This blog post gives a walkthrough of the Step-By-Step Activity Guides of the Google Professional Cloud Architect Certification Training program that you must perform to learn this course.
The walkthrough of the 25 Step-By-Step Activity Guides of the Professional Google Cloud Architect Training program will prepare you thoroughly for this certification and clear your concepts.
To get started with your Google Cloud Architect journey, let’s look at these hands-on guides in detail.
LAB 1: Cloud Console & Cloud Shell Overview and Navigation
Cloud Console enables users to perform basic storage management tasks with data using a browser.
Cloud Shell is an in-browser command prompt execution environment (CLI) that allows users to enter commands at a terminal prompt in order to manage resources and services in a Cloud project in the GCP console.
This Google Cloud hands-on lab is an introductory guide focussing on Google Cloud Console navigation so you all can get familiar with the environment that you will be working on.
![Google Cloud Architect Console Overview]()
LAB 2: Set Up Projects, Billing Budgets & Alerts in GCP
LAB 2: Set Up Projects, Billing Budgets & Alerts in GCPProjects organize all the Google Cloud resources. It consists of users, APIs, billing, authentication, and monitoring settings for the available set of APIs.
A Cloud Billing account specifies who pays for an assigned set of Google Cloud services.
In this lab, you will start with creating a project in the Google Cloud Platform console where the services and resources will be deployed. After creating a project you will create a billing budget and allow alerts which will help you to keep track of your credits and alert you in case the set budget price is exceeded.
LAB 3: Create & Connect Linux VM Instances in GCP
Compute Engine is a computing and hosting service that allows users to create and run virtual machines on Google infrastructure. An instance is a virtual machine hosted on Google’s infrastructure which can be created using Google Cloud Console, the gcloud command-line tool, or the Compute Engine API.
This lab focuses on creating and connecting Linux VM instances in Compute Engine using the Google Cloud Console.
LAB 4: Create and Manage VM Using CLI & Cloud Shell
This lab will guide you through the steps to create and manage a VM instance using the Cloud shell using the gcloud commands.
LAB 5: Create Auto mode & Custom VPC using Cloud Console
Google Cloud proposes two types of VPC networks, defined by their subnet creation mode:
- Auto-mode VPC
- Custom mode VPC
Auto-mode VPC: Here one subnet is automatically created in each region, all subnets use the same predefined range of IP addresses and also all default firewall rules are applicable.
Custom-mode VPC: Here no subnets are automatically created. It provides you with complete control over its subnets and IP ranges. You can switch from auto to custom but not vice versa
In this lab, you will create both auto-mode and custom VPC networks using cloud console, create VMs and test connectivity for these networks.
LAB 6: Create a Custom Mode VPC Network on GCP Using CLI
In this lab, you will create a custom mode VPC network using the command-line interface i.e., Cloud shell. You will use the gcloud commands to create the network, its subnet, firewall rules, and a VM instance.
LAB 7: Add IAM user & Assigning various roles
Identity and Access Management (IAM) lets users create and manage permissions for Google Cloud resources. It unifies access control for Google Cloud services into a single system and presents a uniform set of operations.
In this lab, we will focus on adding a user to a project and assign various roles to the added user.
LAB 8: Create Service Accounts and Assign Roles to Services
Service accounts are a special type of Google account that grants permissions to virtual machines instead of end-users. These are primarily used to ensure safe, managed connections to APIs and Google Cloud services.
In this guide, you will get hands-on practice with the ins and outs of service accounts.
LAB 9: Create Cloud Storage bucket, Upload objects & Grant Public Access (Console & CLI)
Buckets are the basic containers that hold the data so, everything that is stored in Cloud Storage must be contained in a bucket. Objects are individual pieces of data that can be stored in Cloud Storage. Google Cloud Architects will have to choose the correct storage or database service for their projects as per the client’s requirements.
This lab guides you to create a storage bucket in cloud storage, upload objects to the bucket, and steps to grant public access to the object to view it via a public URL.
LAB 10: Create & Delete Lifecycle Policy for a cloud storage bucket
Lifecycle management configuration can be implemented to a bucket. The configuration contains a set of rules which apply to current and future objects in the bucket. So, when an object meets the criteria of one of the rules, Cloud Storage automatically performs a specified action on the object, for example, delete objects created before May 29, 2021.
LAB 11: Introduction To Google Cloud SQL
Cloud SQL is a fully managed database service that helps you set up, maintain, manage, and administer your relational databases on the Google Cloud Platform.
This guide describes the steps to Create a Google Cloud SQL instance, connect to your instance using the MySQL client in the Cloud Shell then create a database and upload data, and finally terminating the resources.
LAB 12: Perform Simple Operations in Cloud Spanner
Cloud Spanner is a globally distributed database service and storage solution. It offers
global transactions, strongly consistent reads, and automatic multi-site
replication and failover.
In this guide, you will perform the steps to create a Cloud spanner instance and database, create a schema for the database, insert/modify data, and run queries against the inserted data.
LAB 13: Create And Manage Cloud Bigtable Instances
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of
columns, letting users store terabytes or even petabytes of data.
In this guide, you will learn how to create and connect a Bigtable instance, Read & Write data, Edit instance and finally deleting the instance to save the account credits.
LAB 14:
App Engine is a fully managed, serverless platform for developing and hosting
web applications at scale. Users can select from various popular languages, libraries, and
frameworks to develop their apps, then let App Engine take care of provisioning servers
and scaling their app instances based on demand.
This lab will guide you through the steps of creating and deploying a web application in App Engine.
LAB 15:
Cloud Functions is a serverless execution environment to build and execute cloud
services. It is triggered when an event being watched is fired. The code executes in a fully managed environment.
This lab covers the steps to create a deploy a cloud function using both Console and Cloud Shell (CLI).
LAB 16:
Cloud Run is a managed compute platform enabling users to run stateless containers
that are invoked via web requests or Pub/Sub events.
In this lab, you will deploy a sample pre-built container in Cloud Run.
LAB 17:
BigQuery is Google Cloud’s fully managed, petabyte-scale, and cost-effective analytics
data warehouse that allows running analytics over vast amounts of data in near real-time.
In this lab, you will start by enabling the BigQuery data transfer API, create a dataset for storing the billing tables. After creating the dataset, we will enable the billing records and export those in our dataset which we want to examine and query. Later we will review the schema of the billing tables and query data as per some specific requirements.
LAB 18: Monitoring Compute Engine Resource with Resource Monitoring & Logging
Cloud Monitoring collects measurements of your service and of the Google Cloud
resources that you use. Cloud Logging allows you to store, search, analyze, monitor, and alert on logging
data and events from Google Cloud and Amazon Web Services. A Google Cloud Architect will have to monitor the resources and logs and suggest best practices to obtain the highest efficiency.
This lab will help you in understanding the concepts of Cloud Monitoring & logging. Here you will create a VM instance, install Apache server, monitor & logging agents (stackdriver agents) and then create uptime checks in the dashboard and explore logs generated for the VM instance.
LAB 19:
Cloud VPN securely extends your peer network to Google’s network through an IPsec VPN tunnel. Traffic is encrypted and travels between the two networks over the public internet. Cloud VPN is useful for low-volume data connections.
This lab will help you in understanding the concept of Cloud VPN and its basics.
LAB 20: Set Up Simple External
A load balancer distributes user traffic across multiple instances of your applications. By
distributing the load, load balancing reduces the risk that your applications experience
performance issues.
This lab includes the steps to create a managed instance group, configuring firewall rules, setting up the load balancer, and finally test the traffic sent to the VM instance associated with the managed instance group.
Source: Google Documents
LAB 21: Autoscaling in GCP
Autoscaling lets your apps gracefully handle increases in traffic, and it reduces costs when the need for resources is lower. After you define the autoscaling policy, the autoscaler performs automatic scaling based on the measured load.
LAB 22:
Google Cloud Deployment Manager is an infrastructure deployment service that automates
the creation and management of Google Cloud resources.
In this lab, you will Create Google Compute Engine VM using Deployment Manager.
LAB 23:
A cluster consists of at least one cluster control plane machine and multiple worker machines called nodes. You deploy applications to clusters, and the applications run on the nodes. GKE is an important topic from the Professional Google Cloud Architect certification point of view.
In this guide, you will learn how to create Zonal, Regional & Autopilot clusters in Cloud Console.
LAB 24:
Google Kubernetes Engine (GKE) provides a managed environment for deploying, managing, and scaling your containerized applications using Google infrastructure. The GKE environment consists of multiple machines (specifically, Compute Engine instances) grouped together to form a cluster.
This guide covers the steps to deploy a simple web server containerized application to a GKE cluster.
Source: Google Documents
LAB 25:
In this lab, you will learn how to Provision a complete Kubernetes cluster using Kubernetes Engine, deploy and manage Docker containers using kubectl, and break an application into microservices using Kubernetes’ Deployments and Services.
To practice all these guides you can create a Free-Trial Google Cloud Account where you will get $300 credit for 90 days.
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