PMLE Automating and Orchestrating ML Pipelines Practice Question

An ML engineer is designing a Vertex AI Pipeline that includes a custom training component. The component must read a dataset from a Cloud Storage bucket and write the trained model to another Cloud Storage location. The engineer wants the component to be reusable across pipelines and to ensure that the pipeline tracks the exact dataset and model artifacts. Which approach should the engineer take?

⚠ Common exam trap

The trap here is assuming that passing URIs as plain string parameters is sufficient for artifact tracking, when only declared artifacts provide lineage and metadata.

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

✓

Pass the Cloud Storage URIs as component inputs and outputs, and declare them as artifacts of type Dataset and Model.

Using artifact inputs and outputs with types Dataset and Model ensures that Vertex AI Pipelines tracks lineage and metadata. It keeps the component reusable because the actual URIs are passed at runtime. Declaring artifacts also enables the pipeline to display them in the UI and to use them for caching and conditional execution.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Pass the Cloud Storage URIs as component inputs and outputs, and declare them as artifacts of type Dataset and Model.
Why this is correct
Declaring inputs and outputs as artifacts of type Dataset and Model allows Vertex AI Pipelines to track lineage and metadata automatically. The component remains reusable because the URIs are parameterized. This approach also enables the pipeline to visualize the artifacts and their relationships in the Vertex AI Pipelines UI, and supports artifact-based triggering and caching.
✗
Hardcode the Cloud Storage URIs inside the component's container code and use environment variables for configuration.
Why it's wrong here
Hardcoding URIs makes the component non-reusable and tightly coupled to a specific environment. It also prevents Vertex AI Pipelines from tracking the dataset and model artifacts because they are not declared as inputs or outputs. Environment variables can provide configuration, but they do not provide artifact lineage or metadata tracking.
✗
Use a single string parameter for the dataset URI and a single string parameter for the model URI, without declaring artifact types.
Why it's wrong here
While string parameters allow reusability, they do not provide artifact typing or lineage tracking. Vertex AI Pipelines treats them as plain parameters, so the UI cannot display them as artifacts or track their relationships. Using artifact types is necessary to capture metadata such as the model's framework or the dataset's schema.
✗
Mount a Cloud Storage bucket as a volume in the component container and write the model directly to the mounted path.
Why it's wrong here
Mounting a Cloud Storage bucket as a volume is not natively supported in Vertex AI Pipelines custom components. Even if it were, writing directly to a mounted path bypasses artifact declaration, so the pipeline would not track the model as an artifact. This approach also reduces portability because it relies on a specific mount configuration.

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PMLE Automating and Orchestrating ML Pipelines Practice Question

Why each option matters

Option-by-option breakdown

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