hardMultiple Select
PDE Practice Question: Which THREE steps are essential for implementing…
Which THREE steps are essential for implementing a continuous training pipeline with Vertex AI?
⚠ Common exam trap
Candidates often mistakenly include manual approval (B) as essential in a continuous training pipeline, or believe models can auto-update (C) without explicit pipeline steps. For Vertex AI, the required steps are triggering via events, evaluation, and automated deployment upon passing checks.
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
✓
If the new model passes evaluation, deploy it to a production endpoint.
Option A is correct because a continuous training pipeline must close the loop by promoting a model that passes evaluation to a production endpoint, typically via Vertex AI Model Registry and an Endpoint deployment, so the retrained model actually serves predictions. Option D is correct because continuous training requires an automated trigger, such as a Cloud Storage object-finalize event routed through Eventarc or Cloud Functions to launch the Vertex AI Pipeline when new training data lands. Option E is correct because the pipeline must include a model evaluation step that scores the new model against a held-out validation set (for example using Vertex AI Model Evaluation metrics) to gate promotion. Option B is not essential because manual approval breaks the continuous/automated nature of the pipeline, even if it can be added as an optional gate. Option C is incorrect because deploying the original model with auto-update is not a Vertex AI mechanism for retraining; continuous training is driven by pipelines and triggers, not by an endpoint auto-updating itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
If the new model passes evaluation, deploy it to a production endpoint.
Why this is correct
Passing evaluation gates the promotion, so only a model meeting the defined metric threshold reaches the production endpoint. This conditional deployment step completes the continuous training pipeline by automating release, satisfying the requirement that retrained models serve live traffic without manual intervention.
- ✗
Manually approve each new model version before deployment.
Why it's wrong here
Manual approval inserts a human gate, which contradicts the automation a continuous training pipeline demands; retraining and redeployment must trigger automatically. It tempts because approval gates are standard governance for high-risk releases, and that would be correct when the requirement is controlled, audited promotion rather than continuous automated delivery.
- ✗
Deploy the original model once and set it to auto-update.
Why it's wrong here
Continuous training requires a pipeline that retrains on new data and redeploys the resulting model; deploying once with auto-update does not retrain anything. It tempts because managed auto-update features exist for endpoints, and that would be correct when the requirement is keeping a deployed model's serving infrastructure patched rather than refreshing model weights.
- ✓
Set up a trigger to start a training pipeline when new training data is available (e.g., via Cloud Storage events).
Why this is correct
Cloud Storage object-finalise events can invoke Eventarc, which triggers the Vertex AI pipeline, satisfying the continuous retraining requirement. This event-driven mechanism removes manual invocation, so fresh data automatically initiates training without human intervention, which is precisely the automation the scenario demands.
- ✓
Include a step in the pipeline that evaluates the new model against a validation set.
Why this is correct
Evaluating the new model against a validation set provides the quality gate that decides whether retraining produced an improvement, satisfying the pipeline's requirement to automate promotion decisions. Without this comparison against a held-out benchmark, continuous retraining would deploy degraded models unchecked, defeating the purpose of an automated Vertex AI pipeline.
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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