PMLE Monitoring ML Solutions Practice Question

A company wants to automatically retrain their model when data drift is detected. Which THREE components are needed to implement this pipeline?

⚠ Common exam trap

PMLE often tests the confusion between components that are part of the ML workflow (like Feature Store or Cloud Storage) and those that are specifically needed for event-driven automation (Pub/Sub, Cloud Function, Monitoring alert). Candidates may incorrectly include storage or feature management components as necessary for the retraining trigger.

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

✓

Cloud Function to invoke Vertex AI Pipeline

Option A (Cloud Function to invoke Vertex AI Pipeline) is correct because the Cloud Function acts as the automation trigger that programmatically starts the Vertex AI Pipeline run to retrain the model once drift is signaled. Option C (Cloud Monitoring alert policy for drift metric) is correct because Vertex AI Model Monitoring publishes drift metrics to Cloud Monitoring, and an alerting policy on that metric is what detects the drift condition and fires the event. Option D (Pub/Sub topic) is correct because the alert policy notification channel uses Pub/Sub to deliver the drift alert, which then triggers the Cloud Function, forming the event-driven chain (Monitoring alert → Pub/Sub → Cloud Function → Vertex AI Pipeline). Option B (Vertex AI Feature Store) is not required here since it is for serving/online feature management, not for the drift-detection-and-retrain trigger mechanism. Option E (Cloud Storage bucket for storing training data) is not required as a distinct component of this pipeline, since training data storage is already assumed to exist and is not part of the drift-triggered automation chain.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Cloud Function to invoke Vertex AI Pipeline
Why this is correct
A Cloud Function provides the event-driven compute layer that reacts to a drift alert and triggers the Vertex AI Pipeline, satisfying the requirement for automatic retraining without manual intervention. It bridges drift detection to pipeline execution.
✗
Vertex AI Feature Store
Why it's wrong here
Vertex AI Feature Store serves and shares engineered features for online and offline inference; it neither monitors incoming data for drift nor initiates retraining. It is tempting because feature consistency matters in production ML, and Feature Store would be correct if the scenario required low-latency feature serving or training-serving skew prevention instead.
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Cloud Monitoring alert policy for drift metric
Why this is correct
A Cloud Monitoring alert policy detects when the drift metric breaches its threshold, satisfying the automatic trigger requirement. It publishes to a Pub/Sub topic, which invokes a Cloud Function that starts the retraining pipeline. Without this alerting mechanism, drift detection would remain passive and no retraining would initiate.
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Pub/Sub topic
Why this is correct
A Pub/Sub topic decouples drift detection from retraining: the monitoring alert publishes an event, and a subscriber triggers the pipeline. This satisfies the automatic retraining requirement without polling, enabling asynchronous, event-driven orchestration across Google Cloud services.
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Cloud Storage bucket for storing training data
Why it's wrong here
A Cloud Storage bucket merely holds objects; it cannot detect drift or trigger retraining, so it fails this pipeline's automation requirement. It is tempting because pipelines do need somewhere to persist training data, and a bucket would be the right choice when the question asks where to stage datasets rather than which components drive drift-triggered retraining.

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PMLE Monitoring ML Solutions Practice Question

Why each option matters

Option-by-option breakdown

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