Hands-On Lab: Identifying and Provisioning AWS Compute Services

Welcome to this guided lab on AWS Compute Services. In the AWS Cloud, "compute" refers to the processing power required to run applications, handle data, and execute scripts. AWS offers a wide spectrum of compute options, from traditional Virtual Machines (Amazon EC2) to serverless computing (AWS Lambda) and container orchestration (Amazon ECS/EKS).

In this lab, you will deploy resources across two different compute paradigms—a traditional Amazon EC2 instance and a serverless AWS Lambda function—allowing you to practically identify their operational differences.

Prerequisites

Before you begin, ensure you have the following:

AWS Account: An active AWS account with Administrator or sufficient PowerUser permissions.
AWS CLI: The AWS Command Line Interface (aws) installed and configured with your credentials (aws configure).
Region: Select a default region (e.g., us-east-1 or us-west-2) and use it consistently.
Tools: A standard terminal/command prompt and a basic text editor.

Architecture Overview

The following diagram illustrates the two distinct compute services we will provision side-by-side during this lab.

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Compute Abstraction Spectrum

To understand why we have multiple compute services, review this abstraction chart. As you move to the right, AWS handles more of the underlying infrastructure.

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Learning Objectives

By the end of this lab, you will be able to:

Provision and identify an Amazon EC2 virtual machine.
Provision and identify a serverless AWS Lambda function.
Differentiate between server-based (IaaS) and serverless compute deployments.
Safely tear down compute resources to avoid ongoing charges.

Step-by-Step Instructions

Step 1: Provision a Virtual Machine (Amazon EC2)

Amazon Elastic Compute Cloud (EC2) provides resizable compute capacity. You maintain full OS-level control. We will launch a t2.micro instance (free-tier eligible).

First, we need to retrieve the latest Amazon Linux 2 AMI ID for your region. Run this command and copy the resulting ami-xxxxxxxxx value:

bash

aws ssm get-parameters --names /aws/service/ami-amazon-linux-latest/amzn2-ami-hvm-x86_64-gp2 --query "Parameters[0].Value" --output text

Now, launch the instance using the retrieved AMI ID:

bash

aws ec2 run-instances \
    --image-id <YOUR_AMI_ID_FROM_ABOVE> \
    --instance-type t2.micro \
    --tag-specifications "ResourceType=instance,Tags=[{Key=Name,Value=brainybee-lab-ec2}]"

📸 Screenshot: Capture your terminal output showing the InstanceId.

▶Console alternative

Navigate to the EC2 Dashboard in the AWS Management Console.
Click Launch Instance.
Name: Enter brainybee-lab-ec2.
AMI: Select Amazon Linux 2 AMI (HVM).
Instance type: Select t2.micro.
Key pair: Select Proceed without a key pair (we will not SSH into it for this lab).
Click Launch instance.

Step 2: Prepare the Serverless Execution Role

Before we can launch an AWS Lambda function, we must create an IAM Role that grants Lambda permission to execute.

Create a trust policy file:

bash

cat > trust-policy.json <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": { "Service": "lambda.amazonaws.com" },
      "Action": "sts:AssumeRole"
    }
  ]
}
EOF

Create the IAM role:

bash

aws iam create-role \
    --role-name brainybee-lambda-role \
    --assume-role-policy-document file://trust-policy.json

[!TIP] Take note of the Arn value in the output (e.g., arn:aws:iam::<YOUR_ACCOUNT_ID>:role/brainybee-lambda-role). You will need it in the next step.

Step 3: Provision Serverless Compute (AWS Lambda)

AWS Lambda lets you run code without provisioning or managing servers. We will create a simple Python function.

Create the Python file:

bash

echo 'def lambda_handler(event, context): return "Hello from Serverless Compute!"' > lambda_function.py

Zip the deployment package:

bash

zip function.zip lambda_function.py

Deploy the Lambda function (replace the <YOUR_ACCOUNT_ID> with your actual AWS 12-digit account ID):

bash

aws lambda create-function \
    --function-name brainybee-lab-lambda \
    --runtime python3.9 \
    --role arn:aws:iam::<YOUR_ACCOUNT_ID>:role/brainybee-lambda-role \
    --handler lambda_function.lambda_handler \
    --zip-file fileb://function.zip

▶Console alternative

Navigate to the Lambda console.
Click Create function.
Choose Author from scratch.
Function name: brainybee-lab-lambda.
Runtime: Python 3.9.
Expand Change default execution role, select Create a new role with basic Lambda permissions.
Click Create function.
In the Code source editor, paste def lambda_handler(event, context): return "Hello from Serverless Compute!".
Click Deploy.

Checkpoints

Verify that your compute resources are successfully running.

Checkpoint 1: Verify EC2 Instance State

Ensure your virtual server is actively running in the cloud.

bash

aws ec2 describe-instances \
    --filters "Name=tag:Name,Values=brainybee-lab-ec2" \
    --query "Reservations[*].Instances[*].{ID:InstanceId,State:State.Name}" \
    --output table

Expected Output: A table showing your Instance ID and the state as running.

Checkpoint 2: Invoke Lambda Function

Execute your serverless code to ensure it processes the request and returns the expected string.

bash

aws lambda invoke \
    --function-name brainybee-lab-lambda \
    output.txt && cat output.txt

Expected Output: "Hello from Serverless Compute!"

Troubleshooting

Issue / Error	Likely Cause	Solution
AccessDeniedException	Your IAM user lacks permissions to create EC2 or Lambda resources.	Attach `PowerUserAccess` or explicitly grant `ec2:` and `lambda:` permissions to your user.
InvalidAMIID.NotFound	The AMI ID doesn't exist in your currently configured AWS Region.	Run the SSM command in Step 1 to dynamically fetch the correct AMI ID for your specific region.
InvalidParameterValueException (Lambda)	The IAM Role hasn't propagated yet.	IAM roles take ~10 seconds to become available globally. Wait 15 seconds and rerun the `create-function` command.

Clean-Up / Teardown

[!WARNING] Cost Warning: Remember to run the teardown commands to avoid ongoing charges. While t2.micro and basic Lambda usage may fall under the Free Tier, it is a critical best practice to delete unused infrastructure.

1. Terminate the EC2 Instance (Replace <YOUR_INSTANCE_ID> with the ID found in Checkpoint 1)

bash

aws ec2 terminate-instances --instance-ids <YOUR_INSTANCE_ID>

2. Delete the Lambda Function

bash

aws lambda delete-function --function-name brainybee-lab-lambda

3. Delete the IAM Role and local files

bash

aws iam delete-role --role-name brainybee-lambda-role
rm trust-policy.json lambda_function.py function.zip output.txt

▶Console alternative

EC2: Go to EC2 Dashboard -> Instances -> Select brainybee-lab-ec2 -> Instance state -> Terminate instance.
Lambda: Go to Lambda Dashboard -> Functions -> Select brainybee-lab-lambda -> Actions -> Delete.
IAM: Go to IAM Dashboard -> Roles -> Search brainybee-lambda-role -> Delete.