PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A team is operationalizing a machine learning pipeline using Vertex AI. They want to automatically track experiment runs, log model parameters and metrics, and store model artifacts for reproducibility. They also need to capture lineage between pipeline components (e.g., which dataset and hyperparameter tuning job produced a model). Which TWO services should they use together to achieve this? (Choose two.)
⚠ Common exam trap
PMLE often tests the overlap between Experiments and Metadata — candidates pick one service thinking it covers both experiment tracking and lineage, when the scenario explicitly requires both capabilities together.
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
✓
Vertex AI Metadata
Vertex AI Experiments (D) is correct because it automatically tracks and compares experiment runs, logging parameters, metrics, and artifacts so results are reproducible and comparable across training runs. Vertex AI Metadata (C) is correct because it records lineage and context for ML artifacts, capturing relationships such as which dataset and hyperparameter tuning job produced a given model, which is exactly the lineage requirement. Together they cover both experiment tracking and artifact lineage. Vertex AI Model Registry (A) manages model versions and deployment lifecycle but does not itself track experiment runs or component lineage. Vertex AI Feature Store (B) serves and manages feature values for training/serving, not experiment tracking or lineage. Vertex AI Workbench (E) is a notebook development environment and does not provide the required automatic tracking or lineage services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertex AI Model Registry
Why it's wrong here
Model Registry catalogues and versions trained models for deployment; it does not track experiment runs, parameters, metrics or the lineage linking datasets and tuning jobs to a model. It is tempting because it stores model artifacts, and it would be correct when the requirement is governing model versions and their deployment stages.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store serves and manages feature values for training and online serving; it does not record experiment runs, parameters, metrics or component lineage. It is tempting because it is a genuine Vertex AI pipeline component, and it would be the right choice when the requirement is consistent feature reuse across training and prediction.
- ✓
Vertex AI Metadata
Why this is correct
Vertex AI Metadata records and stores lineage artefacts, capturing which dataset, pipeline component and hyperparameter tuning job produced each model. This satisfies the lineage requirement, letting teams trace model provenance across pipeline components for reproducibility and audit.
- ✓
Vertex AI Experiments
Why this is correct
Vertex AI Experiments automatically tracks runs, logging parameters, metrics and artefacts for each training attempt. This satisfies the experiment-tracking and reproducibility requirement, complementing Metadata's lineage records by capturing the parameters and metrics of individual runs.
- ✗
Vertex AI Workbench
Why it's wrong here
Workbench provides managed JupyterLab notebook instances for interactive development; it neither logs experiment runs and artifacts nor captures pipeline component lineage. It is tempting because notebooks are where experiments are often written, and Workbench would be correct when the requirement is an interactive coding environment integrated with Vertex AI.
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Same concept, more angles
7 more ways 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 team wants to track the lineage of ML pipeline runs, including which datasets, parameters, and models were used in each execution. Which Vertex AI service should they use?
easy- ✓ A.Vertex AI Metadata
- B.Vertex AI Feature Store
- C.Vertex AI Model Registry
- D.Vertex AI Experiments
Why A: Vertex AI Metadata (part of Vertex ML Metadata) is the service designed to record and query ML metadata, including artifacts (datasets, models), executions (pipeline runs), and events, forming a lineage graph. It captures which datasets, parameters, and models were used in each pipeline execution, enabling reproducibility and auditability. This directly matches the requirement to track lineage of ML pipeline runs.
Variation 2. Which THREE are valid uses of Vertex AI Metadata?
easy- ✓ A.Record the execution of a pipeline step
- ✓ B.Track which dataset was used to train a model
- ✓ C.Query upstream sources of a model
- D.Deploy a model to an endpoint
- E.Store hyperparameter values of an experiment run
Why A: Vertex AI Metadata is a managed ML metadata store that records artifacts, executions, and their lineage relationships, so option A is correct because each pipeline step runs as an Execution resource whose inputs, outputs, and parameters can be logged for reproducibility and auditing. Option B is correct because datasets and models are Artifacts, and Metadata lets you record the training Execution that links a specific dataset artifact to the resulting model artifact, capturing provenance. Option C is correct because Metadata's lineage tracking APIs (e.g., via the Vertex AI SDK's get_execution, get_artifact, and lineage queries) let you traverse relationships to discover which datasets, executions, or artifacts are upstream of a given model. Option D is not a Metadata function; deploying a model to an endpoint is done through Vertex AI Endpoints (e.g., Model.deploy or the endpoints API), not the metadata store. Option E is not the intended use of Metadata as a primary store; hyperparameter values belong to Vertex AI Experiments (backed by the Experiments/MLMD tracking), and while Metadata can log parameters on executions, storing experiment hyperparameters is the role of Vertex AI Experiments rather than a valid standalone use of Metadata.
Variation 3. A team uses Vertex AI Pipelines and wants to track lineage of artifacts and executions. Which three resources should they use? (Choose three.)
hard- ✓ A.Artifacts
- B.Vertex AI Experiments
- ✓ C.Vertex AI Metadata
- D.Model Registry
- ✓ E.Executions
Why A: Vertex AI Metadata is the core lineage service that stores and connects metadata about ML resources, so option C is correct because it provides the underlying metadata store for tracking lineage. Within that metadata store, Artifacts (option A) represent the inputs and outputs of pipeline steps—such as datasets, models, and metrics—and are the nodes whose lineage is tracked. Executions (option E) represent a single run of a pipeline step or component and record the events that consume and produce artifacts, which is exactly what links artifacts together into a lineage graph. Vertex AI Experiments (option B) is for tracking and comparing experiment runs and metrics, not for artifact/execution lineage, and Model Registry (option D) is for managing model versions and deployment, not for general lineage tracking.
Variation 4. A team uses Vertex AI Metadata to track pipeline runs. They need to identify all artifacts that were generated by a particular pipeline execution. Which API method should they use?
hard- A.List executions and then list artifacts separately
- ✓ B.Use the lineage query API with the execution ID
- C.Create a context and query executions
- D.Query artifacts by filter on execution ID
Why B: The Vertex AI Metadata lineage query API accepts an execution ID and returns all artifacts, contexts, and events connected to that execution, giving the full provenance graph in one call. This is the purpose-built method for tracing which artifacts a pipeline run produced.
Variation 5. A machine learning pipeline in Vertex AI produces a dataset artifact, a trained model, and evaluation metrics. The team wants to query the lineage to find all downstream artifacts that depend on a particular dataset. Which Vertex AI service should they use?
hard- A.Vertex AI Feature Store
- B.Vertex AI Experiments
- C.Vertex AI Model Registry
- ✓ D.Vertex AI Metadata
Why D: Vertex AI Metadata is the service that records and stores ML metadata — artifacts, executions, and contexts — and their relationships, forming the lineage graph. It lets you query upstream and downstream dependencies of any artifact, such as finding all models and metrics derived from a dataset. This is exactly the lineage-query capability the team needs.
Variation 6. An organization uses Vertex AI Pipelines and wants to track the lineage of datasets, models, and metrics across pipeline runs. They need to query upstream and downstream dependencies of an artifact. Which service should they use?
medium- A.Vertex AI Feature Store
- B.Vertex AI Experiments
- C.Vertex AI Model Registry
- ✓ D.Vertex AI Metadata
Why D: Vertex AI Metadata stores the ML metadata graph produced by Vertex AI Pipelines, including artifacts (datasets, models, metrics), executions, and their input/output relationships. It exposes APIs to traverse this graph in both directions, so you can query upstream sources and downstream dependents of any artifact. This is the correct service for cross-run lineage queries.
Variation 7. Which Vertex AI service is used to track the lineage of ML pipeline components, artefacts, and executions?
easy- ✓ A.Vertex AI Metadata
- B.Vertex AI Model Registry
- C.Vertex AI Feature Store
- D.Vertex AI Experiments
Why A: Vertex AI Metadata is the service that tracks the lineage of ML pipeline components, artefacts, and executions. It provides a centralized repository to store and manage metadata about ML workflows, enabling reproducibility, auditing, and collaboration. By recording relationships between components, artefacts, and executions, it allows you to trace the provenance of models and data.
JA
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.