mediumMultiple SelectObjective-mapped
ML Experiment Reproducibility in Vertex AI
A machine learning team is collaborating on a project using Vertex AI Experiments to track model training runs. They want to ensure that all team members can reproduce any experiment by using the same code, data, and environment. Which THREE actions should the team take?
Quick Answer
The answer is to store training code in a Cloud Source Repository with tags linked to experiment IDs, record the dataset path and version in experiment parameters, and log the complete environment specification including framework versions. These three actions are correct because they create a full lineage for each Vertex AI experiment, capturing the three critical pillars of reproducibility: code, data, and environment. By tagging code commits to specific experiment IDs, you ensure any team member can checkout the exact source; recording dataset parameters locks in the data source; and logging environment details prevents dependency drift. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of Vertex AI Experiments’ built-in lineage tracking, often appearing as a multi-select scenario where distractors include vague actions like “save the model artifact” without versioning or “use default runtime” without pinning dependencies. A common trap is forgetting that environment specification must be explicit, not implicit. Memory tip: think “Code, Data, Env” as the three legs of the reproducibility stool—if any leg is missing, the experiment falls over.
⚠ Common exam trap
Google Cloud often tests the distinction between actions that enable reproducibility versus actions that improve model performance or access control, so candidates mistakenly select hyperparameter tuning or service account sharing as reproducibility measures.
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
✓
Store the training code in a Cloud Source Repository and tag commits with the experiment ID.
Storing training code in a Cloud Source Repository with tags linked to experiment IDs ensures that every team member can retrieve the exact code version used for a given experiment. This is a core reproducibility practice in Vertex AI Experiments, where the code snapshot is a key component of the experiment lineage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Store the training code in a Cloud Source Repository and tag commits with the experiment ID.
Why this is correct
This ensures the exact code version is tied to the experiment.
- ✓
Build a custom container image for training and push it to Artifact Registry with a fixed tag.
Why this is correct
A fixed container image ensures the same environment is used across runs.
- ✓
Record the path and version of the training dataset in the experiment parameters.
Why this is correct
This allows others to use the exact same data for reproduction.
- ✗
Share a service account key with all team members so they can access the same resources.
Why it's wrong here
Sharing keys is a security risk and not a standard reproducibility practice.
- ✗
Use Vertex AI's hyperparameter tuning job to automatically find the best parameters.
Why it's wrong here
Automatic tuning can change parameters between runs, making exact reproduction difficult.
Go deeper
Related to this question
About these practice questions
One of 990 original PMLE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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. A data science team is collaborating on a project to build a churn prediction model. They use Vertex AI Workbench instances for development. Each data scientist has their own instance with a persistent disk. They share code via a GitHub repository. They want to ensure that the model training is reproducible across different team members' environments. Currently, they manually install Python packages in their instances, and they have noticed that the model metrics differ slightly between runs on different instances. Which of the following is the best action to ensure reproducibility?
medium- A.Standardize the instance machine type and ensure all have the same number of CPUs.
- B.Use Cloud Functions to run the training code instead.
- ✓ C.Use Vertex AI Experiments with a fixed environment by specifying a prebuilt container.
- D.Create a custom Docker image with all dependencies and use it in Vertex AI Training jobs.
- E.Ask all team members to use the same Python virtual environment and install packages from a requirements.txt file.
Why C: Vertex AI Experiments with a prebuilt container ensures a fixed, reproducible environment by pinning the exact OS, Python version, and all dependencies. This eliminates the variability introduced by manual package installations and differing instance configurations, directly addressing the team's issue of inconsistent model metrics across 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.