Get AWS Generative AI Certified in 2025

AWS Generative AI Certifications: Complete 2025 Guide

AWS generative AI certifications has gotten complicated with all the new credentials, retirement announcements, and overlapping study paths flying around. As someone who spent the last year knee-deep in Bedrock APIs and SageMaker notebooks, I learned everything there is to know about these certs. Today, I will share it all with you.

Here’s the deal. AWS rolled out two big generative AI credentials: the AWS Certified AI Practitioner (AIF-C01) aimed at folks who work around AI but don’t necessarily write the code, and the AWS Certified Generative AI Developer — Professional (AIP-C01) for those of us who actually build things with foundation models. And with the Machine Learning Specialty exam heading for retirement in 2026, these two are basically the future if you want AI creds on AWS.

I took the AI Practitioner exam first. Honestly? It was more straightforward than I expected, but don’t let that fool you into skipping the prep.

AWS Certified AI Practitioner (AIF-C01)

Probably should have led with this section, honestly. The AI Practitioner cert is your entry point. It’s built for product managers, business analysts, and anyone who needs to speak the language of AI without necessarily writing Python all day. If your job involves making decisions about AI tools or evaluating what’s possible with generative AI on AWS, this is your cert.

Exam Details

What the Exam Actually Covers

Let me break down what you’ll face on test day. The exam domains break down like this:

• Domain 1: Fundamentals of AI and ML (20%) — This is the bread and butter stuff. Core ML concepts, supervised vs unsupervised learning, neural network basics. If you’ve taken any intro ML course, you’re halfway there.
• Domain 2: Fundamentals of Generative AI (24%) — Foundation models, transformer architecture, and prompt engineering basics. They love asking about the difference between fine-tuning and prompt engineering here.
• Domain 3: Applications of Foundation Models (28%) — The biggest chunk. Amazon Bedrock dominates this section. Model selection criteria, RAG architectures, and when to use which model. I saw a lot of scenario-based questions in this domain.
• Domain 4: Guidelines for Responsible AI (14%) — Bias detection, fairness metrics, transparency. Don’t sleep on this one. I nearly did and regretted it.
• Domain 5: Security, Compliance, and Governance (14%) — Data privacy, model access controls, encryption at rest and in transit. Standard AWS security stuff but applied to AI workloads.

AWS Certified Generative AI Developer — Professional (AIP-C01)

Now this is where things get real. The professional-level cert is a different beast entirely. Three and a half hours in a testing center. Eighty-five questions. And they expect you to actually know how to build production GenAI apps, not just talk about them.

I won’t sugarcoat it — I studied for about 10 weeks before I felt comfortable scheduling this one.

Exam Details

Exam Domains

Here’s the breakdown and my honest take on each section:

• Domain 1: Foundation Model Integration, Data Management, and Compliance (31%) — The heavyweight domain. Nearly a third of the exam. They want to see that you know how to pick the right foundation model, prepare your data properly, fine-tune when needed, and keep everything compliant. I spent the most study time here.
• Domain 2: Implementation and Integration (26%) — Building actual applications with Bedrock, wiring up LangChain, creating agents with tool use, and hooking things together via APIs. This is where hands-on experience really pays off.
• Domain 3: AI Safety, Security, and Governance (20%) — Guardrails configuration, content filtering, IAM policies for model access. They go deep on Bedrock Guardrails specifically.
• Domain 4: Operational Efficiency and Optimization (12%) — Cost optimization, latency tuning, caching strategies. Smaller domain but the questions can be tricky.
• Domain 5: Testing, Validation, and Troubleshooting (11%) — Evaluation metrics like BLEU and ROUGE, debugging model outputs, setting up monitoring. Straightforward if you’ve actually deployed a model to production.

Key AWS Services to Master

Both certs revolve around a handful of core services. I’m going to walk through each one because understanding them deeply made the difference for me on exam day.

Amazon Bedrock

This is the big one. Bedrock gives you access to foundation models from Anthropic (Claude), Meta (Llama), Amazon (Titan), and several others through a single unified API. I remember the first time I got a Claude response back through Bedrock — it felt like magic compared to managing my own model endpoints. No infrastructure to manage. You just call the API and get results.

Here’s a quick example of invoking Claude through Bedrock:

import boto3
import json

# Initialize Bedrock runtime client
bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')

# Invoke Claude 3 Sonnet
response = bedrock.invoke_model(
    modelId='anthropic.claude-3-sonnet-20240229-v1:0',
    body=json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 1024,
        "messages": [{
            "role": "user",
            "content": "Explain RAG architecture in 3 sentences."
        }]
    })
)

result = json.loads(response['body'].read())
print(result['content'][0]['text'])

Amazon SageMaker

When Bedrock isn’t enough and you need to bring your own model or fine-tune something specific, SageMaker is where you go. I’ve used SageMaker JumpStart more times than I can count — it’s basically a catalog of pre-trained models you can deploy with a few clicks and then customize to your heart’s content. The exam loves asking about when to use SageMaker vs Bedrock, so make sure you understand the trade-offs.

Amazon Kendra

Kendra is the enterprise search service that keeps showing up in RAG architecture questions. You feed it your company documents, it indexes everything, and then your LLM can pull relevant context before generating a response. I built a RAG pipeline with Kendra at work last year and it dramatically improved our chatbot’s accuracy on internal policy questions. Night and day difference.

AWS Lambda

Lambda is the glue that holds GenAI applications together. Serverless compute for your API backends, event-driven processing, and all the stuff in between. Pretty much every GenAI architecture diagram you’ll see on the exam has Lambda somewhere in it.

Building a RAG Application: Code Example

RAG is probably the single most important pattern you need to understand for either cert. I’ve built maybe a dozen of these at this point, and they all follow roughly the same shape. You take your documents, turn them into vector embeddings, store them in a vector database, and then at query time you search for relevant chunks to feed into your LLM prompt as context.

Here’s a simplified version of what that looks like with LangChain and Bedrock:

import boto3
from langchain.embeddings import BedrockEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import Bedrock

# Initialize embeddings with Amazon Titan
embeddings = BedrockEmbeddings(
    model_id="amazon.titan-embed-text-v1",
    client=boto3.client('bedrock-runtime')
)

# Create vector store from documents
documents = ["AWS Bedrock supports multiple FMs...",
             "Claude 3 excels at reasoning tasks..."]
vectorstore = FAISS.from_texts(documents, embeddings)

# Query with semantic search
query = "What models does Bedrock support?"
relevant_docs = vectorstore.similarity_search(query, k=3)

# Generate response with context
llm = Bedrock(model_id="anthropic.claude-3-haiku-20240307-v1:0")
context = "\n".join([doc.page_content for doc in relevant_docs])

prompt = f"""Based on this context:
{context}

Answer: {query}"""

response = llm.invoke(prompt)
print(response)

That’s the skeleton. In production you’d add error handling, chunk your documents properly, maybe use OpenSearch Serverless instead of FAISS for persistence, and set up proper IAM roles. But conceptually this is what the exam wants you to understand.

My 8-Week Study Plan (What Actually Worked)

Responsible AI: The Stuff You Can’t Skip

Both exams hit responsible AI pretty hard. I know it might feel like the “soft” part of the curriculum, but trust me, the questions get surprisingly specific. Here’s what you need to nail down:

• Bias and Fairness: Know how to spot bias in training data and model outputs. They’ll give you scenarios and ask what went wrong. Real-world stuff like demographic imbalance in datasets.
• Transparency: Model cards, explainability tools, documentation. AWS has specific guidelines here and the exam tests whether you know them.
• Guardrails: Amazon Bedrock Guardrails is a service you absolutely must understand. Content filtering, topic avoidance, word filters — learn how to configure all of it.
• Privacy: Data handling policies, PII detection with Comprehend, compliance with GDPR and other regulations. Standard but important.
• Human Oversight: When do you need a human in the loop? The exam presents scenarios where automated decisions need human review. Know the thresholds.

Cost Optimization Tips

That’s what makes these GenAI certifications endearing to us cloud professionals — they don’t just test whether you can build something, they test whether you can build it without burning through your budget. The professional exam especially digs into cost optimization, and honestly, this knowledge pays for itself on the job.

One thing I learned the hard way: always start with the smallest model that can handle your use case. I once ran a classification task through Claude 3 Opus when Haiku would have worked perfectly fine. My AWS bill was not happy with me that month.

Official Resources

These are the links I bookmarked and kept coming back to throughout my study journey:

• AWS Certified AI Practitioner Exam Page
• AWS Certified Generative AI Developer Professional
• AWS Skill Builder: Generative AI Learning Plan
• Amazon Bedrock Documentation

Pro Tip from someone who’s been there: The GenAI Developer Professional exam is heavily hands-on. Reading whitepapers alone won’t cut it. Build at least 2-3 complete applications using Bedrock before you schedule your exam date. I’d specifically recommend building a RAG chatbot and an agent that uses tool calling — those two projects alone covered probably 40% of the questions I saw.