The correct answer is that the model was imported from a custom container without a serving specification or artifact. This is the most likely reason because Vertex AI requires both a defined predict route (the serving specification) and a model artifact (such as a saved model file) within the container to know how to handle inference requests; without these, the platform cannot instantiate the model for deployment, even though the `gcloud` command confirms it is registered. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of Vertex AI’s deployment prerequisites versus mere model registration—a common trap is assuming a listed model is automatically deployable. Remember the memory tip: “No spec, no artifact, no deploy”—if either piece is missing, the model stays stuck in registry limbo.
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.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Option B is correct because 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.
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.
✗
The model is still in training and not yet ready.
Why it's wrong here
A model that is in training would not appear in the list.
✓
The model was imported from a custom container but without a serving specification or artifact.
Why this is correct
A model must have a serving container or artifacts to be deployable.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The model does not have the correct IAM permissions assigned to the deployment service account.
Why it's wrong here
Permissions affect access, not deployability.
✗
The region for the endpoint is different from the model's region.
Why it's wrong here
Region mismatch would cause a different error, not 'not deployable'.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI requires a model to have a `containerSpec` with a `predictRoute` (or `healthRoute`) and an `artifactUri` pointing to a model artifact in Cloud Storage. When importing a custom container without these, the model's `deployable` field remains `false`. In practice, this often occurs when a data scientist exports a model from a training job but forgets to include the serving infrastructure, such as a Docker image with a gRPC or HTTP prediction handler.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: The model was imported from a custom container but without a serving specification or artifact. — Option B is correct because 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.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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