The answer is the missing `container_spec` field. In Vertex AI, a model resource requires a `container_spec` to define the runtime environment for serving predictions, whether through a pre-built or custom container; without it, the model has no deployment configuration and triggers the "model not deployable" error. On the Google Cloud Generative AI Leader exam, this tests your understanding of the fundamental deployment prerequisites for Vertex AI models, often appearing as a YAML-based scenario where candidates must spot the omission. A common trap is assuming a model can deploy with just a name and artifact URI, but the platform strictly enforces the container specification to ensure a functional endpoint. Memory tip: think "no container, no deploy"—the `container_spec` is the bridge between your model artifact and a live endpoint.
Generative AI Leader Fundamentals of Generative AI Practice Question
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
description
Why it's wrong here
Description is optional, not required for deployability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI models require a `container_spec` that defines the image URI, health check routes, and environment variables for the serving container. Without this spec, the AI Platform cannot instantiate a pod to run inference. In practice, even a simple model like a scikit-learn pickle must be wrapped in a container (e.g., using the pre-built `us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24` image) to be deployable.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What does this Generative AI Leader question test?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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