Why IaC matters in GCP - See It in Action

Why IaC matters

📖 Scenario: You work as a cloud engineer for a small company. Your team wants to manage cloud resources in a way that is easy, fast, and reliable. You will learn how to use Infrastructure as Code (IaC) to create and manage cloud resources automatically.

🎯 Goal: Build a simple Google Cloud Platform (GCP) configuration using IaC to create a virtual machine instance. This will show why IaC matters by making resource setup repeatable and consistent.

📋 What You'll Learn

Create a dictionary called vm_config with keys name, machine_type, and zone with exact values.

Add a variable called project_id with the exact string value.

Write a function called create_instance_config that returns a dictionary combining project_id and vm_config.

Add a final dictionary called deployment_config that includes the instance configuration under the key resources.

💡 Why This Matters

🌍 Real World

IaC is used by cloud engineers to automate and manage cloud resources reliably without manual clicks.

💼 Career

Understanding IaC is essential for cloud jobs to improve speed, reduce errors, and enable collaboration.

1

Create the VM configuration dictionary

Create a dictionary called vm_config with these exact entries: 'name': 'test-vm', 'machine_type': 'e2-medium', and 'zone': 'us-central1-a'.

GCP

# Your code here

Need a hint?

Use curly braces to create a dictionary with the exact keys and values.

2

Add the project ID variable

Add a variable called project_id and set it to the string 'my-gcp-project'.

GCP

vm_config = {
    'name': 'test-vm',
    'machine_type': 'e2-medium',
    'zone': 'us-central1-a'
}
# Your code here

Need a hint?

Assign the exact string to the variable project_id.

3

Write a function to create instance config

Write a function called create_instance_config that returns a dictionary with keys project set to project_id and instance set to vm_config.

GCP

vm_config = {
    'name': 'test-vm',
    'machine_type': 'e2-medium',
    'zone': 'us-central1-a'
}

project_id = 'my-gcp-project'

# Your code here

Need a hint?

Define a function that returns a dictionary combining the project ID and VM config.

4

Create the final deployment configuration

Create a dictionary called deployment_config with a key resources set to a list containing the result of calling create_instance_config().

GCP

vm_config = {
    'name': 'test-vm',
    'machine_type': 'e2-medium',
    'zone': 'us-central1-a'
}

project_id = 'my-gcp-project'

def create_instance_config():
    return {
        'project': project_id,
        'instance': vm_config
    }

# Your code here

Need a hint?

Create a dictionary with a list of resources using the function result.