PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
Your team is preparing to hand a trained model to a separate operations team that will deploy it to a Vertex AI endpoint. The operations team needs to understand the model's input schema, the training run that produced it, and which alias currently points to production. Which two Vertex AI resources should you share with them to provide this information? (Choose two.)
⚠ Common exam trap
The trap here is equating data access with model handoff, assuming the operations team needs the raw training data rather than the model's schema and version metadata.
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
✓
The registered model resource in Vertex AI Model Registry, including its version and alias.
A complete handoff to operations requires the registered model resource for version and alias information, and the training run record for parameters, metrics, and input schema. Feature stores, pipeline jobs, and raw data buckets serve different purposes and do not provide the deployment team with the model-specific governance details they need.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Vertex AI Pipeline job resource that was used to schedule nightly retraining.
Why it's wrong here
A pipeline job resource describes an orchestration run, not the specific model version being handed off. It does not carry the alias information or the training run's logged schema, so the operations team would still lack the details required to deploy and validate the model.
- ✗
The Cloud Storage bucket containing the raw training data.
Why it's wrong here
Raw training data is not a model artifact and does not convey the input schema for serving or the alias pointing to production. Sharing it adds no governance value for deployment and could expose sensitive data unnecessarily, so it is not one of the resources the operations team needs.
- ✓
The registered model resource in Vertex AI Model Registry, including its version and alias.
Why this is correct
The Model Registry resource holds the model version, its metadata, and aliases such as 'production'. Sharing this resource name lets the operations team see exactly which version is aliased for production and deploy it, satisfying the alias and versioning part of the handoff.
- ✗
The Vertex AI Feature Store online store resource used during training.
Why it's wrong here
An online store resource serves feature values at low latency but does not describe a model's input schema or training provenance. Sharing it would not tell the operations team which training run produced the model or which alias is active, so it does not fulfill the stated handoff needs.
- ✓
The Vertex AI Experiments run that logged the training parameters, metrics, and input schema.
Why this is correct
The Experiments run records the parameters, metrics, and artifacts from training, and it can also store the input schema artifact. Sharing it gives the operations team the training context and schema they need to validate incoming requests, completing the handoff requirements.
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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
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