PMLE Automating and Orchestrating ML Pipelines Practice Question
A company wants to implement continuous delivery (CD) for ML models, where a model is automatically deployed to a staging environment and only promoted to production after passing an evaluation gate. Which combination of GCP services is BEST suited for orchestrating this CD pipeline?
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 Build with Vertex AI Pipelines and Cloud Deploy
Cloud Build can trigger on code/model changes and run a pipeline that deploys to staging. After evaluation, if successful, it can promote to production using Cloud Deploy or directly update Vertex AI endpoints. Cloud Composer (Airflow) is also a good option for complex orchestration, but for CI/CD, Cloud Build is a natural fit. The combination of Cloud Build, Cloud Deploy, and Vertex AI provides a robust CD pipeline.
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 Scheduler and Pub/Sub
Why it's wrong here
Cloud Scheduler triggers cron jobs and Pub/Sub moves messages; neither stores model artefacts, runs evaluation gates, nor tracks deployment revisions. This pairing fits periodic batch invocation, such as nightly retraining triggers, not gated promotion between staging and production.
- ✗
Cloud Composer (Airflow) with Cloud Functions
Why it's wrong here
Cloud Composer orchestrates batch DAGs on a schedule, not event-driven model promotion gates; Cloud Functions cannot track artefact lineage or deployment state. It suits scheduled ETL and retraining pipelines, where Airflow's dependency management across recurring jobs is genuinely valuable.
- ✓
Cloud Build with Vertex AI Pipelines and Cloud Deploy
Why this is correct
Cloud Build handles CI image and pipeline builds, Vertex AI Pipelines runs the training and evaluation gate, and Cloud Deploy manages progressive promotion to staging then production. Together they satisfy the stem's requirement that promotion occur only after the evaluation gate passes.
- ✗
Vertex AI Pipelines with Cloud Run
Why it's wrong here
Cloud Run hosts containerised services; it provides no pipeline orchestration, artifact lineage or evaluation-gate constructs, so it cannot sequence training, evaluation and promotion stages. It is tempting because Cloud Run deploys models as scalable endpoints, and would be correct for serving a model behind HTTP rather than orchestrating the CD workflow.
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1 more way this is tested on PMLE
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Variation 1. A company has a CI/CD pipeline that retrains a model every time new training data is available. They want to automatically deploy the new model to production only if it passes a set of evaluation tests on a staging environment. Which approach best implements this?
hard- ✓ A.Implement a two-stage pipeline: train and deploy to staging, run evaluation tests, and if passed, deploy to production using conditional logic.
- B.Use Cloud Build to trigger a training job and then a separate deployment job without evaluation.
- C.Use a single Vertex AI pipeline that trains and deploys to staging, then manually promote.
- D.Train and deploy directly to production in one pipeline.
Why A: The correct approach is a two-stage pipeline: train and deploy to a staging environment, run automated evaluation tests against the staged model, and use conditional logic to promote to production only if the tests pass. This implements a proper ML CI/CD gate that prevents regressions from reaching production.
JA
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.