AI0-001 AI Infrastructure and Technologies Practice Question
A data science team uses Vertex AI for model training and deployment. They want to implement CI/CD for ML pipelines. Which THREE Google Cloud services should they integrate?
⚠ Common exam trap
The trap is selecting data or infrastructure services like BigQuery or GKE instead of the specific CI/CD services; candidates must recognize that CI/CD for ML requires build, orchestrate, and deploy tools, not data warehouses or container platforms.
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 Pipelines
Vertex AI Pipelines (A) is correct because it orchestrates and automates the ML workflow steps (data prep, training, evaluation, deployment) as reproducible pipeline runs, which is the core of CI/CD for ML. Cloud Deploy (B) is correct because it provides managed continuous delivery to targets such as GKE, Cloud Run, and Anthos, enabling progressive rollout and approval gates for the deployment stage of the ML pipeline. Cloud Build (D) is correct because it executes the CI portion—building container images, running tests, and triggering pipeline jobs—and integrates natively with Vertex AI and Cloud Deploy via triggers and build steps. BigQuery (C) is not correct here because it is a data warehouse/analytics service, not a CI/CD component, even though it may store training data. Google Kubernetes Engine (E) is not correct because it is a runtime platform for containers, not a CI/CD service, and Cloud Deploy can target it without GKE itself being the CI/CD integration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Vertex AI Pipelines
Why this is correct
Vertex AI Pipelines orchestrates the ML workflow itself, running training, evaluation and deployment steps as a reproducible DAG. It supplies the pipeline automation the CI/CD requirement demands, letting each code commit trigger retraining and validation before Cloud Deploy handles release promotion.
- ✓
Cloud Deploy
Why this is correct
Cloud Deploy provides the managed continuous-delivery pipeline that promotes a trained model through staging and production targets, satisfying the CI/CD requirement. It orchestrates progressive rollout and approval gates, complementing Vertex AI Pipelines, which handles the training and pipeline orchestration stages rather than release promotion.
- ✗
BigQuery
Why it's wrong here
BigQuery is a analytics warehouse for querying and storing tabular data, not a CI/CD component for orchestrating, building or versioning ML pipelines. It tempts because BigQuery often supplies the training data and feature tables that Vertex AI pipelines read during execution.
- ✓
Cloud Build
Why this is correct
Cloud Build supplies the serverless execution engine that runs the pipeline's build, test and deployment steps, satisfying the CI/CD automation requirement. It natively integrates with Vertex AI and Artifact Registry, orchestrating container builds and `gcloud ai` commands across triggers, so training and deployment stages execute automatically on commit.
- ✗
Google Kubernetes Engine (GKE)
Why it's wrong here
GKE runs containerised workloads; it provides no native ML pipeline orchestration, artifact tracking, or model registry. Vertex Pipelines, Cloud Build, and Artifact Registry supply those. GKE is tempting because CI/CD runners often execute on Kubernetes, and it would be correct for deploying containerised inference services rather than orchestrating ML pipelines.
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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 CompTIA exam blueprint
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