PMLE Automating and Orchestrating ML Pipelines Practice Question
A machine learning engineer needs to create a pipeline that runs a custom container component on Vertex AI. The container expects a Cloud Storage path as input and outputs a model artifact. Which component type should they define using the Kubeflow Pipelines SDK v2?
⚠ Common exam trap
It's easy for candidates to confuse the Importer component (which only imports existing artifacts) with a component that runs a container to produce an artifact, or they mistakenly think Google Cloud Pipeline Components can wrap any custom container when they only provide pre-built Google service integrations.
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
✓
Container component using @dsl.container_component
The Kubeflow Pipelines SDK v2 provides the @dsl.container_component decorator specifically for defining components that wrap custom container images. This allows the engineer to specify the container image, input/output paths (like a Cloud Storage path), and artifact metadata, enabling Vertex AI to execute the container as a pipeline step and capture the model artifact.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Google Cloud Pipeline Components (GCPC) for custom containers
Why it's wrong here
GCPC pre-built components wrap specific Google services, not arbitrary user containers, so they cannot execute the custom image. They suit pipelines using standard Vertex AI or BigQuery steps; a custom container requires the dedicated custom container component definition instead.
- ✗
Python function component using @dsl.component
Why it's wrong here
A @dsl.component Python function runs as a lightweight Python execution, not inside the supplied container image, so the custom container never executes. Python function components suit simple inline logic; running a pre-built image demands a custom container component.
- ✗
Importer component to load the container as an artifact
Why it's wrong here
An Importer component only registers an existing artefact into the pipeline's metadata store; it executes no container and produces no model output. Importers suit bringing externally produced artefacts into lineage tracking, not running a container that trains or exports a model.
- ✓
Container component using @dsl.container_component
Why this is correct
The @dsl.container_component decorator lets you define a component from a custom container image, with typed inputs and outputs declared in the function signature. This satisfies the stem's need to pass a Cloud Storage path in and emit a model artifact out.
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