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PMLE Practice Question: An ML engineer is scaling a prototype to…
An ML engineer is scaling a prototype to production using Vertex AI Pipelines. The pipeline includes data validation, preprocessing, training, and deployment steps. They want to ensure that the pipeline can be reproduced and audited. What is the best practice?
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
✓
Define the pipeline using Kubeflow Pipelines SDK and run it on Vertex AI Pipelines.
Vertex AI Pipelines is a fully managed service that automatically tracks artifacts, parameters, and lineage, ensuring reproducibility and auditability. Option A uses the Kubeflow Pipelines SDK to define the pipeline and runs it on Vertex AI Pipelines, which provides built-in tracking. Option B (Docker with fixed tags) lacks automated lineage tracking. Option C (no versioning) loses audit trail. Option D (requirements.txt only) addresses dependencies but not pipeline orchestration or artifact tracking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define the pipeline using Kubeflow Pipelines SDK and run it on Vertex AI Pipelines.
Why this is correct
Vertex AI Pipelines automatically tracks artifacts, parameters, and lineage.
- ✗
Use a Docker container with fixed tags and manually record runs.
Why it's wrong here
Manual recording is error-prone and not scalable.
- ✗
Store all data and models in a single Cloud Storage bucket with no versioning.
Why it's wrong here
Versioning is important for reproducibility; no versioning loses history.
- ✗
Pin all library versions in a requirements.txt file.
Why it's wrong here
Pinning versions is good but not sufficient; pipeline orchestration and tracking are needed.
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Same concept, more angles
1 more way this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An ML team is moving from a prototype Jupyter notebook to a production training pipeline. They want to ensure reproducibility. Which approach should they take?
easy- A.Use interactive parameter tuning.
- ✓ B.Use a container with fixed dependencies and record hyperparameters.
- C.Export the notebook's output model directly.
- D.Save the notebook as a .py file.
Why B: Using a container with fixed dependencies and recording hyperparameters ensures that the training environment and configuration are captured, enabling exact reproduction. Option A is wrong because interactive parameter tuning is not reproducible—it introduces manual adjustments. Option C is wrong because exporting the notebook's output model directly lacks environment tracking and hyperparameter records. Option D is wrong because saving the notebook as a .py file does not capture the full environment or dependencies.
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.