PMLE Automating and Orchestrating ML Pipelines Practice Question
An ML engineer needs to trigger a Vertex AI Pipeline on a recurring schedule, every 24 hours, to retrain a model with the latest data. Which approach should they use to set up this schedule?
⚠ Common exam trap
The PMLE exam often tests the misconception that Vertex AI Pipelines has a built-in scheduling feature (like a cron parameter in the pipeline definition or a UI schedule button), when in fact scheduling must be implemented using Cloud Scheduler or similar external services.
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
✓
Create a Cloud Scheduler job that calls the Vertex AI API to create a pipeline job.
Cloud Scheduler is the native Google Cloud service for cron-based job scheduling. By configuring a Cloud Scheduler job to call the Vertex AI API (e.g., via a HTTP POST to the projects.locations.pipelineJobs.create endpoint), the engineer can trigger a pipeline run every 24 hours. This approach is reliable, supports authentication via OAuth, and integrates directly with Vertex AI Pipelines without requiring additional orchestration code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Cloud Tasks to queue pipeline runs daily.
Why it's wrong here
Cloud Tasks dispatches HTTP callbacks to a target at a scheduled time, but it does not authenticate to or invoke the Vertex AI Pipelines scheduling API, so no PipelineJob is created. It is tempting as a generic deferred-execution queue, and would be correct for rate-limited task dispatch to your own service.
- ✓
Create a Cloud Scheduler job that calls the Vertex AI API to create a pipeline job.
Why this is correct
Cloud Scheduler provides cron-based recurring triggers, satisfying the 24-hour retraining requirement. Its HTTP target can authenticate to the Vertex AI API and invoke pipelines.create, launching a pipeline run on each fire. This avoids maintaining external orchestration infrastructure, unlike Cloud Functions or manual invocation, and natively supports the fixed daily cadence the stem demands.
- ✗
Set a cron expression in the pipeline definition file using the 'schedule' parameter.
Why it's wrong here
The pipeline definition file (a compiled IR spec or Python DSL) has no 'schedule' parameter; scheduling is configured separately when submitting the PipelineJob via the Vertex AI SDK or REST API. It is tempting because pipeline code holds other run configuration, but recurrence is a submission-time concern, not part of the IR.
- ✗
Use the Vertex AI Pipelines UI to set a schedule directly on the pipeline.
Why it's wrong here
The Vertex AI Pipelines UI does not expose a native recurring-schedule control; schedules are created through the Vertex AI SDK or the REST API against the PipelineJob schedule endpoint. It is tempting because the console does show pipeline runs and templates, but scheduling belongs to Cloud Scheduler-backed API calls, not the UI.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.