PMLE Automating and Orchestrating ML Pipelines Practice Question
A company wants to implement a CI/CD pipeline for their ML models using Vertex AI. They need to automatically retrain the model when new data arrives, but only if the model performance on a validation set has degraded by more than 5% compared to the current production model. Which three services or components should they incorporate into the automated pipeline? (Choose three.)
⚠ Common exam trap
In Google Cloud, the distinction between event-driven triggers (Cloud Functions/Eventarc) and scheduled triggers (Cloud Scheduler) is commonly tested. Candidates often mistakenly choose Cloud Scheduler when the requirement is for an event-driven retraining pipeline triggered by new data arrival.
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
✓
Vertex AI Evaluation component to compute model performance metrics on the validation set
Option B is correct because the Vertex AI Evaluation component is the mechanism that computes model performance metrics (such as accuracy, AUC, or RMSE) on a validation set, which is exactly what is needed to compare the newly trained model against the current production model and detect a degradation greater than 5%. Option C is correct because Cloud Functions can be configured with a Cloud Storage trigger (via Eventarc) to fire when new data objects arrive in a bucket, thereby automatically kicking off the retraining pipeline without manual intervention. Option D is correct because the Vertex AI Model Registry uses aliases (for example, a 'production' alias) to point to a specific model version, and updating that alias is how a newly validated model gets promoted to production once it passes the performance threshold. Option A is not required by the scenario, since the question focuses on triggering, evaluating, and promoting models rather than on data cleaning, and no data quality issue is stated. Option E is incorrect because Cloud Scheduler runs jobs on a fixed time-based schedule, whereas the requirement is event-driven retraining triggered by the arrival of new data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dataflow pipeline to clean the new data before training
Why it's wrong here
Dataflow cleans data, but the stem's trigger is validation-set degradation exceeding 5%, requiring a model-evaluation component comparing candidate against production. Dataflow is correct when preprocessing is the bottleneck, not when conditional retraining gates on measured performance regression.
- ✓
Vertex AI Evaluation component to compute model performance metrics on the validation set
Why this is correct
The Vertex AI Evaluation component computes metrics on the validation set, producing the performance figures needed to compare against the production model. This satisfies the stem's 5% degradation gate by supplying the quantitative basis for the retrain decision.
- ✓
Cloud Functions to trigger the pipeline when new data arrives in Cloud Storage
Why this is correct
Cloud Functions provides the event-driven trigger that satisfies the requirement to retrain automatically when new data arrives in Cloud Storage. Its native Cloud Storage object-finalise events initiate the pipeline without polling, ensuring retraining starts only on genuine data arrival, while the 5% degradation check is handled downstream by Vertex AI model evaluation.
- ✓
Vertex AI Model Registry alias update to promote the model if performance passes the threshold
Why this is correct
Updating a Vertex AI Model Registry alias points the production alias at the newly validated model, satisfying the stem's promotion step. This only happens once evaluation confirms performance passes the 5% degradation threshold, gating deployment on measured metrics.
- ✗
Cloud Scheduler to run the pipeline on a fixed schedule
Why it's wrong here
Cloud Scheduler fires jobs at fixed times, whereas the stem requires retraining triggered by new data arrival and gated on a 5% validation-performance drop. Scheduler suits periodic batch pipelines, not event-driven conditional retraining governed by model evaluation.
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
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.