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MLA-C01 Practice Question: An ML engineer creates a SageMaker inference…
An ML engineer creates a SageMaker inference pipeline with two containers: a preprocessor and a predictor. The preprocessor is a lightweight Python script that transforms input data. How should the engineer structure the endpoints to ensure both containers run sequentially?
⚠ Common exam trap
It's easy for candidates to assume chaining containers requires external orchestration (like Lambda or separate jobs), but SageMaker's PipelineModel natively supports sequential container execution within a single endpoint, which is the simplest and most efficient approach.
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
✓
Create a PipelineModel in SageMaker with both containers listed in order: first preprocessor, then predictor.
SageMaker's PipelineModel allows you to define an ordered sequence of containers that are executed sequentially within a single HTTPS endpoint. When an inference request is made, the preprocessor container transforms the input, and the output is passed directly to the predictor container, all within the same endpoint invocation. This ensures low latency and tight coupling without needing external orchestration.
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 batch transform with two transform jobs chained together.
Why it's wrong here
Batch transform is for offline processing, not real-time inference.
- ✗
Use an AWS Lambda function as a proxy to invoke the preprocessor and then the predictor separately.
Why it's wrong here
Lambda adds complexity and latency; pipeline is the native way.
- ✗
Combine the preprocessor and predictor into a single Docker container.
Why it's wrong here
Combining reduces flexibility and reusability.
- ✓
Create a PipelineModel in SageMaker with both containers listed in order: first preprocessor, then predictor.
Why this is correct
PipelineModel automatically sends the output of the first container as input to the second.
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