How to Automate BigQuery ML Model Retraining Weekly with Minimal Effort?

PMLE By islever Editorial Last updated

Short answer

A Use BigQuery’s scheduling service to run the model retraining query periodically.

Question

You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?
  1. Use BigQuery’s scheduling service to run the model retraining query periodically. Correct Answer
  2. Create a pipeline in Vertex AI Pipelines that executes the retraining query, and use the Cloud Scheduler API to run the query weekly.
  3. Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.
  4. Use the BigQuery API Connector and Cloud Scheduler to trigger Workflows every week that retrains the model.

Key Takeaways

This question tests whether you recognize that BigQuery's native scheduling feature is the simplest way to run recurring queries, with the trap being the temptation to choose more complex orchestration services that require extra setup and incur higher costs.

The community agrees that BigQuery's built-in scheduling service is the correct approach for weekly retraining of a BigQuery ML model, as it minimizes development effort and scheduling cost. Alternatives involving Vertex AI Pipelines, Cloud Functions, or Workflows add unnecessary complexity and overhead.

Common mistake: The most common mistake is selecting an option that adds external components like Cloud Functions or Workflows, because these require more development effort, permissions, and execution costs even though BigQuery's native scheduler already fulfills the same purpose.

Expert Analysis

Why the Answer Is Correct

Option A is correct because BigQuery's scheduling service is a built-in feature designed exactly for running queries at specified intervals. It requires no additional infrastructure, custom code, or external dependencies, making it the lowest-effort and lowest-cost solution. As one comment notes, 'No additional setup: BigQuery's scheduling feature is built-in, eliminating the need to create pipelines, functions, or workflows.' Another comment strengthens this: 'Straightforward configuration: Setting up a schedule for a query is a simple process within the BigQuery interface.'

Why the Other Options Are Wrong

Option B (Vertex AI Pipelines) is overkill for a simple recurring query; it introduces pipeline execution costs and development overhead. Option C (Cloud Function) requires writing and deploying code, configuring triggers and IAM permissions, and paying per execution. Option D (Workflows) is also an orchestration layer that adds complexity without benefit. A community comment summarizes: 'Option B: Vertex AI Pipelines offer flexibility for complex workflows, but it involves more development effort and potential costs for pipeline execution. Option C: Cloud Functions provide a serverless way to execute code, but they incur execution costs and require additional configuration.'

Community Comment Notes

The community is unanimous, with all votes for Option A. One comment simply states, 'No-brainer A.' Another highlights that using BigQuery's scheduling service automates retraining 'without needing to write custom code or manage additional dependencies.' These comments reinforce that the ideal answer is the one that leverages the most native, integrated capability of BigQuery itself.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

When a question emphasizes minimal development effort and scheduling cost, prefer the native feature of the service involved—in this case, BigQuery's built-in scheduler. Rule out any option that introduces extra components like pipelines, functions, or workflows unless the question explicitly requires complex orchestration.

Community Votes

A
100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 4 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 4

  1. daidai75 👍 6 Answered A
    No additional setup: BigQuery's scheduling feature is built-in, eliminating the need to create pipelines, functions, or workflows. Straightforward configuration: Setting up a schedule for a query is a simple process within the BigQuery interface.
  2. pikachu007 👍 3 Answered A
    Option B: Vertex AI Pipelines offer flexibility for complex workflows, but it involves more development effort and potential costs for pipeline execution. Option C: Cloud Functions provide a serverless way to execute code, but they incur execution costs and require additional configuration for triggering and permissions. Option D: Workflows can manage complex orchestration, but configuring the BigQuery API Connector and Cloud Scheduler adds complexity and potential costs.
  3. AzureDP900 👍 2
    A is right Using BigQuery's scheduling service allows you to automate the retraining process without needing to write custom code or manage additional dependencies.
  4. b1a8fae 👍 3 Answered A
    No-brainer A.

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