DEV Community: Amr Abed

Stop Writing Lambda Boilerplate

Amr Abed — Thu, 01 Oct 2026 02:30:28 +0000

Introducing the cur8d/lambda open-source repository — a collection of production-ready Python Lambda templates for Bedrock Agent, REST API, GraphQL, DynamoDB Stream, EventBridge, S3, and SQS scenarios. These templates come pre-integrated with AWS Lambda Powertools, AWS CDK, Pydantic, and a robust testing infrastructure.

Every serverless developer knows the feeling. You spin up a new Lambda project, and before writing a single line of business logic, you're already copying boilerplate from a previous project: setting up AWS Lambda Powertools, wiring up structured logging, configuring X-Ray tracing, and building yet another Repository class for DynamoDB access.

This repetitive setup isn't just tedious — it's a source of inconsistency across projects and a hidden risk. Teams often skip best practices under time pressure, and the "quick Lambda" becomes the undocumented, untested function that nobody dares touch six months later.

That's why I built cur8d/lambda — a collection of production-ready, plug-and-play Python Lambda templates for some of the common real-world scenarios on AWS.

The Problem with Lambda Bootstrapping

AWS Lambda's promise is simplicity: deploy a function, it scales. But production Lambda functions require significantly more than a handler file. You need:

Structured logging that integrates with CloudWatch Insights
Distributed tracing with AWS X-Ray
Metrics for operational visibility
Batch processing for event sources like SQS and DynamoDB Streams
Strong typing to catch bugs at development time, not runtime
Infrastructure as Code, so your function is reproducible and auditable
Comprehensive tests with AWS service mocking

Most developers piece this together manually each time — or worse, they copy a "good enough" version from a previous project and gradually accumulate inconsistencies.

Introducing cur8d/lambda

cur8d/lambda is a GitHub template repository that gives you a complete, opinionated, production-grade starting point for Python Lambda development. It currently covers seven real-world scenarios out of the box:

Bedrock Agent — Handle function-based actions from Amazon Bedrock Agents
GraphQL API — Resolve AppSync GraphQL requests
REST API — Handle API Gateway REST requests
DynamoDB Stream — Batch-process stream events with failure handling
EventBridge — React to events and call external APIs
S3 to SQS — Queue messages on S3 object changes
SQS to DynamoDB — Batch-process SQS messages into DynamoDB

Each template is a fully self-contained starting point. Pick your scenario, rename the project, and start writing business logic immediately.

What’s Pre-Wired for You

AWS Lambda Powertools

Every template integrates AWS Lambda Powertools for Python — AWS's first-party library for Lambda best practices. This gives you:

Structured JSON logging with correlation IDs automatically injected
CloudWatch EMF Metrics for operational dashboards without custom code
AWS X-Ray Tracing with automatic subsegment creation
Batch processing utilities for SQS and DynamoDB Streams, including partial failure handling
Parameter and Secrets loading with caching built in
Event source data classes for type-safe event parsing

You don't configure any of this — it's already there. More on that later.

Pydantic Data Modeling

All input/output models are defined using Pydantic, giving you automatic validation, serialization, and type safety.

from uuid import uuid4

from pydantic import BaseModel, Field
from pydantic.alias_generators import to_camel


class Item(BaseModel, alias_generator=to_camel, populate_by_name=True):
    id: str = Field(
        description="Unique item identifier", default_factory=lambda: str(uuid4())
    )
    name: str = Field(description="Human-readable item name")

The alias_generator=to_camel configuration means the JSON payload from API Gateway uses camelCase while the Python code uses snake_case — matching JSON conventions on the wire and Python conventions in code, with zero manual mapping.

Infrastructure as Code with AWS CDK

Every template ships with a matching AWS CDK stack under infra/stacks/. Deploy any stack with a single command:

make deploy STACK=api

No manual console clicks. No documentation to update. The infrastructure is the code.

Clean Architecture with the Repository Pattern

All database access is encapsulated in a Repository class, separating your business logic from AWS service calls. This makes testing trivial and keeps your handler functions focused on what matters: the business logic.

Testing That Actually Tests Things

The test suite uses:

pytest for test orchestration
moto for AWS service mocking (DynamoDB, SQS, S3 — all mocked locally)
Hypothesis for property-based testing, automatically generating edge-case inputs
coverage for coverage reporting

Run the entire suite with:

make test

Code Quality, Automated

ruff for linting and formatting (replacing Flake8, isort, Black)
pyright for static type checking
pre-commit hooks to enforce quality before every commit
Dependabot for automated dependency updates

Developer Experience

Dev Containers support means you can open the repo in VS Code and have the entire development environment running in Docker — Python, Poetry, CDK, and all tools pre-installed—no setup required.
MkDocs for auto-generated API documentation published to GitHub Pages
GitHub Actions workflows for CI/CD and documentation deployment
AI Agent guidelines (AGENTS.md) providing context for AI coding assistants working in the repo

AWS Lambda Powertools — In Practice

Every template integrates AWS Lambda Powertools for Python as a first-class citizen — not as an afterthought bolted on after the fact. Here's how it actually shapes each template.

Batch Processing with Partial Failure Handling

This is where Powertools saves the most code — and prevents the most production incidents.

Without Powertools, batch processing from SQS or streams requires you to manually track which records succeeded and which failed, then construct a batchItemFailures response telling Lambda which messages to retry. Get it wrong and you either re-process successfully handled messages (causing duplicates) or silently drop failed ones.

The SQS template uses BatchProcessor to handle this automatically:

from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.batch.types import PartialItemFailureResponse

processor = BatchProcessor(event_type=EventType.SQS)


def handle_record(record) -> None:
    try:
        ...
    except Exception as exc:
        logger.error("Failed to process record", exc_info=exc)
        raise  # Re-raise so BatchProcessor marks it as a failed item


def main(event, context) -> PartialItemFailureResponse:
    return process_partial_response(
        event=event,
        record_handler=handle_record,
        processor=processor,
        context=context,
    )

If handle_record raises for one message, BatchProcessor catches it, logs the failure, continues processing the rest of the batch, and returns the correct batchItemFailures response. Your DLQ only receives the genuinely failed messages — not the whole batch.

The same pattern applies to DynamoDB Streams, where handling partial failures is equally critical to prevent stream stalls:

from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)

processor = BatchProcessor(event_type=EventType.DynamoDBStreams)


def handle_record(record):
    try:
        ...
    except Exception as exc:
        logger.error("Failed to process record", exc_info=exc)
        raise  # Re-raise so BatchProcessor marks it as a failed item


def main(event, context):
    return process_partial_response(
        event=event,
        record_handler=handle_record,
        processor=processor,
        context=context,
    )

Event Parsing and Handling

Lambda Powertools really shines here. Whether it’s handling a Bedrock Agent tool use, REST API request, or a GraphQL query, Powertools provides resolvers that eliminate custom event parsing.

The Bedrock Agent template uses the BedrockAgentFunctionResolver and the @app.tool decorator:

from aws_lambda_powertools.event_handler import BedrockAgentFunctionResolver
from aws_lambda_powertools.utilities.data_classes import BedrockAgentEvent

app = BedrockAgentFunctionResolver()


@app.tool(name="getItem", description="Gets item details by ID")
def get_item(item_id: str) -> dict: ...


@app.tool(
    name="createItem",
    description="Creates a new item with name and optional description",
)
def create_item(item_id: str, name: str, description: "str | None = None) -> dict: ..."


def main(event: BedrockAgentEvent, context: LambdaContext) -> dict:
    """Lambda entry point for the Bedrock Agent handler"""
    return app.resolve(event, context)

The REST API template uses the APIGatewayRestResolver and Flask-like decorators — meaning if you've used Flask or FastAPI, you'll feel right at home:

from aws_lambda_powertools.event_handler import APIGatewayRestResolver
from aws_lambda_powertools.event_handler.api_gateway import Response

app = APIGatewayRestResolver()


@app.get("/items/<id>")
def get_item(id: str) -> Response:
    """Retrieve an item by ID"""
    ...

    return Response(status_code=200, content_type="application/json", body=dumps(item))


@app.post("/items")
def create_item() -> Response:
    """Create a new item from the request body"""
    body = app.current_event.json_body
    ...
    return Response(status_code=201, content_type="application/json", body=dumps(item))


def main(event: dict, context: LambdaContext) -> dict:
    """Lambda entry point for the API Gateway handler"""
    return app.resolve(event, context)

The EventBridge template uses the event_parser to ensure your events match your Pydantic models before your logic even runs:

from aws_lambda_powertools.utilities.parser import event_parser
from aws_lambda_powertools.utilities.parser.models import EventBridgeModel


@event_parser(model=EventBridgeModel)
def main(event: EventBridgeModel, context) -> None:
    handle(event)

Combining Powertools with Pydantic

One of the most useful patterns across the templates is combining Powertools event data classes with Pydantic models for end-to-end type safety.

Powertools parses the raw Lambda event into a typed object (so you're not indexing into raw dicts). Pydantic then validates and deserializes the business payload within that event:

from aws_lambda_powertools.event_handler import APIGatewayRestResolver
from pydantic import ValidationError

app = APIGatewayRestResolver()


@app.post("/items")
def create_item() -> Response:
    # Powertools resolves the typed API Gateway envelope
    body = app.current_event.json_body
    # Pydantic validates the business payload
    try:
        item = Item.model_validate(body)
    except ValidationError as exc:
        return Response(
            status_code=422,
            content_type="application/json",
            body=dumps({"error": "..."}),
        )
    ...
    return Response(
        status_code=201, content_type="application/json", body=dumps(item.dump())
    )

In the Bedrock Agent template, the same pattern applies via BedrockAgentFunctionResolver — Powertools resolves the agent event, and typed function signatures replace manual parameter extraction:

# templates/agent/handler.py
from aws_lambda_powertools.event_handler import BedrockAgentFunctionResolver
from aws_lambda_powertools.utilities.data_classes import BedrockAgentEvent
from aws_lambda_powertools.utilities.typing import LambdaContext

from templates.agent.models import Item

repository = Repository(settings.table_name)
app = BedrockAgentFunctionResolver()


@app.tool(name="getItem", description="Gets item details by ID")
def get_item(item_id: str) -> dict:
    item = repository.get_item(item_id)
    if not item:
        return {"error": f"Item {item_id} not found"}
    return item


@app.tool(
    name="createItem",
    description="Creates a new item with name and optional description",
)
def create_item(item_id: str, name: str, description: "str | None = None) -> dict:"
    item = Item(id=item_id, name=name, description=description)
    repository.put_item(item.model_dump())
    return item.model_dump(by_alias=True, exclude_none=True)


def main(event: BedrockAgentEvent, context: LambdaContext) -> dict:
    """Lambda entry point for the Bedrock Agent handler"""
    return app.resolve(event, context)

Fetching Parameters and Secrets

In the EventBridge template, the handler calls an external API using an API token stored in the Secrets Manager. With Lambda Powertools, that adds exactly three lines of code:

from aws_lambda_powertools.utilities.parameters import SecretsProvider

secret_provider = SecretsProvider()

token = secret_provider.get(secret_name)

The same goes for loading configuration from the Parameter Store (SSMProvider), AppConfig (AppConfigProvider), or a DynamoDB table (DynamoDBProvider):

from aws_lambda_powertools.utilities.parameters import SSMProvider

provider = SSMProvider()

configuration = provider.get(key)

Structured Logging That Travels With Your Request

The logger is initialized once at module level and injected into every handler via the @logger.inject_lambda_context decorator. This automatically appends the Lambda context (function name, version, cold start flag, and AWS request ID) to every log line — without you writing a single line of configuration code.

from aws_lambda_powertools import Logger

logger = Logger(service="api")


@logger.inject_lambda_context(log_event=True)
def handler(event: APIGatewayProxyEventV2, context: LambdaContext) -> dict:
    logger.info("Processing request", path=event.path, method=event.http_method)
    ...

The output in CloudWatch is structured JSON from the first line:

{
  "level": "INFO",
  "message": "Processing request",
  "path": "/users",
  "method": "GET",
  "service": "api",
  "function_name": "my-api-handler",
  "cold_start": true,
  "aws_request_id": "abc-123"
}

This matters at scale: CloudWatch Logs Insights can now query your logs by any of these fields. Finding all cold starts, or all errors on a specific path, becomes a one-line query instead of a regex hunt through raw strings.

Tracing Without Instrumentation Noise

X-Ray tracing is applied via two decorators that require no additional configuration:

from aws_lambda_powertools import Tracer

tracer = Tracer(service=settings.service_name)


# On methods — creates child subsegments in X-Ray
@tracer.capture_method
def handle_record(record: SQSRecord) -> None:
    repository.put_item(process(record.body))


# On the Lambda handler — creates the root X-Ray segment
@tracer.capture_lambda_handler
def main(event: dict, context: LambdaContext) -> dict: ...

The @tracer.capture_lambda_handler decorator creates the root X-Ray segment, and @tracer.capture_method on Repository methods creates child subsegments — so your X-Ray service map shows handler → repository → DynamoDB as distinct, timed segments. You can see exactly where latency lives without writing a single xray_recorder.begin_subsegment() call.

CloudWatch Metrics Without Custom Namespaces

Most Lambda functions either skip custom metrics entirely — relying on the default Lambda service metrics — or end up with a tangled mix of boto3 CloudWatch put_metric_data calls scattered across the codebase. Neither approach is great in production.Powertools solves this with the Metrics utility, which uses CloudWatch Embedded Metric Format (EMF) to emit custom metrics as structured log lines. No boto3 calls, no extra latency, no IAM permissions beyond what CloudWatch Logs already has.

Each template initializes a Metrics instance at module level with a shared namespace and service name:

from aws_lambda_powertools import Metrics
from aws_lambda_powertools.metrics import MetricUnit

metrics = Metrics(namespace=settings.metrics_namespace, service=settings.service_name)


def process(event, context):
    ...
    metrics.add_metric(name="Processed", unit=MetricUnit.Count, value=1)


@metrics.log_metrics
def main(event: dict, context: LambdaContext) -> dict:
    return process(event, context)

The @metrics.log_metrics decorator flushes all buffered metrics at the end of each invocation as a single EMF-formatted log line — CloudWatch automatically parses it and makes those metrics available in dashboards and alarms without any additional infrastructure.

capture_cold_start_metric=True is a one-liner that adds a ColdStart metric to every function automatically. In production, tracking cold starts per function gives you a clear signal for when to invest in provisioned concurrency — no custom instrumentation needed.

Getting Started in Under 5 Minutes

1. Create your repository

Click Use this template on GitHub to create a new repository from the template.

2. Rename the project

After cloning, run the setup command once:

make project NAME="new-name" DESCRIPTION="New description" AUTHOR="Your Name" EMAIL="you@example.com" GITHUB="yourusername"

This renames all references throughout the codebase in a single step.

3. Install and run

make install      # Install dependencies via Poetry
make precommit    # Install pre-commit hooks
make test         # Run tests with coverage

4. Deploy

That's it. You're shipping production-grade Lambda code, not maintaining boilerplate.

make deploy STACK=api    # Deploy the REST API scenario

Design Decisions Worth Noting

Python 3.14+: The templates target the latest Python runtime. Lambda supports Python 3.14, and you should be using it.
Poetry over pip/requirements.txt: Deterministic dependency resolution and lock files eliminate the "works on my machine" problem. pyproject.toml is the single source of truth for the entire project configuration.
Makefile as the interface: All common operations — install, test, lint, deploy, docs — are exposed through make commands. This creates a consistent interface regardless of the underlying tools, making CI/CD pipeline configuration trivial.
camelCase for JSON, snake_case for Python: AWS services speak camelCase; Python speaks snake_case. Pydantic's alias_generator=to_camel bridges the gap automatically, keeping both sides idiomatic.

The Bedrock Agent Template

One template deserves special mention: the Bedrock Agent template. As generative AI workloads move to production on AWS, Lambda functions increasingly serve as the action executors behind Amazon Bedrock Agents. This template gives you a proper starting point for building agent action groups — with typed request/response models, error handling, and the exact response schema Bedrock expects.

It's production-ready for the AI-native workloads being built today.

Contributing

The repository is open source under the MIT license. Contributions are welcome — whether that's a new template scenario, improved infrastructure code, additional test coverage, or documentation improvements. Check out the contributing guidelines to get started.

If you're building on AWS Lambda and you find yourself writing the same setup for the third time, I hope cur8d/lambda saves you that time and helps you ship better, more consistent serverless code.

Ready to start? Head over to the repository and click "Use this template":

👉 github.com/cur8d/lambda

The Only AIP-C01 Study Plan You Need

Amr Abed — Thu, 01 Oct 2026 02:30:26 +0000

How I Passed as One of the First 5,000 — And the Exact Plan I Used

I recently passed the new AWS Certified Generative AI Developer Professional (AIP-C01) exam as an early adopter (one of the first 5,000 worldwide). Here’s the exact 6-week plan I used, so you don’t have to figure it out from scratch.

AWS Certified AWS Generative AI Developer Professional Badge

What you’ll find here is a complete, curated, sequenced study plan — the one I wish someone had handed me before I started. No fluff, just the resources that matter, in the order they matter.

In this guide, you’ll find:

The Fast Track (if you’re short on time)
Detailed Study Plan
Notion Tracking Template
Time-Saving Lab Strategies

Do I really need to prepare?

“I have been using AWS Generative AI for the last year or so. Do I really need to prepare?”

Short answer: Yes!

No matter how long you have been working with AWS Generative AI tools, you may not be using all the tools covered by the exam in your day-to-day experience, or you may not have encountered all the scenarios seen on the exam. That’s proven true for any AWS certification exam, not only this one. If you have taken any AWS certification exam before, you already know that. In short, being *AWS-*ome at what you do daily and passing an AWS certification exam are two different skills, though highly interconnected.

That said, a minimum level of familiarity with the exam topic and at least some of the tools covered is required to pass. No amount of preparation can make up for that. Always make sure you are prepared before scheduling and taking the exam.

How long does it take?

Plan for 6–8 weeks at roughly 2–3 hours per day, depending on your existing Bedrock experience.

What to expect?

If you’re wondering how hard the AIP-C01 is, it felt harder than the AWS Machine Learning Specialty exam but more focused than the Solutions Architect exam. Expect Bedrock depth and several multi-service scenario questions. My practice scores plateaued at around 75% for the first couple of weeks, then eventually rose to around 95%. Don’t panic if progress feels slow mid-way. The domains click together in the final stretch.

The Fast Track

If you are pressed for time:

Focus on the Bedrock User Guide and the Exam Prep Plan
The Generative AI for Developers videos are highly recommended for some exam tips and tricks
You can just read only the topics that address any knowledge gaps you find in the Advanced Learning Plan

Notion Tracking Template

I have created a Notion database of study-plan items to track my preparation progress and group, sort, or filter by certification, plan, format, and domain. I am sharing it here so others can benefit as well. The database is available as a template at amrabed.notion.site/aws-exam-prep. Feel free to duplicate and use right away.

The database has content for other exams as well. Remember to filter by certification for AWS Certified Generative AI Developer — Professional

Notion database for AWS Exam Preparation

Preparation Material

The AWS Training & Certification team recommends these two learning plans for preparation:

I added a couple more learning plans for more hands-on practice:

Both the AWS SimuLearn and the AWS Cloud Quest plans share the same labs. The list below points to the AWS SimuLearn labs, but you may want to do the Cloud Quest plan instead to earn a training badge.

Amazon Web Services (AWS) also produced a dedicated LinkedIn Live 4-episode series for the exam, Generative AI for Developers. I highly recommend watching them for some exam tips and tricks.

Last but definitely not least, the Bedrock User Guide is a must-read for this exam. I have highlighted the most relevant parts for each domain below.

Create an AWS skills profile if you don’t already have one. You will earn points and training badges during your preparation, and you want to showcase them.

AWS Skills Profile

Before you start

Go over the Exam Guide 🆓 to understand the exam structure
Take the Official Practice Question Set 🆓 to identify your knowledge gaps
Go over the Exam Prep Overview 🆓

The 6-Week Plan

Day 0: Exam guide + Practice Questions
Day 1–13: Domain 1 (31%)
Day 14–24: Domain 2 (26%)
Day 25–31: Domain 3 (20%)
Day 32–40: Domains 4 & 5 (23%)
Day 41–42: Final Review & Pretest

My Comprehensive Study Plan

I have curated the list below covering the readings, labs, videos, and practice questions from the material above in a recommended best-effort sequence that aligns with the exam guide and provides the most comprehensive preparation.

Most of the items on the list require a subscription. The free items are marked 🆓. The subscription is currently $29 per month — totally worth it! And, you can always try to complete all the paid items on the list within one month 😉

Please feel free to copy the list and use it as a preparation checklist.

Domain 1: FM Integration, Data Management, and Compliance

Documentation 🆓

Hands-on Labs

Overview

Explore the Amazon Bedrock Playgrounds 🆓
Get Started with Generative AI 🆓
Use AI Services with Amazon SageMaker 🆓
Introduction to Generative AI
Generative AI for Personalized Marketing

Foundation Models

Prompt Engineering

Knowledge Bases

Fine Tuning

Review and Questions

Domain 1 Review 🆓
Domain 1 Questions

Video 🆓

Generative AI For Developers | S1 E1 | Exam Overview and FM Integrations

Optional Reading

Domain 2: Implementation and Integration

Documentation 🆓

Hands-on Labs

Agentic AI

Model Deployment & Integration

Development Tools

Review and Questions

Video 🆓

Generative AI For Developers | S1 E2 | Implementation and Integration

Optional Reading

Domain 3: AI Safety, Security, and Governance

Documentation 🆓

Hands-on Labs

Review and Questions

Domain 3 Review 🆓
Domain 3 Questions

Video 🆓

Generative AI For Developers | S1 E3 | AI Safety, Security, and Governance

Optional Reading

Domain 4: Operational Efficiency and Optimization for AI Applications

Documentation 🆓

Review and Questions

Video 🆓

Generative AI For Developers | S1 E4 | Operations and Troubleshooting

Optional Reading

Domain 5: Testing, Validation, and Troubleshooting

Documentation 🆓

Evaluate models

Review and Questions

Video 🆓

Episode 4 also covers domain 5 in the last 10 minutes

Optional Reading

Final Review and Pretest

Go over the Exam Prep Summary 🆓
Retake: Official Practice Question Set 🆓, Domain 1 Questions, Domain 2 Questions, Domain 3 Questions, Domain 4 Questions, Domain 5 Questions
Take the Official Pretest

Bonus

Take the AWS Agentic AI Demonstrated microcredential
Play the Generative AI track on AWS Card Clash just for fun!

Would you still like more?

If you need even more training, you can always search the AWS Skill Builder content with filters like:

Roles: Generative AI Development Engineer
Format: Learning plan

AWS Skill Builder Gen AI Learning Plans

Time-Saving Tips

As you see in the study plan above, there is a lot to cover, ideally in no more than a month or two. So, you’ll need to save as much time as you can during your preparation. Here are a few tips and tricks based on my experience, especially if you decide to take the hands-on labs. You will notice that most of the lab time is consumed by provisioning the environment, deploying model endpoints, and running Jupyter notebooks. Here is how you can finish the labs faster.

No idle time

While the lab environment is being provisioned, don’t wait for provisioning to complete before starting. Go through the instructions for all three stages (Learn, Practice, and DIY) to hit the ground running once the environment is ready.

Parallelize tasks

Many DIY sections do not depend on the practice sections, which means you can complete both practice and DIY together. For instance, the practice section may ask you to start a 2-instance cluster, which the DIY asks to scale up to 3 instances. You can just go ahead and start a 3-instance cluster right away. Moreover, some steps are independent of one another, so open multiple tabs and continue to the next step while the current one completes.

Run all cells

For the Jupyter notebooks, skim through the notebook to understand the concepts, add any missing configuration or code, then run all cells rather than cell by cell.

Split screen

This is not technically a preparation tip, but it helps. Once the AWS sandbox environment is loaded, split the screen between the lab window with instructions and the AWS environment window to follow along more quickly.

When to schedule the exam

I prefer to schedule the exam only when I can answer at least 90% of the questions correctly, with each question taking no more than 90 seconds on average. That gives me confidence that I can pass the exam smoothly, even under the added stress.

I only schedule the exam when I can hit 90%+ accuracy, spending no more than 90 seconds per question. That’s my personal go/no-go signal.

DEV Community: Amr Abed

Stop Writing Lambda Boilerplate

The Problem with Lambda Bootstrapping

Introducing cur8d/lambda

What’s Pre-Wired for You

AWS Lambda Powertools

Pydantic Data Modeling

Infrastructure as Code with AWS CDK

Clean Architecture with the Repository Pattern

Testing That Actually Tests Things

Code Quality, Automated

Developer Experience

AWS Lambda Powertools — In Practice

Batch Processing with Partial Failure Handling

Event Parsing and Handling

Combining Powertools with Pydantic

Fetching Parameters and Secrets

Structured Logging That Travels With Your Request

Tracing Without Instrumentation Noise

CloudWatch Metrics Without Custom Namespaces

Getting Started in Under 5 Minutes

Design Decisions Worth Noting

The Bedrock Agent Template

Contributing

The Only AIP-C01 Study Plan You Need

How I Passed as One of the First 5,000 — And the Exact Plan I Used

Do I really need to prepare?

How long does it take?

What to expect?

The Fast Track

Notion Tracking Template

Preparation Material

Before you start

The 6-Week Plan

My Comprehensive Study Plan

Domain 1: FM Integration, Data Management, and Compliance

Domain 2: Implementation and Integration

Domain 3: AI Safety, Security, and Governance

Domain 4: Operational Efficiency and Optimization for AI Applications

Domain 5: Testing, Validation, and Troubleshooting

Final Review and Pretest

Bonus

Would you still like more?

Time-Saving Tips

No idle time

Parallelize tasks

Run all cells

Split screen

When to schedule the exam

Further Reading