- A
Package the model in a custom container with a web server (e.g., FastAPI) and deploy to Vertex AI.
Correct: Custom container allows full control over PyTorch serving environment.
- B
Use Vertex AI's pre-built PyTorch container and upload the state dictionary.
Why wrong: Vertex AI does not provide an official pre-built PyTorch container; custom container is needed.
- C
Export the PyTorch model to a SavedModel format and deploy using Vertex AI's pre-built TensorFlow container.
Why wrong: This requires conversion and may not preserve all PyTorch features; not recommended.
- D
Convert the model to TensorFlow.js and deploy to Cloud Functions.
Why wrong: Cloud Functions is not designed for GPU inference and conversion is unnecessary.
Question 682 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling 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 data scientist wants to deploy a model trained with PyTorch to a Vertex AI endpoint for online predictions. What is the recommended approach?
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
✓
Package the model in a custom container with a web server (e.g., FastAPI) and deploy to Vertex AI.
Option A is correct because Vertex AI requires a custom container for PyTorch models, as it does not provide a pre-built PyTorch serving container. The recommended approach is to package the trained PyTorch model with a web server like FastAPI (or Flask) that loads the model and exposes an HTTP endpoint for predictions. This container is then deployed to Vertex AI for online predictions, giving full control over the inference environment and dependencies.
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.
- ✓
Package the model in a custom container with a web server (e.g., FastAPI) and deploy to Vertex AI.
Why this is correct
Correct: Custom container allows full control over PyTorch serving environment.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Vertex AI's pre-built PyTorch container and upload the state dictionary.
Why it's wrong here
Vertex AI does not provide an official pre-built PyTorch container; custom container is needed.
- ✗
Export the PyTorch model to a SavedModel format and deploy using Vertex AI's pre-built TensorFlow container.
Why it's wrong here
This requires conversion and may not preserve all PyTorch features; not recommended.
- ✗
Convert the model to TensorFlow.js and deploy to Cloud Functions.
Why it's wrong here
Cloud Functions is not designed for GPU inference and conversion is unnecessary.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that Vertex AI provides pre-built containers for all major frameworks, but in reality, only TensorFlow, scikit-learn, and XGBoost have official pre-built containers; PyTorch requires a custom container.
Detailed technical explanation
How to think about this question
Under the hood, the custom container approach uses a web server (e.g., FastAPI with Uvicorn) that loads the PyTorch model via `torch.load()` or `torch.jit.load()` and runs inference on incoming POST requests. Vertex AI expects the container to listen on port 8080 and respond with predictions in a specific JSON format. A real-world scenario is deploying a fine-tuned BERT model for NLP inference, where the custom container allows you to include tokenizers, handle batching, and use GPU acceleration via CUDA, which is not possible with pre-built containers or serverless functions.
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.
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Package the model in a custom container with a web server (e.g., FastAPI) and deploy to Vertex AI. — Option A is correct because Vertex AI requires a custom container for PyTorch models, as it does not provide a pre-built PyTorch serving container. The recommended approach is to package the trained PyTorch model with a web server like FastAPI (or Flask) that loads the model and exposes an HTTP endpoint for predictions. This container is then deployed to Vertex AI for online predictions, giving full control over the inference environment and dependencies.
What should I do if I get this PMLE 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.
About these practice questions
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Last reviewed: Jul 4, 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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