The answer is to deploy to Vertex AI online prediction using a prebuilt container for scikit-learn. This is the correct choice because Vertex AI’s prebuilt containers are optimized specifically for frameworks like scikit-learn, handling model serialization and request routing without the overhead of custom container builds, which directly minimizes both latency and cost for a 200 MB model. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of Vertex AI’s managed serving options versus legacy AI Platform or serverless alternatives like Cloud Functions, which impose payload limits and cold-start penalties that make them unsuitable for models of this size. A common trap is to assume custom containers are always necessary, but the prebuilt container for scikit-learn already includes the required dependencies and is automatically scaled by Vertex AI. Memory tip: “Prebuilt for pre-trained” — if your framework has a prebuilt container, use it to avoid reinventing the wheel.
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.
You are deploying a scikit-learn model for online predictions. The model size is 200 MB. You want to minimize latency and cost. Which serving option should you choose?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy to Vertex AI online prediction using a prebuilt container for scikit-learn.
Vertex AI online prediction with custom containers is suitable for scikit-learn models. Vertex AI will host the container and scale. Using AI Platform or Cloud Functions with a 200 MB model might hit limits.
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.
✓
Deploy to Vertex AI online prediction using a prebuilt container for scikit-learn.
Why this is correct
Vertex AI provides optimized containers and autoscaling for online prediction.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Cloud Run with a custom container.
Why it's wrong here
Cloud Run can work but lacks Vertex AI's model management and monitoring features.
✗
Create a Kubernetes cluster on GKE and deploy the model there.
Why it's wrong here
Too much overhead; Vertex AI simplifies deployment.
✗
Export the model as a Cloud Function.
Why it's wrong here
Cloud Functions have a 500 MB limit but 200 MB is okay; however, it's not optimized for serving.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Deploy to Vertex AI online prediction using a prebuilt container for scikit-learn. — Vertex AI online prediction with custom containers is suitable for scikit-learn models. Vertex AI will host the container and scale. Using AI Platform or Cloud Functions with a 200 MB model might hit limits.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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