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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 processes stored datasets offline, so it cannot host a persistent endpoint serving real-time requests. Chaining two transform jobs also runs them as separate passes, not sequentially within one invocation. Inference pipelines exist precisely to run multiple containers in order behind a single endpoint.
- ✗
Use an AWS Lambda function as a proxy to invoke the preprocessor and then the predictor separately.
Why it's wrong here
Lambda invoking two separate endpoints introduces orchestration outside SageMaker and two network hops, and the containers do not run sequentially within one request. Lambda suits glue logic around endpoints. A SageMaker inference pipeline hosts both containers in one endpoint, passing the preprocessor's output directly to the predictor.
- ✗
Combine the preprocessor and predictor into a single Docker container.
Why it's wrong here
Merging both models into one image removes the container boundary the pipeline provides, forcing shared dependencies and preventing independent scaling or updates of preprocessor and predictor. Single containers suit one model with its own dependencies. Inference pipelines keep the containers separate while still running them sequentially.
- ✓
Create a PipelineModel in SageMaker with both containers listed in order: first preprocessor, then predictor.
Why this is correct
A PipelineModel chains containers so inference requests flow sequentially through each stage, satisfying the requirement that the preprocessor runs before the predictor. Listing them in order ensures the preprocessor's transformed output feeds the predictor within a single endpoint invocation.
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