Generative AI Leader Fundamentals of Generative AI Practice Question
Exhibit
Error: Root model provided is not a valid model reference. Please provide a model reference in the format of "projects/{project}/locations/{location}/models/{model}" or "projects/{project}/locations/{location}/models/{model}:{version}".Refer to the exhibit. A developer sees this error when trying to deploy a model from Vertex AI Model Registry. What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the distinction between display names (non-unique, human-readable) and resource names (unique, API-required) in cloud services like Vertex AI, where candidates mistakenly assume display names can be used interchangeably with resource identifiers.
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 developer used the model display name instead of the full resource name
The error occurs because Vertex AI Model Registry requires the full resource name (e.g., 'projects/{project}/locations/{region}/models/{model_id}') to deploy a model, not just the display name. The display name is a human-readable label that is not unique within a project, while the full resource name uniquely identifies the model version. Using the display name causes the API to fail with a 'not found' or 'invalid argument' error.
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 region is not supported
Why it's wrong here
An unsupported region produces a location or resource-not-found error naming the region, not the permission or publication error shown. It is tempting because Vertex AI resources are regional, so region mismatches genuinely block deployment. The exhibit's message instead points to the model's registry state or access rights.
- ✓
The developer used the model display name instead of the full resource name
Why this is correct
Deployment APIs require the fully qualified resource name, formatted as projects/{project}/locations/{location}/models/{model}. Supplying only the display name fails resource resolution, producing the error, since display names are not unique identifiers within the Vertex AI Model Registry.
- ✗
The model is not published
Why it's wrong here
An unpublished model cannot be deployed, but that condition yields a distinct error about the model version lacking a deployed artifact or being unregistered, not the message shown. It is tempting because publishing to the registry is a genuine prerequisite. The exhibit instead indicates a permissions or project-scope problem.
- ✗
The model is in a different project
Why it's wrong here
Cross-project access fails only when the deploying principal lacks IAM roles on the model's project; the same project boundary alone does not block deployment. It is tempting because Vertex AI resources are project-scoped, so project mismatch is a real cause of access errors. The exhibit's specific message instead names the actual missing permission.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader 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 Generative AI Leader exam.