AIP-C01: Who is this exam for?

Engineers who BUILD generative-AI applications on AWS - RAG systems, agents, and production GenAI - and want to prove they can do it.

AIP-C01: What does it cover?

The deep end: agent orchestration, retrieval and RAG, vector stores, Amazon Bedrock, guardrails, and the production trade-offs (latency, cost, governance) that only show up when you ship.

AIP-C01: How hard is it?

Professional level. It assumes you build, not just describe. Come in with real hands-on generative-AI experience.

AIP-C01: Should I do AIF-C01 first?

If you are newer to AWS AI, yes - AIF builds the vocabulary that AIP takes for granted.

AIF-C01: Who is this exam for?

Anyone who USES AI on AWS rather than builds it - product, sales, ops, analysts, or people new to cloud who need to talk about AI and generative AI with confidence. No coding required.

AIF-C01: What does it cover?

The fundamentals: what AI, ML, and generative AI actually are, foundation models and prompting, responsible AI, and the main AWS AI services (Amazon Bedrock, SageMaker) at a high level.

AIF-C01: How hard is it?

It is the entry-level AWS AI certification - about 50 questions in 90 minutes, no hands-on building. Most people are ready after a few weeks of study.

AIF-C01: Should I take AIF-C01 or AIP-C01 first?

Start here. AIF is the foundation that the professional-level AIP exam assumes you already know.

AI Certification Practice

Pass your AI certification by solving real problems, not memorizing flashcards.

Every question is a production scenario with a full worked solution - why the right answer wins, why the rest fail, and the principle to keep.

Sample question - AIP-C01

A retail chatbot built on Amazon Bedrock gives confident, incorrect answers about return policies that aren't in its knowledge base. What's the most likely root cause?

The RAG pipeline isn't retrieving the return-policy documents

Lower the model's temperature parameter

Reduces response randomness - doesn't fix missing grounding.

Increase the foundation model's parameter count

Principle: When answers are confidently wrong, suspect the retrieval step before the model settings.

A company wants generative AI to summarize incoming support tickets. They have no data science team and don't want to manage any infrastructure. Which approach best fits?

Train a new foundation model from scratch on their support data

Call a pretrained foundation model through Amazon Bedrock

Provision and manage their own GPU fleet to self-host an open-source model

Still requires managing the infrastructure they explicitly want to avoid.

Principle: Reach for a managed foundation model before you build or host your own.

A retail chatbot built on Amazon Bedrock gives confident, incorrect answers about return policies that aren't in its knowledge base. What's the most likely root cause?

The RAG pipeline isn't retrieving the return-policy documents

Lower the model's temperature parameter

Reduces response randomness - doesn't fix missing grounding.

Increase the foundation model's parameter count

Principle: When answers are confidently wrong, suspect the retrieval step before the model settings.

2: AWS AI exams
2,000+: practice problems
Free: first set in each exam
100%: with worked solutions

Choose your exam

Cloud AI certifications, each a self-contained set of deep, worked problems. The first set in each is free.

AIP-C01 · Professional

AWS Certified Generative AI Developer - Professional

Best for engineers building and shipping generative-AI apps on AWS.

Start practicing

AIF-C01 · Foundational

AWS Certified AI Practitioner

Best for non-builders who need to understand and talk about AI on AWS.

Start practicing

New to AI? Start foundational. Already building AI systems? Go professional.

Solve first, learn deeper

Most courses hand you theory, then quiz it. We start with the problem - you work a real-world scenario before you're told the answer, then see the full reasoning behind it.

Drill first.

Start with a real problem, not a lecture. Attempt it cold - that's where the learning starts.

See the full reasoning.

Every solution explains the win, the traps, and the trade-offs - with a diagram where the system matters.

Keep the principle.

Each problem ends with the transferable idea, so you're building field understanding, not memorizing an answer key.

Flashcards test what you already know. Problems build what you don't.

What makes a problem here different

A realistic production scenario, not a trivia stem
A worked solution explaining why each wrong answer is wrong
Diagrams for the system relationships that matter
Links to the official documentation
The transferable principle, so it teaches the field - not just the exam

Solve first, learn deeper

Most courses hand you theory, then quiz it. We start with the problem - you work a real-world scenario before you're told the answer, then see the full reasoning behind it.

Drill first.

Start with a real problem, not a lecture. Attempt it cold - that's where the learning starts.

See the full reasoning.

Every solution explains the win, the traps, and the trade-offs - with a diagram where the system matters.

Keep the principle.

Each problem ends with the transferable idea, so you're building field understanding, not memorizing an answer key.

Flashcards test what you already know. Problems build what you don't.

MyAICerts

Pass your AI certification by solving real problems, not memorizing flashcards.

Choose your exam

AWS Certified Generative AI Developer - Professional

AWS Certified AI Practitioner

Solve first, learn deeper

Drill first.

See the full reasoning.

Keep the principle.

What makes a problem here different

MyAICerts

Pass your AI certification by solving real problems, not memorizing flashcards.

Choose your exam

AWS Certified Generative AI Developer - Professional

AWS Certified AI Practitioner

Solve first, learn deeper

Drill first.

See the full reasoning.

Keep the principle.

What makes a problem here different