AWS Certified AI Practitioner (AIF-C01)

The AWS Certified AI Practitioner certification is designed to validate foundational knowledge in artificial intelligence (AI), machine learning (ML), and generative AI technologies, along with their practical applications using AWS services. This certification helps professionals gain a competitive edge, positioning them for potential career advancement and increased earning potential. The AIF-C01 exam is ideal for individuals who can demonstrate a broad understanding of AI/ML and generative AI concepts and tools available on AWS, regardless of their specific job role. The exam assesses a candidate’s ability to:

Comprehend general and AWS-specific AI, ML, and generative AI methodologies, concepts, and strategies
Identify appropriate AI/ML and generative AI solutions to address relevant business questions
Match the right AI/ML technologies to specific use cases
Apply AI/ML and generative AI solutions ethically and responsibly

– Target Audience

This certification is intended for individuals with approximately six months of experience using AWS AI/ML services. Candidates are expected to interact with or use AI/ML solutions on AWS, though they may not necessarily design or develop those solutions themselves.

– Recommended AWS Knowledge

To be well-prepared for the exam, candidates should have a basic understanding of the following AWS topics:

Core AWS services such as Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker, along with their typical use cases
The AWS Shared Responsibility Model regarding security and compliance
AWS Identity and Access Management (IAM) for access control and resource security
The structure of the AWS Global Infrastructure, including Regions, Availability Zones, and edge locations
Familiarity with AWS pricing models and how service costs are determined

Exam Details

The AWS Certified AI Practitioner certification is categorized as a foundational-level credential, designed for individuals who have a general understanding of AI/ML technologies on AWS but are not necessarily responsible for building the solutions themselves. The exam is suitable for professionals in a variety of non-technical and technical roles, such as business analysts, IT support staff, marketing professionals, product or project managers, line-of-business or IT managers, and sales professionals.

The exam format consists of 65 questions, and candidates are given 90 minutes to complete it. It is available through both Pearson VUE testing centers and online proctoring options for convenience. The certification exam is offered in multiple languages, including English, Japanese, Korean, Portuguese (Brazil), and Simplified Chinese. Exam results are reported on a scaled score ranging from 100 to 1,000, with a minimum passing score of 700 required to earn the certification.

Course Outline

The exam covers the following topics:

1. Overview of AI and ML (20%)

Task Statement 1.1: Explaining basic AI concepts and terminologies.

Objectives:

Defining basic AI terms (for example, AI, ML, deep learning, neural networks, computer vision, natural language processing [NLP], model, algorithm, training and inferencing, bias, fairness, fit, large language model [LLM]).
Describing the similarities and differences between AI, ML, and deep learning.
Explaining various types of inferencing (for example, batch, real-time).
Describing the different types of data in AI models (for example, labeled and unlabeled, tabular, time-series, image, text, structured and unstructured).
Describing supervised learning, unsupervised learning, and reinforcement learning.

Task Statement 1.2: Identifying practical use cases for AI.

Objectives:

Recognizing applications where AI/ML can provide value (for example, assist human decision making, solution scalability, automation).
Determining when AI/ML solutions are not appropriate (for example, costbenefit analyses, situations when a specific outcome is needed instead of a prediction).
Selecting the appropriate ML techniques for specific use cases (for example, regression, classification, clustering).
Identifying examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting).
Explaining the capabilities of AWS managed AI/ML services (for example, SageMaker, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).

Task Statement 1.3: Describing the ML development lifecycle.

Objectives:

Describing components of an ML pipeline (for example, data collection, exploratory data analysis [EDA], data pre-processing, feature engineering, model training, hyperparameter tuning, evaluation, deployment, monitoring).
Understanding sources of ML models (for example, open source pre-trained models, training custom models).
Describing methods to use a model in production (for example, managed API service, self-hosted API).
Identifying relevant AWS services and features for each stage of an ML pipeline (for example, SageMaker, Amazon SageMaker Data Wrangler, Amazon SageMaker Feature Store, Amazon SageMaker Model Monitor).
Understanding fundamental concepts of ML operations (MLOps) (for example, experimentation, repeatable processes, scalable systems, managing technical debt, achieving production readiness, model monitoring, model re-training).
Understanding model performance metrics (for example, accuracy, Area Under the ROC Curve [AUC], F1 score) and business metrics (for example, cost per user, development costs, customer feedback, return on investment [ROI]) to evaluate ML models.

2. Understand the Fundamentals of Generative AI (24%)

Task Statement 2.1: Explaining the basic concepts of generative AI.

Objectives:

Understanding foundational generative AI concepts (for example, tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models, multi-modal models, diffusion models).
Identifying potential use cases for generative AI models (for example, image, video, and audio generation; summarization; chatbots; translation; code generation; customer service agents; search; recommendation engines).
Describing the foundation model lifecycle (for example, data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback).

Task Statement 2.2: Understanding the capabilities and limitations of generative AI for solving business problems.

Objectives:

Describing the advantages of generative AI (for example, adaptability, responsiveness, simplicity).
Identifying disadvantages of generative AI solutions (for example, hallucinations, interpretability, inaccuracy, nondeterminism).
Understanding various factors to select appropriate generative AI models (for example, model types, performance requirements, capabilities, constraints, compliance).
Determining business value and metrics for generative AI applications (for example, cross-domain performance, efficiency, conversion rate, average revenue per user, accuracy, customer lifetime value).

Task Statement 2.3: Describing AWS infrastructure and technologies for building generative AI applications.

Objectives:

Identifying AWS services and features to develop generative AI applications (for example, Amazon SageMaker JumpStart; Amazon Bedrock; PartyRock, an Amazon Bedrock Playground; Amazon Q).
Describing the advantages of using AWS generative AI services to build applications (for example, accessibility, lower barrier to entry, efficiency, cost-effectiveness, speed to market, ability to meet business objectives).
Understanding the benefits of AWS infrastructure for generative AI applications (for example, security, compliance, responsibility, safety).
Understanding cost tradeoffs of AWS generative AI services (for example, responsiveness, availability, redundancy, performance, regional coverage, token-based pricing, provision throughput, custom models).

3. Learn About the Applications of Foundation Models (28%)

Task Statement 3.1: Describing design considerations for applications that use foundation models.

Objectives:

Identifying selection criteria to choose pre-trained models (for example, cost, modality, latency, multi-lingual, model size, model complexity, customization, input/output length).
Understanding the effect of inference parameters on model responses (for example, temperature, input/output length).
Defining Retrieval Augmented Generation (RAG) and describe its business applications (for example, Amazon Bedrock, knowledge base).
Identifying AWS services that help store embeddings within vector databases (for example, Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon DocumentDB [with MongoDB compatibility], Amazon RDS for PostgreSQL).
Explain the cost tradeoffs of various approaches to foundation model customization (for example, pre-training, fine-tuning, in-context learning, RAG).
Understand the role of agents in multi-step tasks (for example, Agents for Amazon Bedrock).

Task Statement 3.2: Choosing effective prompt engineering techniques.

Objectives:

Describing the concepts and constructs of prompt engineering (for example, context, instruction, negative prompts, model latent space).
Understanding techniques for prompt engineering (for example, chain-ofthought, zero-shot, single-shot, few-shot, prompt templates).
Understanding the benefits and best practices for prompt engineering (for example, response quality improvement, experimentation, guardrails, discovery, specificity and concision, using multiple comments).
Defining potential risks and limitations of prompt engineering (for example, exposure, poisoning, hijacking, jailbreaking).

Task Statement 3.3: Describing the training and fine-tuning process for foundation models.

Objectives:

Describing the key elements of training a foundation model (for example, pre-training, fine-tuning, continuous pre-training).
Defining methods for fine-tuning a foundation model (for example, instruction tuning, adapting models for specific domains, transfer learning, continuous pre-training).
Describing how to prepare data to fine-tune a foundation model (for example, data curation, governance, size, labeling, representativeness, reinforcement learning from human feedback [RLHF]).

Task Statement 3.4: Describing methods to evaluate foundation model performance.

Objectives:

Understanding approaches to evaluate foundation model performance (for example, human evaluation, benchmark datasets).
Identifying relevant metrics to assess foundation model performance (for example, Recall-Oriented Understudy for Gisting Evaluation [ROUGE], Bilingual Evaluation Understudy [BLEU], BERTScore).
Determining whether a foundation model effectively meets business objectives (for example, productivity, user engagement, task engineering).

4. Understand the Guidelines for Responsible AI (14%)

Task Statement 4.1: Explaining the development of AI systems that are responsible.

Objectives:

Identifying features of responsible AI (for example, bias, fairness, inclusivity, robustness, safety, veracity).
Understanding how to use tools to identify features of responsible AI (for example, Guardrails for Amazon Bedrock).
Understanding responsible practices to select a model (for example, environmental considerations, sustainability).
Identifying legal risks of working with generative AI (for example, intellectual property infringement claims, biased model outputs, loss of customer trust, end user risk, hallucinations).
Identifying characteristics of datasets (for example, inclusivity, diversity, curated data sources, balanced datasets).
Understanding effects of bias and variance (for example, effects on demographic groups, inaccuracy, overfitting, underfitting).
Describing tools to detect and monitor bias, trustworthiness, and truthfulness (for example, analyzing label quality, human audits, subgroup analysis, Amazon SageMaker Clarify, SageMaker Model Monitor, Amazon Augmented AI [Amazon A2I]).

Task Statement 4.2: Recognizing the importance of transparent and explainable models.

Objectives:

Understanding the differences between models that are transparent and explainable and models that are not transparent and explainable.
Understanding the tools to identify transparent and explainable models (for example, Amazon SageMaker Model Cards, open source models, data, licensing).
Identifying tradeoffs between model safety and transparency (for example, measure interpretability and performance).
Understanding principles of human-centered design for explainable AI.

5. Learn About Security, Compliance, and Governance for AI Solutions (14%)

Task Statement 5.1: Explaining methods to secure AI systems.

Objectives:

Identifying AWS services and features to secure AI systems (for example, IAM roles, policies, and permissions; encryption; Amazon Macie; AWS PrivateLink; AWS shared responsibility model).
Understanding the concept of source citation and documenting data origins (for example, data lineage, data cataloging, SageMaker Model Cards).
Describing best practices for secure data engineering (for example, assessing data quality, implementing privacy-enhancing technologies, data access control, data integrity).
Understanding security and privacy considerations for AI systems (for example, application security, threat detection, vulnerability management,
infrastructure protection, prompt injection, encryption at rest and in transit).

Task Statement 5.2: Recognizing governance and compliance regulations for AI systems.

Objectives:

Identifying regulatory compliance standards for AI systems (for example, International Organization for Standardization [ISO], System and Organization Controls [SOC], algorithm accountability laws).
Identifying AWS services and features to assist with governance and regulation compliance (for example, AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, AWS Trusted Advisor).
Describing data governance strategies (for example, data lifecycles, logging, residency, monitoring, observation, retention).
Describing processes to follow governance protocols (for example, policies, review cadence, review strategies, governance frameworks such as the Generative AI Security Scoping Matrix, transparency standards, team training requirements).

AWS Certified AI Practitioner Exam FAQs

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AWS Certification Exam Policy

Amazon Web Services (AWS) outlines a comprehensive set of policies governing its certification exams to ensure fairness, consistency, and transparency throughout the certification process. These policies cover key aspects such as exam retakes, scoring methodology, and eligibility criteria.

– Retake Policy

Candidates who do not pass an AWS certification exam must wait 14 calendar days before attempting the exam again. There is no restriction on the number of retakes; however, the full exam fee must be paid for each attempt. Once a candidate passes an exam, they are not permitted to retake the same version of that exam for a period of two years. If AWS releases an updated version of the exam—distinguished by a new exam guide and series code—candidates are allowed to take the revised exam.

– Scoring and Results

The AWS Certified AI Practitioner (AIF-C01) exam is scored on a pass/fail basis, determined by a minimum standard established by AWS certification experts in alignment with industry best practices. Candidates receive a scaled score between 100 and 1,000, with a minimum passing score of 700. This scoring method accounts for variations in difficulty across different versions of the exam, ensuring consistency and fairness in the evaluation process. The final score reflects overall performance and indicates whether the candidate has met the certification requirements.

AWS Certified AI Practitioner Exam Study Guide

Step 1: Understand the Exam Objectives

Begin your preparation by thoroughly reviewing the official exam guide provided by AWS. This document outlines the core domains, topics, and knowledge areas you will be assessed on. Understanding these objectives is essential, as they serve as a blueprint for your study plan. Pay attention to the weightage of each domain so you can prioritize topics accordingly. Familiarize yourself with key concepts such as AI fundamentals, machine learning workflows, generative AI use cases, and the appropriate application of AWS services like Amazon SageMaker and AWS Lambda in AI/ML solutions.

Step 2: Use Official AWS Training Resources

AWS provides a wide range of official training materials tailored for the AI Practitioner exam. These include foundational courses available through AWS Skill Builder, which are designed to help you build conceptual understanding and learn about AWS services in the context of AI and ML. Start with recommended learning paths specific to AI and generative AI. These courses are created by AWS experts and align directly with exam content, making them a reliable resource for structured learning.

Step 3: Enroll in Digital Courses to Fill Knowledge Gaps

Once you’ve assessed your strengths and weaknesses based on the exam guide and official training, consider enrolling in additional digital courses to bridge any knowledge gaps. Look for in-depth tutorials and hands-on modules that focus on specific services or concepts you find challenging. Many e-learning platforms also offer AI/ML-focused courses that include real-world examples and guided labs, helping you gain practical experience beyond theory.

Step 4: Get Hands-On Practice with AWS Labs and Simulations

Practical experience is crucial for mastering AI/ML services on AWS. Engage with AWS Builder Labs, which provide step-by-step, interactive labs to explore real-world scenarios. Complement this with AWS Cloud Quest, a gamified learning experience where you can complete AI/ML-related challenges in a virtual environment. Additionally, AWS Jam events offer scenario-based team challenges that test your problem-solving and critical-thinking skills using AWS tools in a time-sensitive setting. These resources help reinforce your knowledge through application, not just memorization.

Step 5: Join Study Groups and Online Communities

Learning alongside others can enhance your understanding and motivation. Consider joining AWS certification-focused study groups on platforms like LinkedIn, Reddit, or Discord. These communities are valuable for sharing resources, asking questions, discussing complex topics, and staying updated with new information. Collaborating with peers who are also preparing for the exam provides a supportive environment and different perspectives on key topics.

Step 6: Take Practice Exams to Assess Readiness

Regularly test your progress by taking practice exams that mirror the format and difficulty of the actual AIF-C01 exam. These mock tests help you become familiar with the question style and time constraints, allowing you to identify areas that need improvement. Analyze your results to adjust your study plan and revisit concepts you didn’t fully understand. Use trusted sources for practice exams that align with the latest version of the certification.