PMLE Automating and Orchestrating ML Pipelines Practice Question
What is the purpose of the 'importer' component in Vertex AI Pipelines?
⚠ Common exam trap
PMLE often tests whether candidates confuse the Importer (existing Vertex AI artifacts) with data ingestion components (external raw data) — the word 'import' is deliberately ambiguous.
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
✓
To import existing ML artifacts (e.g., models, datasets) into a pipeline as inputs.
The Importer component in Vertex AI Pipelines (a Kubeflow Pipelines component) brings existing ML artifacts — such as pre-trained models, datasets, or other resources already registered in Vertex AI — into a pipeline as inputs. It creates an artifact reference so downstream components can consume it without regenerating it. This is essential for reusing models or datasets across pipeline runs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To import data from external sources into the pipeline.
Why it's wrong here
The importer component brings an existing artefact, such as a trained model or dataset, into a pipeline run as an input; it does not fetch data from external sources. That is tempting because ingestion sounds like importing, but data loading is handled by dedicated components like ExampleGen.
- ✓
To import existing ML artifacts (e.g., models, datasets) into a pipeline as inputs.
Why this is correct
The importer component registers an existing artefact, such as a model or dataset already in Cloud Storage or Artifact Registry, as a pipeline input without rerunning upstream steps. This lets pipelines consume pre-existing ML artefacts as declared inputs.
- ✗
To import Python libraries into the pipeline environment.
Why it's wrong here
The importer component loads an existing artefact, such as a model or dataset, into the pipeline graph; Python libraries are installed via container images or package specifications. Dependency installation is tempting because both involve bringing something external in, but library management belongs to the pipeline's runtime environment, not a component.
- ✗
To import pipeline definitions from other projects.
Why it's wrong here
The importer component loads an existing artefact, such as a model or dataset, into the pipeline as an input, not pipeline definitions from other projects. Cross-project pipeline reuse is tempting because importers do bring external artefacts in, but pipeline definitions are shared through templates or registries instead.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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