AWS Certified AI Practitioner Course

AWS describes the AWS Certified AI Practitioner as a credential that validates foundational knowledge of AI, ML and generative AI concepts, use cases and AWS AI services. It is aimed at candidates who use AI and ML technologies on AWS without necessarily building them. It sits in the Foundational category alongside the AWS Certified Cloud Practitioner. AWS notes that the Cloud Practitioner exam outline contains a single AI task statement, while the AI Practitioner outline is given over entirely to AI, ML and generative AI. The AWS Certified AI Business Strategist covers business judgement for AI decisions and does not assess knowledge of AWS AI services. AWS lists the Solutions Architect Associate as the recommended next certification for cloud roles, and the Data Engineer Associate and Machine Learning Engineer Associate for data, AI and machine learning roles. The certification is valid for three years, and earning the Machine Learning Engineer Associate recertifies it automatically.

AWS sets no mandatory prerequisite for the AIF-C01 exam. It recommends up to six months of exposure to AI and ML technologies on AWS, and familiarity with:

• core AWS services such as Amazon EC2, Amazon S3, AWS Lambda, Amazon Bedrock and Amazon SageMaker AI
• the AWS shared responsibility model for security and compliance
• AWS Identity and Access Management (IAM)
• AWS service pricing models

No programming or statistics background is assumed, since AWS lists coding AI and ML models and the mathematical or statistical analysis of models as outside the exam.

AWS names foundational cloud training as the starting point for candidates new to IT or AWS, which Logitrain runs as the AWS Certified Cloud Practitioner course and the AWS Technical Essentials course. Candidates who already hold the AWS Certified Cloud Practitioner or an Associate AWS certification can start with this course.

Candidates can achieve this certification by passing the following exam.

AIF-C01, AWS Certified AI Practitioner Course, AWS Certified AI Practitioner

Book and attempt the exam within three months of finishing the course.

Attempt it at any Pearson VUE test centre, or online through Pearson VUE online proctoring from a private space with a camera, microphone and speaker.

Exam format: 65 questions, 50 scored and 15 unscored, across multiple choice, multiple response, ordering and matching question types, 90 minutes, with a passing score of 700 on a scale of 100 to 1,000.

Digital course material included, covering the AWS Certified AI Practitioner AIF-C01 exam and its five content domains.

• Covering AI, ML, deep learning, neural network, computer vision, natural language processing, large language model and agentic AI terminology
• Comparing batch, real-time, asynchronous and serverless inferencing
• Covering the data types used in AI models, including labelled, unlabelled, tabular, time-series, image and text data
• Comparing supervised, unsupervised and reinforcement learning
• Reviewing AI and ML use cases, and the situations where AI is not the right choice
• Covering regression, classification and clustering techniques
• Outlining the AI and ML pipeline, MLOps concepts, and model and business metrics
• Covering AWS managed AI services, including Amazon SageMaker AI, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex and Amazon Polly
• Explaining tokens, chunking, embeddings, vectors and transformer-based large language models
• Covering the foundation model lifecycle, token-based pricing and context engineering
• Introducing agentic AI concepts, including multi-agent patterns, memory, tool use and the Model Context Protocol
• Weighing the advantages, limitations and cost tradeoffs of generative AI on AWS
• Covering Amazon Bedrock, SageMaker JumpStart, Amazon Quick, Kiro, Strands Agents and Amazon Bedrock AgentCore
• Covering foundation model selection criteria and inference parameters such as temperature
• Explaining Retrieval Augmented Generation, Amazon Bedrock Knowledge Bases and vector database options on AWS
• Covering prompt engineering techniques, prompt risks and Amazon Bedrock Prompt Management
• Outlining pre-training, fine-tuning, continuous pre-training and distillation
• Covering foundation model evaluation with ROUGE, BLEU, BERTScore, LLM-as-a-judge and Amazon Bedrock Model Evaluation
• Covering responsible AI features, bias and variance, and Amazon Bedrock Guardrails
• Comparing transparent and explainable models, including Amazon SageMaker Model Cards
• Covering security for AI systems, including IAM, encryption, Amazon Macie, AWS PrivateLink and prompt injection
• Covering hallucination detection and grounding techniques
• Reviewing governance and compliance services, including AWS Config, Amazon Inspector, AWS Artifact, AWS CloudTrail and AWS Trusted Advisor
• Outlining data governance strategies and the Generative AI Security Scoping Matrix

• Business analysts
• Product managers and product owners
• Project and program managers
• IT support officers and system administrators
• Cloud engineers and cloud administrators
• Software developers new to AI on AWS
• Data analysts
• Pre-sales, sales and account managers in technology organisations
• Marketing, operations and other line-of-business staff
• Graduates and career changers moving into AI or cloud roles
• Holders of the AWS Certified Cloud Practitioner

This is a foundational certification. Machine learning engineers and data scientists who build and train models are better matched to the AWS Certified Machine Learning Engineer Associate.

• AI, ML, deep learning, generative AI and agentic AI compared
• Types of inferencing, types of data and types of learning
• Where AI and ML add value, and where they are not appropriate
• Regression, classification and clustering
• AWS managed AI and ML services
• Traditional ML models compared with foundation models
• Pipeline components and AWS services for each stage
• Sources of models and methods of using a model in production
• MLOps concepts
• Model performance metrics and business metrics
• Tokens, chunking, embeddings, vectors, multi-modal and diffusion models
• The foundation model lifecycle and token-based pricing
• Context engineering
• Agentic AI concepts, including the Model Context Protocol
• Advantages and disadvantages, including hallucinations and nondeterminism
• Model selection factors, business value and metrics
• Amazon Bedrock, Amazon SageMaker AI, SageMaker JumpStart, Amazon Quick, Kiro, Strands Agents and Amazon Bedrock AgentCore
• Cost tradeoffs of AWS generative AI services
• Model selection criteria and inference parameters
• Retrieval Augmented Generation and Amazon Bedrock Knowledge Bases
• Vector databases on AWS
• Cost tradeoffs of customisation approaches
• AI agents and their business applications
• Context, instructions and negative prompts
• Chain-of-thought, zero-shot, single-shot and few-shot prompting, and prompt templates
• Prompt engineering risks, including poisoning, hijacking and jailbreaking
• Amazon Bedrock Prompt Management
• Pre-training, fine-tuning, continuous pre-training and distillation
• Preparing data for fine-tuning, including RLHF
• Human-in-the-loop evaluation, benchmark datasets and Amazon Bedrock Model Evaluation
• ROUGE, BLEU, BERTScore and LLM-as-a-judge
• Evaluating RAG, agents and workflows against business objectives
• Bias, fairness, inclusivity, safety and veracity
• Amazon Bedrock Guardrails
• Legal risks of generative AI
• Dataset characteristics, bias and variance
• Amazon SageMaker Model Cards and Amazon Bedrock Model Evaluations
• Tradeoffs between safety and transparency
• Human-centred design for explainable AI
• IAM, encryption, Amazon Macie, AWS PrivateLink and the shared responsibility model
• Amazon Bedrock AgentCore Identity and Amazon Bedrock Guardrails
• Data lineage, data cataloguing and secure data engineering
• Prompt injection, data leakage prevention and output filtering
• Hallucination detection and grounding techniques
• AWS Config, Amazon Inspector, AWS Artifact, AWS CloudTrail and AWS Trusted Advisor
• Data governance strategies
• Governance protocols and the Generative AI Security Scoping Matrix

• AWS Certified Cloud Practitioner course: the other Foundational AWS certification, covering cloud concepts, AWS services, security and pricing.
• AWS Technical Essentials course: an introduction to core AWS services for candidates new to AWS.
• AWS Solutions Architect Associate course: the associate certification AWS recommends after this one for cloud roles.
• AWS Security Essentials course: AWS security fundamentals, related to the security domain of this exam.
• Prompt engineering course: prompt design for large language models and generative AI.
• AI Foundation course: the vendor-neutral BCS Foundation Certificate in Artificial Intelligence.
• AI Essentials course: the BCS Essentials Certificate in Artificial Intelligence, focused on generative AI.
• AWS courses: every AWS course Logitrain runs.
• AI and machine learning courses: the full Logitrain artificial intelligence category.

Got a team to train? Logitrain delivers the AWS Certified AI Practitioner Course in house at the client site anywhere in Australia, with a skilled trainer, a mock test and the official AWS AIF-C01 exam, as the public courses do. Digital course material included. It suits teams who want a group trained together on dates that suit the business. Send the number of participants, the preferred start date and the delivery location through the in-house enquiry form, and Logitrain will come back with a quote.

What is the AWS Certified AI Practitioner?

It is a foundational AWS certification that validates knowledge of artificial intelligence, machine learning, generative AI and AWS AI services. AWS designed it for people who use AI and ML technologies on AWS without necessarily building them.

Is the AIF-C01 exam included in the course fee?

Yes. The course fee includes the official AWS AIF-C01 exam, which you can book and attempt within three months of finishing the course.

Are there prerequisites for the AWS Certified AI Practitioner course?

AWS sets no mandatory prerequisite. It recommends up to six months of exposure to AI and ML on AWS, and familiarity with core AWS services, the shared responsibility model, IAM and AWS pricing models. No programming background is assumed.

What is the current AWS Certified AI Practitioner exam version?

The current exam is AIF-C01 which includes agentic AI, the Model Context Protocol, Kiro, Strands Agents and Amazon Bedrock AgentCore.

How many questions are on the AWS AI Practitioner exam?

The exam has 65 questions in 90 minutes, of which 50 are scored and 15 are unscored. Questions are multiple choice, multiple response, ordering or matching, and the passing score is 700 on a scale of 100 to 1,000.

How long is the AWS Certified AI Practitioner course?

The course runs 3 days or 3 weeks. Full-time classes run on weekdays from 9:30 am to 5 pm, and part-time classes run on weeknights from 6 pm to 9 pm or on weekends from 10 am to 5 pm.

Can I take the AIF-C01 exam online?

Yes. You can attempt it at any Pearson VUE test centre, or online through Pearson VUE online proctoring from a private space with a camera, microphone and speaker.

Should I do the AWS Cloud Practitioner or the AI Practitioner first?

If you are new to IT or AWS, AWS recommends starting with foundational cloud training such as the Cloud Practitioner. If you already hold the AWS Certified Cloud Practitioner or an Associate AWS certification, you can start with the AI Practitioner. Both are Foundational certifications, but the Cloud Practitioner outline has one AI task statement while the AI Practitioner outline is given over entirely to AI, ML and generative AI.

What is the difference between the AWS AI Practitioner and the AI Business Strategist?

The AWS Certified AI Practitioner covers AI, ML and generative AI concepts and AWS AI services. The AWS Certified AI Business Strategist covers business judgement for AI decisions, such as investment choices, business cases and governance, and does not assess knowledge of AWS AI services.

Does the AWS Certified AI Practitioner course include coding?

No. AWS lists developing or coding AI and ML models as out of scope for the AIF-C01 exam, and the course outline follows the exam guide.

How long is the AWS Certified AI Practitioner valid?

The certification is valid for three years. You can recertify by passing the latest version of the exam, or by earning the AWS Certified Machine Learning Engineer Associate, which recertifies it automatically.

Which AWS certification comes after the AI Practitioner?

AWS recommends the Solutions Architect Associate for cloud roles, and the Data Engineer Associate or Machine Learning Engineer Associate for data, AI and machine learning roles.