The answer is that the model version is not deployed to a Vertex AI endpoint. This error occurs because in Vertex AI, a model version exists as a trained artifact in the Model Registry, but it cannot serve predictions until it is explicitly deployed to an endpoint—a process that allocates compute resources and exposes a serving API. The log entry’s direct reference to the model version’s deployment status confirms this as the root cause, not a misconfigured endpoint or an expired model. On the Google Professional Data Engineer exam, this scenario tests your understanding of the Vertex AI deployment lifecycle, often appearing in troubleshooting questions where a developer mistakenly calls predict() on a model version ID that was never deployed. A common trap is confusing model registration with deployment; remember that registration only stores the model, while deployment activates it for inference. Memory tip: “Register to store, deploy to serve.”
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Exhibit
{
"severity": "ERROR",
"message": "Prediction failed: Model 'projects/my-project/models/12345/versions/v1' not found.",
"timestamp": "2024-01-20T10:00:00Z",
"request": "POST /v1/projects/my-project/models/12345:predict"
}
Refer to the exhibit. A developer sees this log entry when trying to get a prediction. What is the most likely cause?
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 version is not deployed
The log entry indicates that the model version specified in the request is not currently deployed to the serving infrastructure. In Google Cloud's Vertex AI, a model version must be explicitly deployed to an endpoint before it can serve predictions; attempting to predict against a non-deployed version returns an error. This is the most likely cause because the error message directly references the model version's deployment status.
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 ID is incorrect
Why it's wrong here
The model ID '12345' might be correct; the error specifically says version not found, not model not found.
✓
The model version is not deployed
Why this is correct
A model version must be deployed to an endpoint to serve predictions; 'not found' suggests it is not deployed.
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 endpoint does not exist
Why it's wrong here
The request is to the model resource, not an endpoint; the error is about the model version.
✗
The project ID is wrong
Why it's wrong here
The project ID is included in the model path; if incorrect, the error would be different.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between model registry operations (uploading, versioning) and serving operations (deploying, predicting), trapping candidates who assume any model version in the registry is automatically available for predictions.
Detailed technical explanation
How to think about this question
In Vertex AI, a model version is a snapshot of the model artifact, and deploying it creates a dedicated serving container on a compute resource (e.g., a VM or GKE cluster). The deployment status is tracked in the endpoint's 'deployedModels' list; if the version is not in this list, the prediction request fails with a 'Model version not deployed' error. This is distinct from model registry operations, where versions can exist without being deployed.
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.
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model version is not deployed — The log entry indicates that the model version specified in the request is not currently deployed to the serving infrastructure. In Google Cloud's Vertex AI, a model version must be explicitly deployed to an endpoint before it can serve predictions; attempting to predict against a non-deployed version returns an error. This is the most likely cause because the error message directly references the model version's deployment status.
What should I do if I get this PDE 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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