---
title: AWS Certified AI Practitioner
description: Foundational AWS AI/ML certification covering generative AI fundamentals, Amazon Bedrock, SageMaker, responsible AI, and core ML concepts. The entry point into the AWS AI certification track.
url: https://www.factualminds.com/certifications/aws-ai-practitioner/
examCode: AIF-C01
level: foundational
publishDate: 2026-05-15
updateDate: 2026-05-15
---

# AWS Certified AI Practitioner

> The AIF-C01 is the foundational AI/ML certification AWS launched in late 2024. It validates that you understand generative AI, Bedrock, SageMaker fundamentals, and responsible-AI guardrails — without requiring deep ML engineering experience.

## Why AIF-C01 exists

AWS launched the AI Practitioner certification in late 2024 to fill a gap in the certification ladder. Solutions Architect tracks assumed candidates understood AI/ML as architects of larger systems; ML Specialty (now retiring as MLS-C01) was deep-end ML engineering. AIF-C01 covers the _literacy_ layer — what GenAI is, how Bedrock works, how to evaluate responsible-AI risks — without requiring hands-on training of models.

For consultants, sales engineers, product managers, and engineering managers shipping AI features on AWS, this is the certification that proves you can speak the language credibly.

## Recommended 3-week study plan

**Week 1 — Foundations (10 hours)**
Skim the AWS AI Practitioner exam guide. Run through AWS Skill Builder's _Standard Plan for AIF-C01_ learning plan (free). Watch the _Generative AI on AWS Foundations_ digital course. Read the _Amazon Bedrock User Guide_ introduction.

**Week 2 — Hands-on Bedrock + Q (8 hours)**
Spend a Saturday in the Bedrock console: try a Knowledge Base on a 100-document S3 bucket; try Bedrock Guardrails with a couple of content-filter categories; try the inline-agent feature. In the AWS console, enable Amazon Q Developer Free Tier and ask it 10 questions about your AWS environment.

**Week 3 — Practice exams + responsible AI (6 hours)**
Take the official AWS practice question set (free on Skill Builder). Take two Tutorial Dojo practice exams. Review every wrong answer and write a one-line explanation. Read the AWS _Responsible AI_ whitepaper.

## What this certification will NOT teach you

- Building production agents
- Operating SageMaker training pipelines
- Picking embedding dimensionality for your use case
- Cost-controlling per-tenant GenAI spend

For those, you need MLA-C01 (ML Engineer Associate) plus hands-on experience.

## Related FactualMinds Content

- [Amazon Bedrock Consulting](/services/aws-bedrock/)
- [Generative AI on AWS](/services/generative-ai-on-aws/)
- [Amazon Q for Developers Consulting](/services/amazon-q-for-developers/)

## Exam Details

- **Duration:** 90 minutes
- **Questions:** 65
- **Passing score:** 700 / 1000
- **Cost:** $100 USD
- **Format:** Multiple choice and multiple response
- **Validity:** 3 years
- **Recommended experience:** 6 months of exposure to AWS and basic AI/ML concepts — no hands-on ML engineering required

## Exam Domains

### Fundamentals of AI and ML (20%)

- Difference between AI, machine learning, deep learning, and generative AI
- Supervised, unsupervised, and reinforcement learning at a conceptual level
- Training vs inference; the role of datasets, features, and labels
- Common AI/ML use cases: classification, regression, recommendation, anomaly detection
- Model performance metrics: accuracy, precision, recall, F1, AUC at a high level

### Fundamentals of Generative AI (24%)

- What foundation models are; tokens, embeddings, context windows, temperature
- Prompt engineering basics: zero-shot, few-shot, chain-of-thought
- Retrieval-augmented generation (RAG) at a conceptual level
- Fine-tuning vs RAG vs prompt engineering trade-offs
- Multimodal models: text, image, video, audio inputs and outputs

### Applications of Foundation Models (28%)

- Amazon Bedrock: foundation model access, Knowledge Bases, Agents, Guardrails
- Amazon Q Business, Q Developer, Q in QuickSight, Q in Connect — when each fits
- Amazon Nova family — Micro, Lite, Pro, Premier, Canvas, Reel
- Amazon SageMaker JumpStart for pre-trained model deployment
- Embedding models and vector stores: S3 Vectors, OpenSearch, Aurora pgvector

### Guidelines for Responsible AI (14%)

- Bias, fairness, explainability — concepts and AWS service support
- Bedrock Guardrails: content filters, PII detection, contextual grounding, automated reasoning checks
- AWS AI Service Cards and model documentation
- Watermarking outputs: Nova Canvas and Reel invisible watermarks
- Data residency, training-data privacy, customer-content protections on Bedrock and Q

### Security, Compliance, and Governance for AI Solutions (14%)

- IAM for AI workloads — least-privilege model access, KMS encryption
- PrivateLink and VPC endpoints for Bedrock and SageMaker
- Compliance regimes that recognize Bedrock and SageMaker (HIPAA, SOC 2, ISO 27001)
- Audit trails: CloudTrail logging for Bedrock and Q
- AI/ML model risk management at an organizational level

---

*Source: https://www.factualminds.com/certifications/aws-ai-practitioner/*