Fundamentals of Generative AI →mediumMultiple ChoiceObjective-mapped

Generative AI Leader Fundamentals of Generative AI Practice Question

Exhibit

model:
  name: "projects/my-project/locations/us-central1/models/1234"
  explanation_spec:
    metadata:
      inputs:
        my_input:
          input_tensor_name: "input"
          modality: "text"

Refer to the exhibit. A developer creates a model resource with this YAML config but gets an error that the model is not deployable. What is missing?

⚠ Common exam trap

Google Cloud often tests the misconception that `artifact_uri` is the key requirement for deployment, but the real mandatory field is the container specification that defines the runtime environment.

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

✓

container_spec

The error 'model is not deployable' occurs because the YAML config lacks a `container_spec` field. In Vertex AI, a model must specify how to serve predictions—either via a pre-built container (using `container_spec`) or a custom container. Without this, the model has no runtime environment and cannot be deployed to an endpoint.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
model_type
Why it's wrong here
model_type is not a required field.
✗
artifact_uri
Why it's wrong here
artifact_uri is needed for custom models, but the primary requirement is container_spec for deployment.
✓
container_spec
Why this is correct
container_spec is required to tell Vertex AI which container to use.
✗
description
Why it's wrong here
Description is optional, not required for deployability.

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Generative AI Leader Fundamentals of Generative AI Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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