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Google Cloud Professional

Course Content- Duration 40hrs


 Understanding the fundamentals of Google Cloud Platform

 The Google global infrastructure

 Products for storage, compute, networking, Machine Learning, and more

 Availability zones

 Different projects running on the GCP infrastructure, including Google projects


 Introduction to Google Cloud services

 Managing Google services using command-line tools, app, and console

 Installing and configuring SDK

 Deploying Cloud Shell for GCP environment management

 Deploying Gsutil

Project 1:

Create 2 VMs in differents , Deploy Nginx and establish connection between the 2 vms


 Different security and identity and access management (IAM) services

 Various roles in IAM

 Creating and managing Google resource permissions


 The creation of custom roles

 Sharing resources and isolation

 Deploying penetration testing, auditing, and security controls



Create a Custom Roles through console.

Use the Command Line (glcoud) to create a custom role by

combining one or more of the available Cloud IAM permissions.


 Introduction to Google networking

 Setting up the Google network

 Connecting various Google Cloud Platform resources

 Isolation using firewalls and network policies

 Creating and managing virtual private network

 Working with cloud routers and interconnecting networks


Project:VPC Networks

First you cannot create VM instances without a VPC network.

Next you create a new auto mode VPC network with subnets, routes, firewall rules,

and two VM instances and tested the connectivity for the VM instances. Because

auto mode networks aren't recommended for production, you convert the auto mode

network to a custom mode network.

Next, you create two more custom mode VPC networks with firewall rules and VM

instances using the Cloud Console and the gcloud command line.

Then you test the connectivity across VPC networks and VMs, which work when

pinging external IP addresses but not when pinging internal IP addresses they do not


VPC networks are by default isolated private networking domains. Therefore, no

internal IP address communication is allowed between networks.


Project: VPC Network Peering

Google Cloud Virtual Private Cloud (VPC) Network Peering allows private

connectivity across two VPC networks regardless of whether or not they

belong to the same project or the same organization.

VPC Network Peering allows you to build SaaS (Software-as-a-Service)

ecosystems in Google Cloud, making services available privately across

different VPC networks within and across organizations, allowing workloads to

communicate in private space.


Project: Load Balancing:

Google Cloud HTTP(S) load balancing is implemented at the edge of Google's  network in Google's points of presence (POP) around the world. User traffic  directed to an HTTP(S) load balancer enters the POP closest to the user and  is then load balanced over Google's global network to the closest backend  that has sufficient capacity available. 

In this lab, you configure an HTTP Load Balancer with global backends, as  shown in the diagram below. Then, you stress test the Load Balancer. 


 Understanding Google computing services

 Creating and managing virtual machines in Google Cloud

 Launching VMs on-demand using Google Compute Engine

 Choosing the right computing solutions based on the workload like memory or CPU

 Important actions with Compute Engine

 Balancing the load



Project : App Engine:

Nodejs on App Engine

1. Deploy a nodejs application on App Engine standard using gcloud (use default application

mentioned in the gcp tutorial)

2. Deploy the above version as ver-1

3. Change the content of html to “Hello world from intellipaat”

4. Deploy the above as ver-2

5. Browse both ver-1 and ver-2 using the URL

6. Finally deploy a test instance, and load test on your instance with 5000 requests (do this using



 Various storage services offered by Google Cloud

 The process of storing and retrieving data

 Rich content management

 Hosting a website

 Choosing the right storage options

 Integrating on-premises with the cloud storage environment

 Nearline and Coldline storage

 Cloud Datastore (No SQL Database)

 Managing cloud storage using Gsutil

7.Google Big Data

 Data flow

 Data Proc

8.Google Artificial Intelligence

 Cloud Vision API


Project: AI Project with various Components:

We will deploy a AI Application built on Python Flask web application to the App Engine Flexible environment. 

The application allows a user to upload a photo of a person's face and learn how likely it is that the person is happy. 

The application uses Python code, Google App Engine Flexible, Google Cloud APIs for Vision, Storage, and Datastore/Firestore.



 Understanding how to migrate to Google Cloud

 Various criteria to be considered

 Choosing the right options as per the specific needs

 Managing a hybrid cloud model

 Complete migration from the on-premises model to the cloud

 Choosing an automation framework for resource provisioning

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