PMLE Collaborating to manage data and models Practice Question

A company has multiple teams working on different models. They want to enforce consistent data preprocessing steps across all teams. Which approach should they take?

⚠ Common exam trap

Google Cloud often tests the distinction between 'sharing code' (e.g., packages) and 'sharing executable, environment-encapsulated pipeline steps' (e.g., components), leading candidates to choose a code-sharing option like Artifact Registry instead of the pipeline component approach that enforces consistency.

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 shared Vertex AI Pipelines components

Vertex AI Pipelines components allow teams to define reusable, versioned, and parameterized preprocessing steps that can be shared across models and pipelines. This ensures consistent execution of data transformations because each component encapsulates the exact code and environment, and pipelines enforce the same DAG of steps regardless of which team triggers them.

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 Cloud Composer to orchestrate preprocessing
Why it's wrong here
Cloud Composer orchestrates and schedules preprocessing tasks, but each DAG defines its own steps, so teams can still implement transformations differently. Composer suits workflow scheduling and dependency management. Enforcing identical preprocessing across teams needs a reusable, versioned component rather than orchestration alone.
✗
Write shared Python packages in Artifact Registry
Why it's wrong here
Shared Python packages in Artifact Registry distribute code, but nothing forces teams to call them, so preprocessing can still diverge. They suit versioned dependency management across projects. Enforcing consistency requires a pipeline component that every team's workflow must execute, not an optional import.
✗
Use Cloud Dataflow templates
Why it's wrong here
Dataflow templates run a fixed pipeline per job; they do not package reusable preprocessing logic that teams import into their own training code. Templates suit repeatable batch or streaming jobs, not enforcing shared transformations across independent model builds. A shared library is what teams actually call from their pipelines.
✓
Create shared Vertex AI Pipelines components
Why this is correct
Shared Vertex AI Pipelines components package preprocessing logic as reusable, versioned artefacts that every team imports into its own pipeline. This enforces identical preprocessing steps across teams, satisfying the consistency constraint without duplicating code per model.

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PMLE Collaborating to manage data and models Practice Question

Why each option matters

Option-by-option breakdown

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