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PDE Practice Question: A team has trained a model using AutoML Tables
A team has trained a model using AutoML Tables. They want to deploy it for batch predictions on a schedule. What is the simplest approach?
⚠ Common exam trap
Google Cloud often tests the misconception that you must export an AutoML model to use it outside Vertex AI, but the simplest path is to use Vertex AI's native batch prediction service, which avoids the overhead of custom infrastructure like Dataflow or Cloud Functions.
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
✓
Use Vertex AI Batch Prediction with a scheduled pipeline
Vertex AI Batch Prediction is the simplest approach because it is a managed service that directly supports batch predictions on AutoML Tables models without requiring additional infrastructure. By wrapping it in a scheduled Vertex AI pipeline, you can automate the entire workflow—triggering predictions on a schedule, handling input/output to Cloud Storage, and managing compute resources—all within the Vertex AI ecosystem, minimizing operational overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write a Cloud Function triggered by Cloud Scheduler
Why it's wrong here
AutoML Tables batch prediction is a native scheduled operation within Vertex AI, so a Cloud Function adds deployment and orchestration overhead without providing the scheduling itself. Cloud Functions suit event-driven glue code reacting to triggers, not recurring batch inference jobs.
- ✗
Export model to Cloud Storage and use Dataflow
Why it's wrong here
Exporting the model to Cloud Storage and using Dataflow introduces unnecessary orchestration overhead for scheduled batch predictions, as AutoML Tables natively supports scheduled batch prediction jobs directly through its service, eliminating the need for a separate pipeline. This option is tempting because Dataflow is a powerful tool for custom data processing pipelines, and it would be the correct choice if the team needed complex data transformations or streaming predictions that AutoML Tables’ native batch prediction cannot handle.
- ✗
Deploy to App Engine
Why it's wrong here
App Engine serves web applications and HTTP endpoints; it does not natively run scheduled batch prediction jobs against AutoML Tables. It tempts because it is a managed serverless platform requiring minimal infrastructure, but batch scoring on a schedule fits a scheduled pipeline invoking the model's batch prediction capability.
- ✓
Use Vertex AI Batch Prediction with a scheduled pipeline
Why this is correct
Vertex AI Batch Prediction accepts an AutoML Tables model and a BigQuery or Cloud Storage input, and wrapping it in a scheduled pipeline automates recurring runs. This satisfies the scheduled batch constraint with minimal custom code.
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