~/gcp-cloud-devops-engineer · Build and implement CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads ▊

GCP · objective · 20% of the exam

Build and implement CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads — Professional Cloud DevOps Engineer

The official GCP documentation our Build and implement CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads practice questions are cited to. Review the primary sources, then practise.

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Official references for this objective

Google Cloud — Cloud Build serverless CI/CD platform | Google Cloud

Default pool lets you run builds in a secure, hosted environment with access to the public internet.
Google Cloud — Cloud Deploy - Fully Managed Continuous Delivery | Google Cloud

Cloud Deploy brings Skaffold to your pipelines, which, in unison with Cloud Code, brings pipeline parity across dev and CI/CD.
Google Cloud — Terraform blueprints and modules for Google Cloud  |  Terraform on Google Cloud  |  Google Cloud Documentation

cloud-deploy Create Cloud Deploy pipelines and targets
Google Cloud — Cloud Code | Google Cloud

Cloud Code for IDEs uses popular tools such as Skaffold, Jib, and kubectl to provide continuous feedback on your code in real time.
Google Cloud — Cloud Run | Google Cloud

On-demand access to NVIDIA L4 GPUs for running AI inference workloads. GPU instances start in 5 seconds and scale to zero.
Google Cloud — Software Delivery | Google Cloud

A reference architecture and supporting code to implement an opinionated software delivery model that can scale across multiple teams and multiple environments.