PMLE Automating and Orchestrating ML Pipelines Practice Question
A data scientist is defining a Vertex AI pipeline and needs to include a step that imports a pre-existing model from Cloud Storage into the pipeline as an artifact. Which Kubeflow Pipelines SDK v2 component should they use?
⚠ Common exam trap
Watch out — candidates often confuse the Python class naming convention (capitalized `Importer`) with the actual SDK v2 function name (lowercase `importer`), or mistakenly think `dsl.Artifact` can import artifacts when it only defines the artifact schema.
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
✓
dsl.importer
The `dsl.importer` component in Kubeflow Pipelines SDK v2 is specifically designed to import existing artifacts (such as models, datasets, or metrics) from external storage (e.g., Cloud Storage) into a pipeline as a pipeline artifact. It allows you to reference a pre-existing model without retraining or re-uploading, making it the correct choice for this use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
dsl.Collected
Why it's wrong here
dsl.Collected groups a list of artifacts produced by a task, offering no mechanism to bring an external Cloud Storage model into the pipeline. It is tempting because it handles artifacts, but it is used for aggregating outputs of upstream tasks, not importing pre-existing files.
- ✓
dsl.importer
Why this is correct
dsl.importer registers an existing Cloud Storage artefact, such as a trained model, as a pipeline artefact without executing a container to recreate it. This satisfies the requirement to bring a pre-existing model into the pipeline graph as a usable input.
- ✗
dsl.Importer
Why it's wrong here
dsl.Importer registers an existing Cloud Storage artefact as a pipeline input, not a component that imports a model into a pipeline step. It is tempting because importing external artefacts is its purpose, but the stem asks for a component, which is a CustomTrainingJob or ModelImport op.
- ✗
dsl.Artifact
Why it's wrong here
dsl.Artifact is the base class representing a pipeline artifact, not a component that registers an existing Cloud Storage model. It is tempting because artifacts are the objects an importer produces, so naming one feels relevant, yet it cannot perform the import step itself.
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Same concept, more angles
1 more way this is tested on PMLE
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Variation 1. What is the purpose of the 'importer' component in Vertex AI Pipelines?
easy- A.To import data from external sources into the pipeline.
- ✓ B.To import existing ML artifacts (e.g., models, datasets) into a pipeline as inputs.
- C.To import Python libraries into the pipeline environment.
- D.To import pipeline definitions from other projects.
Why B: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.