers (e.g., preprocessing, prediction, postprocessing) into a single endpoint, enabling complex workflows for real-time inference. This is achieved by defining a sequence of Docker containers in the model definition, where each container's output is passed as input to the next, all within the same SageMaker endpoint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a SageMaker Inference Pipeline with multiple containers
Why this is correct
Inference pipelines allow chaining of preprocessing and prediction containers.
- ✓
Use a pre-built SageMaker container with built-in algorithms
Why this is correct
SageMaker provides optimized containers for common frameworks.
- ✗
Use Amazon EMR to host the model
Why it's wrong here
EMR is for processing large datasets using Hadoop/Spark, not for real-time inference.
- ✗
Deploy the model as an AWS Lambda function
Why it's wrong here
Lambda can be used with SageMaker but is not a native way to deploy a SageMaker endpoint.
- ✓
Bring your own Docker container
Why this is correct
SageMaker supports BYOC for custom inference code.
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.