Associate Cloud Engineer Certification Path

Hello World 🖖.
Welcome to my first article on Dev.to, and what's a better way to start than to start with a bang 💥!

I will be starting a series journaling my learning journey towards acquiring my Google Associate Cloud Engineer Certificate.
Google Skills, in partnership with MentorMe Collective, has provided an Associate Cloud Engineer Certificate Path with credits for learning. In this article, I will take you on a tour of Google Cloud Courses and Hands-on Labs, and also introduce Gemini Notebook (formerly NotebookLM) for building a certification study guide.

Let's get into it.

What is Associate Cloud Engineer Certification?

A Cloud Engineer plans, configures, sets up, and deploys cloud solutions. The Associate Cloud Engineer Certification provides the practical skills required for this role, preparing you to successfully manage enterprise solutions on Google Cloud.
The Associate Cloud Engineer Certification path is a collection of on-demand courses, labs, and skill badges, where you will gain real-world, applied experience with Google Cloud technologies. This path focuses on the essential skills for the Cloud Engineering role, from infrastructure setup to application deployment.

What are Courses, Labs, Skill badges and Credits?

In order for the learning to be more engaging, retained, and shareable, Google was intuitive enough to develop a practical approach and set up reading materials, videos, as well as labs (hands-on laboratories) that allow you to test your new skills on Google Cloud Platform without the need for a Google Cloud billing account (although access is time-bound).

Each Course on the Certification is a collection of articles, videos, hands-on labs, and quizzes which allow you to smoothly progress through the topic of the Course and get a firm grasp of it.
The following are the Courses delivered in this Certification Path:

Essential Google Cloud Infrastructure: Foundation. This course allows participants to explore and deploy solution elements, including infrastructure components such as networks, virtual machines, and application services.
Essential Google Cloud Infrastructure: Core Services. This course enables participants to explore and deploy solution elements, including infrastructure components such as networks, systems, and application services.
Elastic Google Cloud Infrastructure: Scaling and Automation. This course allows participants to explore and deploy solution elements, including securely interconnecting networks, load balancing, autoscaling, infrastructure automation, and managed services.
Getting Started with Google Kubernetes Engine. This course introduces the basics of Google Kubernetes Engine, or GKE, as it’s commonly referred to, and how to get applications containerized and running in Google Cloud.
Developing Applications with Cloud Run on Google Cloud: Fundamentals. This course introduces the Cloud Run serverless platform for running applications.
Developing Applications with Cloud Run Functions on Google Cloud. In this course, you will learn about Cloud Run functions, Google's serverless, fully-managed functions-as-a-service (FaaS) product that lets you implement single-purpose function code that responds to HTTP requests and events from your cloud infrastructure.
Select a Google Cloud Database for Your Applications. This course explores relational and NoSQL databases, dives into Cloud SQL, AlloyDB, and Spanner, and teaches you how to align database strengths with your application requirements, including those of generative AI.
AI Infrastructure: Cloud GPUs. This course explores how CPUs, GPUs, and TPUs make AI tasks super fast, what makes each one unique, and how AI software gets the most out of them.
AI Infrastructure: Cloud TPUs. This course explores the advantages and disadvantages of TPUs in various scenarios and compares different TPU accelerators to help you choose the right fit.
AI Infrastructure: Deployment Types. This course provides a guide to deploying, managing, and optimizing AI and high-performance computing (HPC) workloads on Google, and explores diverse deployment strategies, ranging from highly customizable environments using Google Compute Engine (GCE) to managed solutions like Google Kubernetes Engine (GKE).
Logging and Monitoring in Google Cloud. This course covers operations-focused components, including Logging, Monitoring, and Service Monitoring.
Getting Started with Terraform for Google Cloud. This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure.
Implementing Cloud Load Balancing for Compute Engine. This course covers creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.
Deploy Kubernetes Applications on Google Cloud. This course covers configuring and building Docker container images, creating and managing Google Kubernetes Engine (GKE) clusters, utilizing kubectl for efficient cluster management, and deploying Kubernetes applications with robust continuous delivery (CD) practices.
Build Infrastructure with Terraform on Google Cloud. This course covers Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.

Towards the end of this learning path, there are skill badges you will need to acquire. Skill badges are digital credentials that validate your practical, applied knowledge of specific Google Cloud products and services. They are a core component of the certification learning paths, helping you gain real-world experience as you prepare for exams.
The Associate Cloud Engineer learning path has three courses that contain skill badges: Implementing Cloud Load Balancing for Compute Engine, Deploy Kubernetes Applications on Google Cloud, and Build Infrastructure with Terraform on Google Cloud.

Gemini Notebook, for building a certification study guide

Gemini Notebook (formerly known as NotebookLM) is an AI-powered research and note-taking assistant developed by Google. It allows you to upload your own sources, such as PDFs, Google Docs, web links, YouTube videos, and audio files, and acts as a personalized expert that answers questions and generates insights strictly grounded in your provided material.

Gemini Notebook only generates content based on your sources and will not hallucinate or provide false or unrelated information. I cannot overemphasize the importance of this platform while following through with this certification learning path, as it allows you to ask questions with answers sourced directly from your materials, test yourself with quizzes, and generate a study guide to help you succeed and feel confident when taking the certification exam.
Therefore, head over to https://notebook.google.com and start tinkering!

Conclusion: Why Associate Cloud Engineer Certification?

Wait, before you go, I'd like to leave you with one last piece of information. If you are starting your career as a Cloud Engineer, or if you want to prove your engineering capabilities in handling cloud projects, especially in the Google Cloud ecosystem, this certification is the right one for you. It not only equips you with technical skills, but it also validates them through real-world experience and enhances your professional profile, leading to career advancement opportunities in cloud engineering roles.

In the next article, we will learn about foundational concepts in Google Cloud Infrastructure. For now, Ciao 👋.