PMLE Scaling Prototypes into ML Models Practice Question
A team is using Vertex AI Pipelines to orchestrate a machine learning workflow. They want to ensure that the pipeline can be reproduced and that artifacts are tracked. Which two of the following practices should they follow? (Choose two.)
⚠ Common exam trap
The trap here is thinking that manual artifact management or disabling caching is necessary for reproducibility, but automated metadata tracking and component versioning are the core practices.
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
✓
Version all pipeline components and store them in a version control system.
Versioning pipeline components and using Vertex AI Metadata are key practices for reproducibility and artifact tracking. Versioning ensures that the exact code and dependencies are captured, while Metadata automatically records parameters, metrics, and artifacts for each run, providing lineage and enabling reproducibility.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Version all pipeline components and store them in a version control system.
Why this is correct
Versioning pipeline components ensures that changes are tracked and that specific versions can be reproduced. Storing them in version control allows the team to revert to previous versions and understand the history of changes. This is essential for reproducibility and artifact tracking.
- ✗
Use the same container image tags for all pipeline steps to ensure consistency.
Why it's wrong here
Using the same container image tags can lead to inconsistencies if the tags are mutable and updated. It is better to use immutable tags or digests to ensure that the exact same image is used each time. This practice does not directly contribute to reproducibility and artifact tracking.
- ✗
Manually copy all artifacts to a Cloud Storage bucket after each run.
Why it's wrong here
Manually copying artifacts is error-prone and does not provide automatic tracking or lineage. Vertex AI Pipelines automatically stores artifacts in Cloud Storage and tracks them via Metadata. Manual copying adds unnecessary work and can lead to inconsistencies.
- ✓
Use Vertex AI Metadata to record parameters, metrics, and artifacts for each pipeline run.
Why this is correct
Vertex AI Metadata automatically tracks artifacts, parameters, and metrics when using Vertex AI Pipelines. It provides lineage tracking and helps with reproducibility by recording the exact inputs and outputs of each step. This is a core feature for artifact tracking in Vertex AI.
- ✗
Disable caching for all pipeline steps to ensure fresh executions.
Why it's wrong here
Disabling caching can increase cost and time, but it does not inherently improve reproducibility or artifact tracking. Caching, when used correctly with versioned components and metadata, can speed up pipeline runs without compromising reproducibility. The focus should be on versioning and metadata tracking.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.