How to deploy an existing BigQuery ML AutoML model for online prediction?
You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?
Community Votes
67% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The exam tests whether you know that BigQuery ML models can be registered into Vertex AI Model Registry via ALTER MODEL without exporting to Cloud Storage, and that retraining is unnecessary for an already-approved model.
The quickest way to deploy a trained BigQuery ML AutoML regression model for online prediction is to use the ALTER MODEL statement to register it directly with Vertex AI Model Registry, then deploy it to a Vertex AI endpoint—no retraining or Cloud Storage export required. Community consensus favors option C with 65 votes over D.
Option D is the most common wrong answer: exporting the model to Cloud Storage and importing it into Vertex AI. This is unnecessary because BigQuery ML natively supports registering models directly with Vertex AI Model Registry using ALTER MODEL, making the process faster and simpler.
Community Discussion (19 comments)
Comments & Corrections
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Expert Analysis
Why the Answer Is Correct
Option C is correct because the model has already been trained and received positive feedback, so retraining (options A and B) is wasteful and slows deployment. The ALTER MODEL statement in BigQuery ML lets you update the model's metadata to specify Vertex AI as the model registry, effectively registering the existing model directly without needing to export it. As comment [6] notes, "retraining the model (A/B) is not necessary" and "exporting to Cloud Storage (D) is not necessary, since you can use the ALTER MODEL statement to register it on Vertex AI Model Registry." Community comments [2], [3], [5], and [10] all cite official documentation confirming this workflow.
Why the Other Options Are Wrong
Option A is wrong because it requires retraining the model, which is unnecessary and contradicts the goal of deploying as quickly as possible. Option B is wrong for the same reason and also uses Vertex AI training instead of BigQuery ML, adding complexity. Option D is wrong because, while it avoids retraining, it introduces an extra manual step of exporting to Cloud Storage and importing into Vertex AI; the direct ALTER MODEL registration in option C is faster and supported. Comment [3] explicitly states "no need to export the model," and comment [8] reinforces "No need to export the model to Cloud Storage."
Community Comment Notes
Several comments provide useful references: [2] links to the official ALTER MODEL syntax, [3] and [4] link to the BigQuery managing models in Vertex AI guide and the update_vertex page, and [10] links to registering a new BQML model version. Commenters who chose D (like [1] and [9]) incorrectly assumed that an export/import is required for Vertex AI online prediction, but the docs and the majority of comments clearly show the direct registration path. Comment [6] provides a concise summary that aligns with the correct answer and should be used as a quick review note.
Official Reference
Exam Strategy
When a question asks for the 'quickest' or 'as quickly as possible' deployment, always look for the option that avoids unnecessary retraining and extra export/import steps. Remember that BigQuery ML models can be registered directly to Vertex AI Model Registry via ALTER MODEL, so only choose export/import when the question explicitly requires a format change or offline model store.
Related Analysis
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