mediumMultiple Choice
PDE Practice Question: Refer to the exhibit
Network Topology
Refer to the exhibit. What is the cause of this error?
⚠ Common exam trap
Google Cloud tests the distinction between endpoint creation and model deployment parameters in Vertex AI. Candidates often mistakenly assume that machine type can be set during endpoint creation, but it is only valid when deploying a model to the endpoint.
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 machine type flag is only used during model deployment, not endpoint creation
The error occurs because the `machine_type` flag is only valid during model deployment (when creating a deployment in Vertex AI), not during endpoint creation. When creating an endpoint, you specify the endpoint name and region, but the machine type is configured later when deploying a model to that endpoint. Attempting to set `machine_type` during endpoint creation causes a validation error because the API does not accept that parameter at that stage.
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 machine type flag is only used during model deployment, not endpoint creation
Why this is correct
The machine type is a deployment-time property of the Model resource, not the Endpoint. Passing it during endpoint creation is rejected because the API expects no such field there, which is exactly the constraint the error reflects.
- ✗
The endpoint name already exists
Why it's wrong here
The failure stems from the request referencing an endpoint that does not exist or is misnamed, not from a duplicate name conflict. It is tempting because endpoint names must be unique within a project, so a duplicate-name error would occur when creating an endpoint that already exists.
- ✗
The user must specify a model name
Why it's wrong here
The error is produced by a malformed or unsupported request payload rather than a missing model name, which the platform supplies by default. It is tempting because some prediction APIs do require an explicit model identifier, and omitting it would then cause a validation failure.
- ✗
The region is missing
Why it's wrong here
The error arises from the specified region not being enabled or available for the service, so requests to that regional endpoint fail. It is tempting because region misconfiguration does genuinely cause endpoint and connectivity errors, and selecting a supported region would be the fix in that scenario.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.