⚡ No Server to Manage: Build Your First AWS Serverless API with Lambda + DynamoDB

⚡ No Server to Manage: Build Your First AWS Serverless API with Lambda + DynamoDB

You have probably heard the word:

“Serverless.”

At first, it sounds strange.

How can an application run without a server?

The answer is simple:

Servers still exist.

You just are not provisioning, patching, maintaining, and operating a traditional application server in the same way.

Instead, you focus more on:

🧠 Application logic

🌐 Requests

🔐 Permissions

🗄️ Data

📊 Monitoring

AWS handles much of the underlying infrastructure required to run your function.

For a student learning cloud computing, this creates a great opportunity.

You can build something small...

but still learn how multiple AWS services communicate with each other.

That is exactly what we are going to do.

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🎯 What We Are Building

We are going to create a simple:

Student Learning Tracker API

The API will allow us to save learning records such as:

👤 Student ID

📚 Topic

✅ Status

📝 Notes

🕒 Timestamp

For example:

{
"studentId": "student001",
"topic": "AWS IAM",
"status": "completed",
"notes": "Practiced least privilege and AccessDenied troubleshooting"
}

Instead of storing this information inside a traditional server application, we will connect several AWS services.

Our architecture will look like this:

👨‍💻 Client

↓

🌐 Amazon API Gateway

↓

⚡ AWS Lambda

↓

🗄️ Amazon DynamoDB

↓

📤 JSON Response

Behind the scenes, we will also use:

🔐 AWS IAM

for permissions

and

📊 Amazon CloudWatch

for logs and troubleshooting.

This one small project already introduces several important cloud concepts.

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🧠 What “Serverless” Actually Means

Serverless does not mean:

“There are literally no servers.”

It means that you do not directly manage the servers running your application logic.

With AWS Lambda, you upload your code.

Then AWS handles much of the infrastructure required to run that code when it is invoked.

That changes the developer's focus.

Instead of spending most of your time thinking:

Which virtual machine should I create?

How much CPU do I need?

How do I patch the operating system?

How do I keep the server running?

You begin thinking more about:

⚡ What should this function do?

📥 What request does it receive?

🗄️ What data does it need?

🔐 What permissions does it require?

📤 What response should it return?

📊 How will I troubleshoot it?

That is a different way of designing applications.

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🏗️ Understanding the Architecture First

Before touching the AWS console, understand what each service will do.

🌐 Amazon API Gateway

This will provide the HTTP endpoint our client can call.

For example:

POST /entries

or:

GET /entries/{entryId}

⚡ AWS Lambda

This contains our application logic.

It will:

Receive the request

↓

Understand which route was called

↓

Process the data

↓

Communicate with DynamoDB

↓

Return a response

🗄️ Amazon DynamoDB

This stores our learning records.

Each record becomes an item inside a DynamoDB table.

🔐 AWS IAM

This controls what the Lambda function is actually allowed to do.

Our function should not automatically receive unlimited AWS permissions.

It should receive only the permissions required for this project.

📊 Amazon CloudWatch

This helps us observe what happens when the function runs.

If something fails, logs become one of our most useful troubleshooting tools.

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💰 Before Building: Think About Cost

Before creating cloud resources, develop one habit early:

Always know what you are creating.

Even when experimenting with small learning projects:

💳 Review the pricing model.

📊 Monitor your account.

🔔 Configure budget notifications where appropriate.

🗑️ Remove resources you no longer need.

🧠 Understand which resources remain active after you close your browser.

Cloud engineering is not only about making systems work.

It is also about operating resources responsibly.

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🗄️ STEP #1 — Create the DynamoDB Table

Let's start with the data layer.

Create a DynamoDB table.

Suggested table name:

student-learning-tracker

For this project, use:

Partition key:

entryId

Type:

String

Why entryId?

Because every learning record should have its own unique identifier.

A record might eventually look like this:

entryId:
"e7f2..."

studentId:
"student001"

topic:
"AWS Lambda"

status:
"in-progress"

notes:
"Building my first serverless API"

timestamp:
"2026-09-17T15:30:00Z"

Using a unique entry ID makes it straightforward to retrieve one specific learning record later.

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🧩 Understanding the DynamoDB Item

DynamoDB stores data as items.

You can think of an item as similar to one record.

Example:

entryId
→ Unique identifier

studentId
→ Who owns the entry

topic
→ What they are learning

status
→ planned / in-progress / completed

notes
→ Additional information

timestamp
→ When the entry was created

This is intentionally simple.

A beginner project should teach you the architecture before adding unnecessary complexity.

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🔐 STEP #2 — Understand the Lambda Execution Role

This is where our previous IAM article becomes useful.

Your Lambda function needs permission to communicate with DynamoDB.

But the function should not contain hardcoded AWS access keys.

Instead:

⚡ Lambda Function

↓

🎭 Execution Role

↓

📜 IAM Permissions

↓

🗄️ DynamoDB

For this project, our function needs only a few DynamoDB actions.

For example:

dynamodb:PutItem

dynamodb:GetItem

And ideally, those actions should apply only to:

student-learning-tracker

rather than every DynamoDB table in the account.

That is least privilege in practice.

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📜 Example IAM Permission

Conceptually, the DynamoDB permission might look like this:

{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"dynamodb:PutItem",
"dynamodb:GetItem"
],
"Resource": "YOUR_DYNAMODB_TABLE_ARN"
}
]
}

Replace:

YOUR_DYNAMODB_TABLE_ARN

with the ARN of the table you created.

Do not blindly copy broad permissions simply because they make the project work.

Ask:

What actions does this function actually need?

For our first version:

Write one item.

Read one item.

That is enough.

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⚠️ One IAM Detail Worth Knowing

There is an AWS-managed policy called:

AWSLambdaDynamoDBExecutionRole

The name can sound like:

“This lets my Lambda write items into DynamoDB.”

But that policy is primarily intended for Lambda functions working with DynamoDB Streams.

For our application, where Lambda directly performs actions such as:

dynamodb:PutItem

and

dynamodb:GetItem

we need the corresponding table permissions.

Always inspect what a policy actually grants.

Do not rely only on its name.

That habit will save you from many IAM problems later.

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⚡ STEP #3 — Create the Lambda Function

Now create your Lambda function.

Suggested name:

student-learning-api

Choose a Python runtime.

For the execution role:

Use a role that includes basic Lambda logging permissions and the DynamoDB permissions required by this project.

Next, create an environment variable:

Key:

TABLE_NAME

Value:

student-learning-tracker

Why use an environment variable?

Because we do not want to hardcode configuration everywhere inside the application.

Our code can simply read:

TABLE_NAME

from the environment.

That also makes the function easier to reuse in different environments later.

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💻 STEP #4 — Add the Lambda Code

Here is a simple example.

The function supports:

POST /entries

and

GET /entries/{entryId}

Python example:

import json
import os
import uuid
from datetime import datetime, timezone

import boto3

dynamodb = boto3.resource("dynamodb")
table = dynamodb.Table(os.environ["TABLE_NAME"])

def response(status_code, body):
return {
"statusCode": status_code,
"headers": {
"content-type": "application/json"
},
"body": json.dumps(body)
}

def lambda_handler(event, context):

route_key = event.get("routeKey", "")

if route_key == "POST /entries":

    try:
        body = json.loads(event.get("body") or "{}")
    except json.JSONDecodeError:
        return response(
            400,
            {"message": "Invalid JSON body"}
        )

    required_fields = [
        "studentId",
        "topic",
        "status"
    ]

    missing = [
        field
        for field in required_fields
        if not body.get(field)
    ]

    if missing:
        return response(
            400,
            {
                "message": "Missing required fields",
                "fields": missing
            }
        )

    item = {
        "entryId": str(uuid.uuid4()),
        "studentId": body["studentId"],
        "topic": body["topic"],
        "status": body["status"],
        "notes": body.get("notes", ""),
        "timestamp": datetime.now(
            timezone.utc
        ).isoformat()
    }

    table.put_item(Item=item)

    return response(
        201,
        item
    )


if route_key == "GET /entries/{entryId}":

    path_parameters = (
        event.get("pathParameters") or {}
    )

    entry_id = path_parameters.get("entryId")

    if not entry_id:
        return response(
            400,
            {"message": "entryId is required"}
        )

    result = table.get_item(
        Key={
            "entryId": entry_id
        }
    )

    item = result.get("Item")

    if not item:
        return response(
            404,
            {"message": "Entry not found"}
        )

    return response(
        200,
        item
    )


return response(
    404,
    {"message": "Route not found"}
)

Do not worry if every line does not immediately make sense.

The important thing is understanding the flow.

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🔎 Reading the Code as an Architecture

Let's simplify it.

First:

dynamodb = boto3.resource("dynamodb")

This creates the DynamoDB service resource used by the code.

Then:

table = dynamodb.Table(os.environ["TABLE_NAME"])

This tells our function which table to use.

Then:

route_key = event.get("routeKey", "")

This helps determine which API route triggered the function.

If the request is:

POST /entries

we:

📥 Parse the request body.

✅ Validate required fields.

🆔 Generate a unique entry ID.

🕒 Add a timestamp.

🗄️ Write the item into DynamoDB.

📤 Return the created item.

If the request is:

GET /entries/{entryId}

we:

📥 Read entryId from the URL.

🔎 Search DynamoDB.

📦 Retrieve the item.

📤 Return the item as JSON.

That is already a real API workflow.

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🧪 STEP #5 — Test Lambda Before Adding API Gateway

This is an important troubleshooting habit.

Do not connect every service immediately.

First test the function independently.

Why?

Because if everything is connected at once and the application fails, you now have multiple possible failure points.

Is it:

API Gateway?

Lambda?

IAM?

DynamoDB?

The event structure?

The table?

The Region?

Instead:

Build one layer.

Test it.

Then add another layer.

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🧪 Example POST Test Event

Because the final HTTP API will provide a route key and body, you can simulate something similar.

Example:

{
"routeKey": "POST /entries",
"body": "{\"studentId\":\"student001\",\"topic\":\"AWS Lambda\",\"status\":\"in-progress\",\"notes\":\"Building my first serverless API\"}"
}

Run the Lambda test.

Expected result:

201

and an item containing something similar to:

{
"entryId": "...",
"studentId": "student001",
"topic": "AWS Lambda",
"status": "in-progress",
"notes": "Building my first serverless API",
"timestamp": "..."
}

Now open DynamoDB.

Check the table.

You should see the item.

That proves:

⚡ Lambda executed.

🔐 IAM allowed the write.

🗄️ DynamoDB stored the item.

Before we even create the public API.

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💥 What If Lambda Returns AccessDeniedException?

Excellent.

Now you have a troubleshooting exercise.

The question is:

Who is making the DynamoDB request?

The Lambda execution role.

Then ask:

Does that role allow:

dynamodb:PutItem

on:

the correct DynamoDB table ARN?

Check:

🎭 Execution role

📜 Attached permissions

⚙️ Required action

📦 Resource ARN

🌎 AWS Region

This directly connects to the IAM concepts we learned earlier.

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🌐 STEP #6 — Create the API Gateway HTTP API

Now we need an HTTP endpoint.

Create an API in:

Amazon API Gateway

For a beginner serverless application, an HTTP API provides a clean way to connect HTTP routes to Lambda.

Add your Lambda function as the integration.

Our request flow becomes:

👨‍💻 Client

↓

🌐 API Gateway

↓

⚡ Lambda

↓

🗄️ DynamoDB

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🛣️ STEP #7 — Create the Routes

Create two routes.

First:

POST /entries

Purpose:

Create a new learning record.

Second:

GET /entries/{entryId}

Purpose:

Retrieve one learning record.

Connect both routes to:

student-learning-api

Now the Lambda function can inspect the route key and decide what logic to execute.

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📤 STEP #8 — Test the POST Endpoint

After deployment, API Gateway gives you an invoke URL.

Conceptually:

https://YOUR_API_ID.execute-api.REGION.amazonaws.com

Your POST endpoint becomes:

https://YOUR_API_URL/entries

Example request:

curl -X POST "YOUR_API_URL/entries" \
-H "content-type: application/json" \
-d '{
"studentId": "student001",
"topic": "AWS Lambda",
"status": "completed",
"notes": "Built my first Lambda API"
}'

Expected response:

{
"entryId": "...",
"studentId": "student001",
"topic": "AWS Lambda",
"status": "completed",
"notes": "Built my first Lambda API",
"timestamp": "..."
}

Congratulations.

At that point:

🌐 An HTTP request entered API Gateway.

⚡ API Gateway invoked Lambda.

🔐 Lambda used its execution role.

🗄️ Lambda wrote data into DynamoDB.

📤 Lambda returned JSON.

You just connected four AWS services into one working application.

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📥 STEP #9 — Test the GET Endpoint

Copy the entryId from the POST response.

Now request:

GET /entries/{entryId}

Example:

curl "YOUR_API_URL/entries/YOUR_ENTRY_ID"

Expected response:

{
"entryId": "...",
"studentId": "student001",
"topic": "AWS Lambda",
"status": "completed",
"notes": "Built my first Lambda API",
"timestamp": "..."
}

Now you have:

CREATE

and

READ

functionality.

You could later extend the project with:

✏️ UPDATE

🗑️ DELETE

📋 LIST

🔎 QUERY

🔐 AUTHENTICATION

But do not rush.

Understand the basic architecture first.

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📊 STEP #10 — Use CloudWatch Logs

This is where the project becomes especially useful for troubleshooting.

Lambda invocation logs can be sent to:

Amazon CloudWatch Logs

when the execution role has the required logging permissions.

Instead of guessing why the function failed, inspect the logs.

You can also deliberately add useful messages inside your code.

For example:

print("Processing POST /entries")

or:

print(f"Reading entry: {entry_id}")

Do not log passwords, tokens, personal secrets, or sensitive data.

Logging should help you troubleshoot.

Not create another security problem.

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💥 TROUBLESHOOTING LAB #1 — AccessDeniedException

Scenario:

API Gateway works.

Lambda runs.

But DynamoDB fails.

CloudWatch shows:

AccessDeniedException

Ask:

👤 Who made the request?

Lambda execution role.

⚙️ What action failed?

Maybe:

dynamodb:PutItem

📦 Which resource?

Your DynamoDB table.

Likely investigation:

🔐 Execution role

📜 IAM policy

📦 Table ARN

🌎 Region

This is exactly why IAM knowledge matters.

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💥 TROUBLESHOOTING LAB #2 — ResourceNotFoundException

Suppose Lambda reports:

ResourceNotFoundException

Possible causes:

❌ TABLE_NAME contains the wrong value.

❌ DynamoDB table exists in another Region.

❌ You renamed or deleted the table.

❌ Your code points toward a resource that does not exist.

Check:

🗄️ Exact table name

🌎 Region

⚙️ Environment variable

📦 Resource existence

Do not immediately edit the IAM policy.

ResourceNotFound is not the same error as AccessDenied.

Different symptom.

Different investigation.

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💥 TROUBLESHOOTING LAB #3 — API Returns 500

Your browser or API client receives:

500 Internal Server Error

Do not panic.

Start tracing the request.

🌐 Did API Gateway receive it?

↓

⚡ Was Lambda invoked?

↓

📊 What appears in CloudWatch Logs?

↓

💥 Did the code throw an exception?

↓

🗄️ Did DynamoDB return an error?

Logs can reveal errors such as:

JSON parsing failures

Missing environment variables

DynamoDB exceptions

Programming errors

Unexpected event structures

This is where observability turns guessing into evidence.

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💥 TROUBLESHOOTING LAB #4 — Invalid JSON

Try sending:

{
studentId: student001
}

That is not valid JSON.

Our function attempts:

json.loads(...)

and should return:

400

with something like:

Invalid JSON body

This teaches another important lesson:

Not every application failure is an AWS infrastructure problem.

Sometimes the request itself is wrong.

Always identify the failure layer.

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💥 TROUBLESHOOTING LAB #5 — Lambda Works, API Gateway Doesn't

This scenario is particularly educational.

Lambda works when tested directly.

But the API endpoint fails.

What changed?

The application logic may be fine.

Now investigate the integration layer.

Check:

🌐 API Gateway route

⚡ Lambda integration

🛣️ Route key

📥 Event structure

🔐 Permission for API Gateway to invoke Lambda

📊 CloudWatch logs

This is why testing each layer independently is so useful.

You can say:

Lambda works.

DynamoDB works.

The failure appears only when API Gateway enters the path.

You have already reduced the troubleshooting scope.

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🌍 TROUBLESHOOTING LAB #6 — Browser Says CORS Error

Your API works through a terminal tool.

But your web application cannot call it.

The browser reports a CORS problem.

Now you are dealing with another layer.

CORS determines which origins can make browser-based requests to the API.

If you later connect this API to a frontend, configure CORS intentionally.

Do not blindly allow everything in a production application.

Instead, understand:

🌐 Which frontend origin needs access?

⚙️ Which HTTP methods are required?

📋 Which headers are required?

Security configuration should match the application.

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🔐 Security: Do Not Stop at “It Works”

A working API is only the beginning.

Ask:

Who should be allowed to use this endpoint?

Our learning version may be intentionally simple.

A real application may require authentication and authorization.

Possible options can include:

🔐 IAM authorization

👤 Amazon Cognito

🔑 JWT-based authorization

or another architecture appropriate to the application.

Also think about:

🧹 Input validation

🚦 Rate limiting and throttling

📜 Logging

🔐 Least-privilege IAM

🔒 Encryption

🗄️ Data sensitivity

🧾 Auditability

Security should not be something added only at the end.

It should be part of how you think about the design.

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🔑 Never Put AWS Access Keys in the Lambda Code

Do not write:

AWS_ACCESS_KEY_ID = "..."

AWS_SECRET_ACCESS_KEY = "..."

inside your application.

Lambda already supports execution roles.

The application can receive temporary credentials through the AWS environment based on that role.

This is one of the major advantages of using IAM roles correctly.

Your application code should describe:

What the application does.

IAM should describe:

What the application is allowed to do.

Keep those responsibilities separate.

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🛡️ Practice Least Privilege

Our Lambda does not need:

AdministratorAccess

It does not need:

FullAccess to every DynamoDB table.

For this version, it might only need:

dynamodb:PutItem

dynamodb:GetItem

on:

student-learning-tracker

That is much closer to good cloud security.

When the project grows, add permissions intentionally.

Do not start with everything.

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🧠 Think About Failure Before Production

Ask yourself:

What happens if DynamoDB is unavailable?

What happens if the request body is malformed?

What happens if an item does not exist?

What happens if a user sends a huge request?

What happens if Lambda throws an exception?

What happens if permissions change?

What happens if someone repeatedly calls the API?

These questions move you from:

“I made a tutorial work.”

toward:

“I am thinking about how a system behaves.”

That difference matters.

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📊 Observe the Entire Request Path

When the system works:

👨‍💻 Client

↓

🌐 API Gateway

↓

⚡ Lambda

↓

🔐 IAM authorization

↓

🗄️ DynamoDB

↓

⚡ Lambda

↓

🌐 API Gateway

↓

📤 Client

When something fails, identify where the request stopped.

For example:

Client

↓

API Gateway

↓

Lambda

↓

❌ AccessDeniedException

Likely investigation:

Lambda execution role

Or:

Client

↓

API Gateway

↓

❌ Lambda never invoked

Likely investigation:

API route or integration

Or:

Client

↓

❌ Browser CORS failure

Likely investigation:

API CORS configuration

Troubleshooting becomes easier when you can visualize the architecture.

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🧪 Intentionally Break the Application

Once everything works, break it.

Seriously.

In a controlled lab environment, try:

💥 Remove dynamodb:PutItem.

💥 Change TABLE_NAME to the wrong table.

💥 Send malformed JSON.

💥 Request an entry that does not exist.

💥 Change the API route.

💥 Remove required integration permissions.

Then observe:

🚨 Error

↓

📊 Evidence

↓

🔎 Investigation

↓

🧠 Hypothesis

↓

🛠️ Fix

↓

✅ Retest

A working tutorial teaches you the happy path.

A broken tutorial teaches you troubleshooting.

You need both.

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📝 Document the Project Like an Engineer

Do not finish the project and immediately forget it.

Create a GitHub repository.

Your README could contain:

📌 Project Name

Student Learning Tracker Serverless API

🎯 Objective

Build a beginner-friendly serverless API using AWS managed services.

🏗️ Architecture

Client

↓

API Gateway

↓

Lambda

↓

DynamoDB

☁️ AWS Services Used

API Gateway

AWS Lambda

Amazon DynamoDB

AWS IAM

Amazon CloudWatch

🔐 Security

Lambda execution role

Least-privilege DynamoDB access

No hardcoded AWS credentials

🧪 API Routes

POST /entries

GET /entries/{entryId}

💥 Problems Encountered

AccessDenied

Incorrect table name

Malformed JSON

API integration errors

🔎 Troubleshooting

Describe how each problem was isolated and resolved.

📚 Lessons Learned

Explain what you actually understand now that you did not understand before.

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📸 Add Evidence to Your GitHub Project

Useful screenshots could include:

🏗️ Architecture diagram

🗄️ DynamoDB table structure

⚡ Lambda function configuration

🌐 API Gateway routes

📊 CloudWatch logs

🧪 Successful API test

🚨 One failed test and its resolution

But be careful.

Do not expose:

❌ Access keys

❌ Secret keys

❌ Tokens

❌ Passwords

❌ Sensitive account data

❌ Private information

Screenshots should prove your work without leaking credentials.

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🎤 Turn the Project Into an Interview Story

Instead of saying:

“I know Lambda and DynamoDB.”

You could say:

“I built a serverless Student Learning Tracker API using API Gateway, Lambda, DynamoDB, IAM, and CloudWatch. API Gateway exposed POST and GET routes, Lambda handled the application logic, and DynamoDB stored the records. I configured the Lambda execution role with only GetItem and PutItem permissions for the required table. During testing I intentionally removed PutItem, reproduced an AccessDeniedException, used CloudWatch logs to identify the authorization failure, restored the least-privilege permission, and verified the API again.”

That one project demonstrates:

⚡ Lambda

🌐 APIs

🗄️ Databases

🔐 IAM

📊 Monitoring

🔎 Troubleshooting

🧠 Architecture thinking

🗣️ Technical communication

That is much stronger than simply saying:

“Familiar with AWS.”

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🧩 How This Connects to AWS Certification Learning

This project helps turn several certification concepts into something real.

When you study:

AWS Lambda

you now remember the function you deployed.

When you study:

IAM roles

you remember the execution role that needed DynamoDB permissions.

When you study:

DynamoDB

you remember your table and partition key.

When you study:

API Gateway

you remember the HTTP routes.

When you study:

CloudWatch

you remember reading logs after an application failure.

Theory becomes easier to remember when it has a story attached to it.

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🚀 Where You Can Take This Project Next

Once the basic API works, you can extend it gradually.

Possible Version 2 features:

✏️ Update an entry

DELETE /entries/{entryId}

🗑️ Delete an entry

DELETE /entries/{entryId}

📋 List learning entries

GET /entries

🔎 Filter by student or topic

👤 Add authentication

🌐 Build a frontend

📊 Add custom monitoring

🔐 Improve authorization

🏗️ Deploy through Infrastructure as Code

Do not add all of these on Day 1.

Build in layers.

Version 1 should teach you the foundation.

Version 2 should teach you the next problem.

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🧹 Clean Up When You Finish

Cloud learning also means knowing how to remove infrastructure.

When you no longer need the lab:

🗑️ Remove the API Gateway API.

🗑️ Delete the Lambda function.

🗑️ Delete the DynamoDB table if the data is no longer needed.

🗑️ Remove unnecessary IAM policies and roles.

📊 Review CloudWatch log groups.

💰 Check your billing dashboard.

A project is not finished until you understand its lifecycle.

Create.

Operate.

Troubleshoot.

Clean up.

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🌱 The Bigger Lesson

At first, this project looks like:

“Build a Lambda function.”

But it is actually teaching much more.

🌐 API Gateway teaches you how clients reach backend logic.

⚡ Lambda teaches event-driven compute.

🗄️ DynamoDB teaches managed NoSQL storage.

🔐 IAM teaches service-to-service authorization.

📊 CloudWatch teaches observability.

💥 Failures teach troubleshooting.

📝 GitHub teaches documentation.

🎤 Explaining the project teaches communication.

One project.

Multiple skills.

That is why I prefer learning AWS through small systems instead of memorizing isolated service definitions.

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🎯 Final Thoughts

You do not need to build the next massive cloud platform to start learning serverless architecture.

Start with:

One API.

One Lambda function.

One DynamoDB table.

One IAM role.

A few routes.

Some logs.

Then make it work.

Break it.

Understand why it broke.

Fix it.

Document what you learned.

That cycle matters more than simply getting a green success message.

The real goal is not:

“I followed a Lambda tutorial.”

The goal is:

“I understand how an HTTP request travels through my AWS architecture, what permissions each component requires, where failures can occur, and how I would troubleshoot them.”

That is practical cloud learning.

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☁️ Your Turn

Imagine this scenario:

🌐 API Gateway receives the request.

⚡ Lambda runs.

📊 CloudWatch confirms the function was invoked.

🗄️ The DynamoDB table exists.

But Lambda returns:

AccessDeniedException

What would you investigate first?

🎭 The Lambda execution role?

⚙️ The DynamoDB action?

📦 The table ARN?

🌎 The Region?

📜 An explicit deny?

Or something else?

💬 Share how you would troubleshoot it.

And if you build your own serverless project:

Do not just share the finished architecture.

Share the problem that taught you the most. 🚀

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📚 Official AWS References

AWS Lambda + API Gateway Tutorial:
https://docs.aws.amazon.com/lambda/latest/dg/services-apigateway-tutorial.html

AWS Lambda Execution Roles:
https://docs.aws.amazon.com/lambda/latest/dg/lambda-intro-execution-role.html

AWS Lambda CloudWatch Logs:
https://docs.aws.amazon.com/lambda/latest/dg/monitoring-cloudwatchlogs.html

Amazon DynamoDB Getting Started:
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GettingStartedDynamoDB.html