The correct approach is to export the BigQuery ML model directly to Vertex AI and create an endpoint for online prediction. This is because Vertex AI provides a fully managed, low-latency serving infrastructure specifically designed for real-time inference, which is the core requirement for online prediction. BigQuery ML models, such as those built with `CREATE MODEL`, can be exported as a SavedModel and registered in the Vertex AI Model Registry, then deployed to an endpoint that handles autoscaling and request routing. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of the modern, unified MLOps workflow, often contrasting it with the outdated method of exporting to Cloud Storage and deploying to standalone AI Platform—a common trap. The key insight is that AI Platform is now Vertex AI, so the direct export path is both simpler and the recommended best practice. Memory tip: think "BigQuery trains, Vertex serves"—the model never touches Cloud Storage for deployment.
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. 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 wants to deploy a BigQuery ML model for online prediction. Which approach should they take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Export the model to Vertex AI and create an endpoint
BigQuery ML models can be exported directly to Vertex AI for online prediction. Vertex AI provides a managed endpoint that supports real-time serving with low latency, which is required for online prediction. Exporting to Cloud Storage and then deploying to AI Platform is outdated because AI Platform is now part of Vertex AI, and the recommended path is to export the model directly to Vertex AI and create an endpoint.
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.
✗
Export the model to Cloud Storage and deploy to AI Platform
Why it's wrong here
AI Platform is deprecated; Vertex AI is the current service.
✓
Export the model to Vertex AI and create an endpoint
Why this is correct
Vertex AI supports deploying BigQuery ML models for online serving.
Related concept
Read the scenario before looking for a memorised answer.
✗
None of these; BigQuery ML models cannot be used for online prediction
Why it's wrong here
They can be used via Vertex AI.
✗
Use BigQuery ML's ML.PREDICT for online predictions
Why it's wrong here
ML.PREDICT is for batch prediction, not low-latency online.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between batch prediction (ML.PREDICT) and online prediction (Vertex AI endpoint), and the trap here is that candidates assume BigQuery ML's ML.PREDICT can serve real-time requests, but it is designed for batch processing only.
Detailed technical explanation
How to think about this question
When you export a BigQuery ML model to Vertex AI, the model is stored in Cloud Storage as a SavedModel or a TensorFlow model, and Vertex AI automatically deploys it behind a scalable endpoint that handles autoscaling and load balancing. Under the hood, Vertex AI uses a containerized serving infrastructure that can handle thousands of requests per second with sub-second latency. A real-world scenario is a fraud detection system where a BigQuery ML model trained on historical transaction data is exported to Vertex AI to score each transaction in real time as it occurs.
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.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Export the model to Vertex AI and create an endpoint — BigQuery ML models can be exported directly to Vertex AI for online prediction. Vertex AI provides a managed endpoint that supports real-time serving with low latency, which is required for online prediction. Exporting to Cloud Storage and then deploying to AI Platform is outdated because AI Platform is now part of Vertex AI, and the recommended path is to export the model directly to Vertex AI and create an endpoint.
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.
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