- A
A model.joblib file (or model.pkl) along with any custom code.
AI Platform supports joblib/pickle for scikit-learn.
- B
A single .h5 file containing the model weights.
Why wrong: .h5 is for Keras models.
- C
A SavedModel directory containing the model for TensorFlow.
Why wrong: SavedModel is for TensorFlow, not scikit-learn.
- D
A model.pt file for PyTorch models.
Why wrong: PyTorch uses .pt files, but AI Platform supports PyTorch via custom containers.
Question 941 of 1,000
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.
A team has trained a scikit-learn model and wants to deploy it to AI Platform Prediction for online predictions. What is the required format for the model artifact?
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
✓
A model.joblib file (or model.pkl) along with any custom code.
AI Platform Prediction (now Vertex AI) supports scikit-learn models natively. The required artifact format is a serialized model file (model.joblib or model.pkl) optionally accompanied by any custom code dependencies. This is because scikit-learn models are pickled objects, and the platform deserializes them using the same Python environment specified in the runtime version.
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.
- ✓
A model.joblib file (or model.pkl) along with any custom code.
Why this is correct
AI Platform supports joblib/pickle for scikit-learn.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A single .h5 file containing the model weights.
Why it's wrong here
.h5 is for Keras models.
- ✗
A SavedModel directory containing the model for TensorFlow.
Why it's wrong here
SavedModel is for TensorFlow, not scikit-learn.
- ✗
A model.pt file for PyTorch models.
Why it's wrong here
PyTorch uses .pt files, but AI Platform supports PyTorch via custom containers.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistakenly believe that a single universal model file format (e.g., .h5 or SavedModel) works for all frameworks on Vertex AI, but each framework has its own required format. For scikit-learn, it must be a .joblib or .pkl file.
Detailed technical explanation
How to think about this question
Under the hood, AI Platform Prediction uses a prediction wrapper that loads the model via joblib.load() or pickle.load() from the specified artifact path. The model must be serialized with the exact same scikit-learn version as the runtime environment, otherwise deserialization can fail due to API incompatibilities. In real-world scenarios, teams often include a custom prediction routine (e.g., a Python class) to preprocess input or postprocess output, which is packaged alongside the model file.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
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: A model.joblib file (or model.pkl) along with any custom code. — AI Platform Prediction (now Vertex AI) supports scikit-learn models natively. The required artifact format is a serialized model file (model.joblib or model.pkl) optionally accompanied by any custom code dependencies. This is because scikit-learn models are pickled objects, and the platform deserializes them using the same Python environment specified in the runtime version.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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