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PDE Practice Question: Which THREE steps are required to set up a…
Which THREE steps are required to set up a continuous training pipeline on Google Cloud using Vertex AI?
⚠ Common exam trap
Google Cloud often tests the distinction between manual, ad-hoc automation (like cron jobs) and fully managed, integrated orchestration services (like Vertex AI Pipelines), leading candidates to incorrectly select simpler but non-scalable options.
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
✓
Create a Vertex AI Pipeline to orchestrate data preprocessing, training, and model evaluation.
Option B is correct because a Vertex AI Pipeline is the native orchestration mechanism that chains the preprocessing, training, and evaluation components into a repeatable, versioned workflow, which is the backbone of a continuous training pipeline. Option C is correct because continuous training requires an automated trigger — Cloud Scheduler for time-based runs or Cloud Build (often via Eventarc/pub-sub on new data) — to invoke the pipeline without manual intervention. Option E is correct because continuous training must include automated model evaluation and promotion logic, such as comparing accuracy against a threshold and deploying to a Vertex AI endpoint only when the gate passes, enabling CI/CD-style model rollout. Option A is not appropriate because a single Compute Engine VM with cron is a manual, non-managed approach that bypasses Vertex AI's managed pipelines, scaling, and metadata tracking. Option D is not appropriate because manually uploading models to the Vertex AI Model Registry after each run defeats automation; the pipeline should register models programmatically as a step.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run training on a single Compute Engine VM with a cron job.
Why it's wrong here
A cron job on one VM runs a script, not a Vertex AI pipeline: no managed training job, no pipeline scheduling, no artefact lineage. It is tempting because cron is the classic way to schedule recurring jobs, and it would suffice for a simple batch retraining script outside Vertex AI.
- ✓
Create a Vertex AI Pipeline to orchestrate data preprocessing, training, and model evaluation.
Why this is correct
A Vertex AI Pipeline orchestrates the preprocessing, training, and evaluation components as a repeatable directed acyclic graph, which is what makes the pipeline continuous rather than a one-off run. This directly satisfies the stem's requirement to automate the full training workflow on Google Cloud.
- ✓
Set up a trigger (e.g., Cloud Scheduler or Cloud Build) to start training on a schedule or new data.
Why this is correct
Continuous training requires an automated trigger; Cloud Scheduler or Cloud Build invokes the pipeline on a schedule or when new data lands, removing manual initiation. This satisfies the automation constraint that distinguishes continuous training from one-off pipeline runs.
- ✗
Manually upload the model to Vertex AI Model Registry after each training run.
Why it's wrong here
Manual upload after each run inserts a human step, so the pipeline is not continuous and no automated trigger promotes the model. It is tempting because the Model Registry is genuinely where trained models are catalogued, and manual registration is fine for one-off or experimental models.
- ✓
Configure model evaluation and promotion rules (e.g., if accuracy > threshold, deploy to endpoint).
Why this is correct
Promotion rules gate deployment on evaluation metrics, so a model only reaches the endpoint when accuracy exceeds the threshold. This closes the loop between training and serving, satisfying the requirement that continuous pipelines validate before promoting.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.