PMLE Monitoring ML Solutions Practice Question
An ML team has set up automated retraining triggered by Cloud Monitoring alerts. When a feature drift alert fires, a Cloud Function publishes to Pub/Sub, which triggers a Vertex AI Pipeline. However, the retraining pipeline is failing because the training data is not updated. What is the most likely cause?
⚠ Common exam trap
The trap here is that candidates fixate on the orchestration layer (permissions, Pub/Sub, endpoints) because those are the visible components, when the question explicitly says the pipeline is failing due to training data not being updated — a data-pipeline problem, not an infrastructure problem.
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
✓
The training data in the pipeline input is stale or not refreshed
The pipeline is triggering correctly (the alert fires, the Cloud Function runs, Pub/Sub delivers, and the Vertex AI Pipeline starts), so the failure is downstream of orchestration — it is the data itself. When a drift alert fires, the pipeline's input dataset or feature table must be refreshed from the source system before training; if the pipeline references a static/stale snapshot or a BigQuery view that is not re-materialized, training runs on old data and fails validation or produces a useless model. The most likely cause is therefore that the training data in the pipeline input is stale or not refreshed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Cloud Function does not have permission to start the pipeline
Why it's wrong here
Permission failures would block pipeline invocation entirely, producing authorisation errors rather than a pipeline that runs but trains on stale data. Granting the Cloud Function pipeline-start permission is correct when invocation itself is denied, not when the pipeline executes and the input data remains unchanged.
- ✗
The Pub/Sub topic is incorrectly configured
Why it's wrong here
A misconfigured topic would prevent alert messages reaching the pipeline, so retraining would never trigger at all. Correct topic configuration matters when notifications are lost or undelivered; here the pipeline runs and fails on stale input, pointing to the data-refresh step instead.
- ✓
The training data in the pipeline input is stale or not refreshed
Why this is correct
The pipeline executes correctly but trains on an unchanged dataset, so drift persists. The alert and Pub/Sub trigger fire as designed; the failure lies in the input source, which is not being refreshed from the updated feature store or data location before the pipeline runs.
- ✗
The model endpoint is overloaded
Why it's wrong here
Endpoint overload affects inference serving, not pipeline data ingestion, so it cannot explain stale training data. The tempting confusion is that overloaded endpoints do cause retraining failures in some architectures, but only where the pipeline reads live inference traffic; here the fault lies in the feature-store or data-refresh step feeding the pipeline.
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Same concept, more angles
2 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company wants to implement a retraining trigger for their ML model. They have set up Cloud Monitoring alerts that fire when drift exceeds a threshold. What should be the target of the alert to automatically start a Vertex AI Pipeline for retraining?
medium- A.Vertex AI Model Registry
- B.Cloud Storage bucket
- C.Cloud Functions HTTP trigger
- ✓ D.Pub/Sub topic
Why D: Cloud Monitoring alerting policies can route notifications to a Pub/Sub topic, which then acts as the event source that triggers a Vertex AI Pipeline run. Pub/Sub provides the decoupled, asynchronous messaging backbone that lets the alert fire a pipeline execution without manual intervention, typically via an Eventarc or Cloud Function subscriber that calls the Vertex AI Pipelines API.
Variation 2. An ML engineer needs to set up automated retraining triggered by data drift. They have decided to use Cloud Monitoring alerts to detect drift. Which TWO additional services are required to complete the retraining pipeline? (Choose 2)
medium- A.Cloud Dataflow
- B.Cloud Build
- C.Cloud Scheduler
- ✓ D.Vertex AI Pipeline
- ✓ E.Cloud Functions
Why D: Option D (Vertex AI Pipeline) is correct because it is the managed service that orchestrates and executes the actual retraining workflow — defining the training steps, model evaluation, and deployment as a reproducible pipeline that can be triggered programmatically. Option E (Cloud Functions) is correct because a Cloud Monitoring alert must publish to a notification channel, and a Cloud Function (typically invoked via Pub/Sub) acts as the glue that receives the alert and calls the Vertex AI Pipeline API to start the retraining run. Together they complete the loop: Monitoring detects drift, the alert fires, Cloud Functions receives it, and Vertex AI Pipeline performs the retraining. Option A (Cloud Dataflow) is not required since batch data processing is not the missing piece here, and drift detection is already handled by Cloud Monitoring. Option B (Cloud Build) is for CI/CD build and container image creation, not for orchestrating ML retraining. Option C (Cloud Scheduler) is unnecessary because the trigger is event-driven from a Monitoring alert, not a time-based cron schedule.
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.