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PDE A company uses Vertex AI to serve a model Practice Question
A company uses Vertex AI to serve a model. They notice that some predictions are incorrect due to data drift. What is the best way to detect and retrain the model automatically?
⚠ Common exam trap
Candidates often confuse general monitoring tools (Cloud Monitoring, Cloud Logging) with the specialized drift detection and automated retraining capabilities of Vertex AI Model Monitoring, assuming any monitoring solution can trigger retraining without native integration.
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 Model Monitoring with alerts and retraining pipeline
Vertex AI Model Monitoring is specifically designed to detect data drift and feature skew in production models. It can be configured to send alerts and trigger an automated retraining pipeline via Cloud Functions or Vertex AI Pipelines, enabling continuous model improvement without manual intervention. This directly addresses the need for automatic detection and retraining in response to data drift.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store predictions in BigQuery and run scheduled queries
Why it's wrong here
Scheduled BigQuery queries can compute drift statistics, but they only surface numbers; they cannot trigger Vertex AI training pipelines or redeploy a model, so retraining stays manual. BigQuery is the right home for prediction logging and drift analysis when you already orchestrate retraining separately.
- ✗
Create a Cloud Monitoring dashboard
Why it's wrong here
A Cloud Monitoring dashboard only visualises metrics a human must interpret; it neither computes feature-distribution drift nor launches a Vertex AI training job. Dashboards suit ongoing operational visibility once automated drift detection and retraining pipelines already exist.
- ✗
Set up Cloud Logging metrics to monitor predictions
Why it's wrong here
Cloud Logging metrics count or pattern-match log entries; they cannot compare training and serving feature distributions or invoke a Vertex AI pipeline to retrain. Logging metrics suit alerting on error rates or latency, not drift-triggered retraining.
- ✓
Use Vertex AI Model Monitoring with alerts and retraining pipeline
Why this is correct
Vertex AI Model Monitoring compares serving input distributions against training baselines and fires alerts on drift, which can trigger a retraining pipeline automatically. This satisfies the requirement to both detect drift and retrain without manual intervention.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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