Generative AI Leader Fundamentals of Generative AI Practice Question
Network Topology
Refer to the exhibit. A data scientist runs the gcloud command and sees the model listed. However, when they try to deploy the model to an endpoint, they get an error: 'Model is not deployable'. What is the most likely reason?
⚠ Common exam trap
Google Cloud often tests the misconception that a model listed in the registry is automatically deployable, but the trap here is that Vertex AI separates model registration from deployment readiness, requiring explicit serving configuration for custom containers.
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 model was imported from a custom container but without a serving specification or artifact.
A model imported from a custom container must include a serving specification (e.g., a `predict` route) and an artifact (e.g., a saved model file) to be deployable. Without these, Vertex AI cannot determine how to serve predictions, resulting in the 'Model is not deployable' error. The `gcloud` command listing the model only confirms its registration, not its readiness for deployment.
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 model is still in training and not yet ready.
Why it's wrong here
A model still training would typically not appear as listed and ready in the registry; the error concerns deployability, not training state. This is tempting because unfinished training genuinely blocks deployment, but that condition produces a different status than 'not deployable'.
- ✓
The model was imported from a custom container but without a serving specification or artifact.
Why this is correct
Importing a custom container without a serving specification or artifact leaves Vertex AI no defined prediction routine or weights to load, so the model registers but cannot be deployed. The serving specification is what makes a model deployable to an endpoint.
- ✗
The model does not have the correct IAM permissions assigned to the deployment service account.
Why it's wrong here
Missing IAM permissions produce authorisation or permission-denied errors at request time, not a 'Model is not deployable' response. IAM roles are the right fix when a service account cannot access resources, but deployability reflects the model artefact's supported container or framework, not access control.
- ✗
The region for the endpoint is different from the model's region.
Why it's wrong here
Region mismatch produces a deployment error, but the message 'Model is not deployable' specifically indicates an unsupported model format or framework rather than a location conflict. It tempts because region alignment genuinely causes failures when the endpoint region differs from where the model artefact resides.
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