This PMLE practice question tests your understanding of serving and scaling models. 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.
A company serves a scikit-learn model on Vertex AI Prediction but receives a 400 error with 'Prediction failed: Model evaluation error'. 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.
The input data format is incorrect
Why wrong: Incorrect input usually gives a different error.
The model was trained with a different framework
Why wrong: Vertex AI supports scikit-learn natively.
The model uses a scikit-learn version not supported by Vertex AI
Version mismatch causes evaluation failure.
The endpoint is overloaded and timing out
Why wrong: Timeout errors are different from evaluation errors.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The model uses a scikit-learn version not supported by Vertex AI
Vertex AI Prediction supports specific versions of scikit-learn for serving models. If the model was trained with a version that is not in the supported list (e.g., 0.19, 0.20, 0.22, 0.23, 0.24, 1.0, 1.1), the prediction endpoint will fail with a 'Model evaluation error' because the underlying runtime cannot load the serialized model (e.g., pickle or joblib file). This is the most likely cause of a 400 error when the input format is otherwise correct.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
The input data format is incorrect
Why it's wrong here
Incorrect input usually gives a different error.
The model was trained with a different framework
Why it's wrong here
Vertex AI supports scikit-learn natively.
The model uses a scikit-learn version not supported by Vertex AI
Why this is correct
Version mismatch causes evaluation failure.
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 is overloaded and timing out
Why it's wrong here
Timeout errors are different from evaluation errors.
Common exam traps
Google Cloud often tests the misconception that a 400 error always indicates a client-side input format issue, but here the error message 'Model evaluation error' points to a server-side model loading failure due to version incompatibility, not the input data.
Detailed technical explanation
Vertex AI Prediction uses a custom container or prebuilt runtime for scikit-learn that includes specific library versions. When a model is serialized with pickle or joblib, the runtime must have the exact same scikit-learn version (and its dependencies like NumPy) to deserialize it. If the version mismatch occurs, the Python interpreter raises an ImportError or AttributeError during model loading, which Vertex AI catches and returns as a 'Model evaluation error'. This is a common pitfall when models are trained locally with a newer scikit-learn version and then uploaded to Vertex AI without checking the supported versions list.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
The correct answer is: The model uses a scikit-learn version not supported by Vertex AI — Vertex AI Prediction supports specific versions of scikit-learn for serving models. If the model was trained with a version that is not in the supported list (e.g., 0.19, 0.20, 0.22, 0.23, 0.24, 1.0, 1.1), the prediction endpoint will fail with a 'Model evaluation error' because the underlying runtime cannot load the serialized model (e.g., pickle or joblib file). This is the most likely cause of a 400 error when the input format is otherwise correct.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PMLE 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 PMLE exam.
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