AWS Certified AI Practitioner (AIF-C01): Official Exam Format, Cost, and What Changed in the v1.1 Update

What AIF-C01 Certification Actually Certifies

AWS positions this squarely as a AIF-C01 exam foundational, business-facing credential rather than a hands-on technical one. AWS's own framing is direct about who it's for: the exam is designed for individuals who want to demonstrate a foundational understanding of AI concepts and AWS AI tools, focused on practical business applications of AI rather than the mechanics of building or deploying models.

What makes this worth spelling out clearly is what AWS explicitly rules out. The exam guide lists job tasks that are out of scope for the target candidate, and the list is specific: developing or coding AI/ML models or algorithms, implementing data engineering or feature engineering techniques, performing hyperparameter tuning or model optimization, building and deploying AI/ML pipelines or infrastructure, and conducting mathematical or statistical analysis of AI/ML models. If you're expecting a technical, code-heavy exam, this isn't it — that role belongs to AWS's separate Machine Learning Engineer – Associate certification instead.

Who AIF-C01 Exam Is For (and Who It's Not For)

AWS describes the target candidate as someone with up to 6 months of exposure to AI/ML technologies on AWS, who uses but does not necessarily build AI/ML solutions on AWS. That's a deliberately broad net — the certification page explicitly lists candidate role examples spanning cloud, development, data, IT, AI/ML, or line-of-business roles, not just engineers.

It's a poor starting point if you have zero AWS Cloud background at all — AWS's own FAQ recommends starting with AWS Cloud Practitioner Essentials or AWS Technical Essentials first if you're new to AWS entirely, since this exam assumes baseline familiarity with core services like Amazon EC2, Amazon S3, and IAM even though it isn't testing them directly. Candidates who already hold AWS Certified Cloud Practitioner or any Associate-level AWS certification can skip those foundational cloud prerequisites and go straight into AI-specific prep.

One distinction AWS itself flags directly, since candidates commonly confuse the two: this certification is different from the AWS Certified AI Business Strategist certification. AI Practitioner validates technical/conceptual knowledge of AWS AI services; AI Business Strategist validates business judgment — which AI investments to pursue, building the business case, governance — and does not test AWS AI service knowledge at all. They're complementary, not overlapping, credentials.

Exam Format and Structure

The exam runs 90 minutes and includes 65 total questions — 50 that count toward your score and 15 unscored questions AWS uses to evaluate future exam content, mixed in and indistinguishable from the scored ones. Unanswered questions are scored as incorrect, and there is no penalty for guessing, so leaving anything blank is a pure loss.

Beyond standard multiple choice (one correct answer, three distractors) and multiple response (select all that apply from five or more options), the exam also includes ordering questions (arranging 3–5 responses into correct sequence) and matching questions (pairing responses against 3–7 prompts). These formats reward genuinely understanding a process's sequence or a concept's correct pairing, not just recognizing a plausible-sounding term.

Domains and Topics Covered

AWS's official, current exam guide (version 1.1, published April 30, 2026) specifies five weighted content domains:

Applications of Foundation Models carries the single largest weight, and its scope reflects how fast this exam has evolved to track the current generative AI landscape: prompt engineering techniques (chain-of-thought, zero-shot, few-shot), Retrieval Augmented Generation (RAG) and vector database services like Amazon OpenSearch Service and Amazon Aurora, fine-tuning approaches, and FM evaluation metrics including ROUGE, BLEU, BERTScore, and LLM-as-a-judge.

The v1.1 update (published just months before this writing) added meaningful new content that wasn't in the original v1.0 guide: token-based pricing models, context engineering, and — notably — foundational agentic AI concepts, including multi-agent system patterns, the Model Context Protocol (MCP), and tool usage and workflow orchestration. Anyone studying from material written before mid-2026 is likely missing this agentic AI content entirely, since it's a genuinely new addition rather than a rewording.

In-scope AWS services worth knowing by name include Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI and SageMaker JumpStart, Amazon Comprehend, Amazon Rekognition, Amazon Textract, Amazon Transcribe, Amazon Translate, Amazon Polly, Amazon Personalize, Amazon Lex, and the newer additions Kiro and Strands Agents — both added to the in-scope list in the v1.1 revision.

Cost Breakdown

The exam costs $100 USD, consistent with AWS's standard pricing for its Foundational-tier certifications (the same tier as Cloud Practitioner). AWS notes that foreign exchange rates apply for candidates paying in other currencies, and directs candidates to its exam pricing policy page for country-specific figures rather than publishing a single fixed international rate. There's no retake-specific discount publicly listed on AWS's own certification page — retakes are billed at the same $100 rate, subject to AWS's standard retake waiting period between attempts.

Prerequisites and Eligibility

None are formally required — AWS lists no mandatory prior certification or credential. That said, AWS's own guidance strongly implies a practical prerequisite: candidates entirely new to AWS Cloud are pointed toward AWS Cloud Practitioner Essentials or AWS Technical Essentials first, since the exam assumes familiarity with core AWS services and the shared responsibility model without directly testing them as standalone topics.

How AIF-C01 Exam Is Scored and What Passing Requires

Results are reported as a scaled score between 100 and 1,000, with a minimum passing score of 700. AWS uses a compensatory scoring model, meaning you do not need to pass each individual domain separately — only the overall exam score matters, so relative weakness in one lower-weighted domain (say, Responsible AI at 14%) can be offset by strong performance elsewhere. AWS does caution that section-level feedback on your score report is general information rather than a precise diagnostic, so treat it as directional rather than exact.

Renewal and Recertification

This certification is valid for 3 years, matching the standard AWS recertification cycle. Before it expires, you can recertify either by retaking the current version of the AIF-C01 exam, or by earning the AWS Certified Machine Learning Engineer – Associate certification, which AWS states will automatically recertify AI Practitioner as well — a useful shortcut for anyone planning to advance to the more technical ML Engineer credential anyway rather than sitting two separate exams.

How AIF-C01 Exam Compares to Its Closest Alternative

Within AWS's own catalog, the most relevant comparison is to AWS Certified Cloud Practitioner. AWS's own FAQ draws this distinction plainly: Cloud Practitioner's exam content outline contains only one task statement related to AI, while AI Practitioner's entire outline focuses on AI, ML, and generative AI — they're not competing for the same audience so much as testing genuinely different depth on the same general "foundational, non-technical AWS knowledge" tier. Plenty of candidates reasonably earn both.

Outside AWS, the closest cross-vendor comparison is Microsoft's Azure AI Fundamentals (AI-900) certification, which occupies a similar foundational, business-facing niche within Microsoft's Azure certification track. Both test conceptual AI/ML literacy tied to their respective cloud platform's specific AI services rather than platform-agnostic AI theory, and both are commonly recommended as a first AI credential for non-engineers working alongside technical AI teams. Which one is more useful depends entirely on which cloud platform your organization or target employer actually runs on.

 Preparation Timeline

For someone who already holds AWS Cloud Practitioner or an Associate-level AWS certification and has some general AI/ML conceptual familiarity, 3–4 weeks of focused study against the current exam guide is a reasonable target — with particular attention to the Applications of Foundation Models domain given its 28% weight, and the newer agentic AI content added in the April 2026 update.

For someone newer to both AWS and AI concepts generally, budgeting 6–8 weeks, working through AWS's own free Exam Prep Plan on Skill Builder (which includes an official practice question set and a full official practice exam), tends to produce more reliable results — this exam rewards conceptual breadth across genuinely distinct domains (technical AI fundamentals, generative AI mechanics, responsible AI, and security/governance) rather than deep expertise in any single one.