hardMultiple Choice
PMLE Practice Question: A team uses Vertex AI Pipelines to automate…
A team uses Vertex AI Pipelines to automate training and deployment. They need to ensure that only models that pass a set of quality checks (e.g., accuracy > 0.9, latency < 100ms) are deployed to production. How should they implement this?
⚠ Common exam trap
PMLE often tests whether candidates choose manual or external mechanisms instead of native pipeline control flow, tricking them into selecting Cloud Functions or manual review when the correct answer is an in-pipeline conditional gate.
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
✓
Add a Pipeline component that evaluates metrics and uses a conditional gate to deployment
Vertex AI Pipelines supports conditional logic through components and pipeline control flow, allowing a component to evaluate model metrics and a downstream conditional gate to promote the model only if thresholds (accuracy > 0.9, latency < 100ms) are met. This embeds quality gates directly into the MLOps pipeline, ensuring automated, repeatable promotion decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually review each model before promotion
Why it's wrong here
Manual review introduces human latency and cannot enforce numeric thresholds deterministically at pipeline scale. Human sign-off suits low-volume, high-risk releases, but automated promotion requires a pipeline component that evaluates metrics and gates deployment programmatically, such as a condition or custom evaluation step.
- ✗
Use Cloud Functions to deploy only if accuracy is reported in BigQuery
Why it's wrong here
Reporting accuracy to BigQuery records metrics but performs no gating, so failing models still deploy. Cloud Functions suit lightweight event-driven glue, yet the requirement is conditional promotion inside the pipeline; a metrics-based condition component blocks deployment directly when thresholds are unmet.
- ✗
Set up Cloud Build triggers to deploy every model version
Why it's wrong here
Deploying every model version ignores the quality thresholds entirely, promoting failing models to production. Cloud Build triggers suit CI/CD build automation, but quality-gated promotion demands the pipeline itself evaluate accuracy and latency and halt deployment when checks fail.
- ✓
Add a Pipeline component that evaluates metrics and uses a conditional gate to deployment
Why this is correct
A pipeline component computes the quality metrics, and a conditional gate evaluates them against thresholds such as accuracy above 0.9 and latency under 100ms, only allowing deployment when all checks pass. This enforces automated quality control within Vertex AI Pipelines itself.
Go deeper
Related to this question
About these practice questions
Courseiva writes every PMLE question from scratch — 775 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.