Certified AI Practitioner (CAIP)™
The Certified AI Practitioner (CAIP) exam is intended for professionals and students who wish to validate their ability to build, deploy, and manage AI solutions in real - world enviornments.
Self-paced Course
Learn with labs & projects at your own pace.
Practice Exam
Timed questions with instant feedback & review.
AI is everywhere—credible practitioners are rare.
CAIP is built for professionals who need to evaluate AI claims, choose fit-for-purpose methods, and apply responsible practices—without requiring coding or advanced math.
Foundations that translate to decisions
Understand AI, ML, DL, training vs inference, and key limitations like data dependency, overfitting, and domain shift—so you can interpret outcomes, not just headlines.
Modern methods, clearly explained
Move beyond buzzwords: supervised vs unsupervised vs reinforcement learning, NLP and vision basics, and what GenAI can (and cannot) do—plus how to manage hallucinations.
Responsible AI as a core skill
Learn practitioner-ready governance: fairness, transparency, accountability, privacy concepts (PII, minimization, retention), and risk controls that align with modern regulatory expectations.
Exam Curriculum
The CRAIS certification covers a comprehensive curriculum designed to equip professionals with the knowledge and skills to navigate the complex landscape of responsible AI.
Fundamentals of AI
AI vs ML vs DL hierarchy and everyday examples Historical evolution: Turing → expert systems → ML → deep learning → GenAI Rule-based systems vs data-driven learning Key terms: algorithm, model, training, inference, features, labels Limitations: black-box behavior, overfitting, domain shift, data quality
AI Technologies & Methods
Supervised, unsupervised, and reinforcement learning—when to use each NLP fundamentals: chatbots, translation, sentiment, summarization Computer vision: detection, recognition, inspection, medical imaging Speech recognition: transcription, assistants, captions Generative AI & LLMs: capabilities and hallucination risks
Data & AI Foundations
Data quality, quantity, and bias impacts on reliability and fairness Structured vs unstructured vs semi-structured data (tables, text, JSON/XML) Training vs validation vs testing—generalization mindset Data lifecycle: collection → cleaning → labeling → feature prep → storage Privacy concepts: PII, consent, minimization, retention
AI Applications Across Industries
Finance, healthcare, retail & supply chain, manufacturing, public sector patterns Everyday AI: recommendations, assistants, maps, spam filters, smart photos Enterprise trends: decisioning, automation at scale, GenAI copilots Matching use cases to methods (NLP vs CV vs ML) Trade-offs: value vs feasibility vs risk
Responsible AI & Ethics
Fairness, transparency, accountability—practical definitions Bias sources: data, algorithmic, societal (with examples) Governance basics: policies, reviews, monitoring, incident response Regulatory awareness (conceptual): privacy and risk-based approaches Ethical risks: hiring, credit, recommenders, opaque decisions
AI Strategy & Future Trends
AI in transformation: automation, personalization, insights, innovation Maturity stages: experimenting → operationalizing → scaling → transforming Build vs buy considerations: talent, platform, risk, change management Emerging frontiers: edge AI, robotics, multimodal AI Future of work: human–AI collaboration and new roles
AI Practitioner Skills
AI literacy: probabilistic outputs, limits, and why verification matters Communicating AI with clarity: analogies and outcome-driven messaging Identifying use cases: repetitive, data-rich, high-value tasks Risk-aware practice: guardrails, monitoring, escalation, documentation Continuous development: learning, experimentation, cross-functional collaboration
Syllabus Weightage
Sample Examination
Experience the scenario-based methodology used in GIofAI professional assessments.
Tip: GIofAI scenario questions often present multiple technically "valid" options. You must choose the one that aligns best with the FATE (Fairness, Accountability, Transparency, and Explainability) framework and institutional risk policy.
GIofAI Exam Portal
A world-class testing interface designed for precision, security, and accessibility.
Clean, Distraction-Free UI
Our portal uses a high-contrast, minimalist design to help you focus on complex case studies.
Integrated Utility Tools
Access on-screen scientific calculators, fairness metric scratchpads, and translation assistants.
Safe Exam Browser
Institutional-grade lockdown technology ensures the integrity of your professional designation.
Flexible Session Management
Auto-save features and interruption-recovery protocols protect your progress against connectivity drops.
Logistics & Compliance
Global delivery with institutional security standards.
Exam Format
Retake Policy
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Up to 2 retakes permitted within a 6-month period.
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Each retake requires a separate examination purchase.
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Practice Exam completion is strongly recommended prior to retake attempts.
Scoring & Results
No Negative Marking
Candidates are encouraged to answer all 250 questions; there is no penalty for incorrect answers.
Instant Provisionals
View your provisional result immediately upon submitting your examination session.
Official Transcript
Verified certificate and transcript will be available in your dashboard within 48 hours.
Verified Identity
Biometric and government ID verification is mandatory for all candidates via the web-based portal.
Transcript & Certificate
Official documentation of your certification achievement.
Official Transcript
Your verified transcript includes detailed performance metrics and section-wise scores.
Digital Certificate
Your professional certification credential, verifiable and shareable.
Frequently Asked Questions
Quick clarity on format, preparation, outcomes, and what CAIP is designed to validate.
CAIP validates the ability to apply foundational AI concepts, understand core methods (including GenAI), work with data fundamentals, and make responsible AI decisions in real-world scenarios—without requiring coding or advanced math.
Typical delivery is closed book with no external assistance. Environment checks and monitoring help ensure fairness and integrity for all candidates.
CAIP is typically 50–60 multiple-choice questions (commonly 55). Duration is configurable from 60–120 minutes, with 60–90 minutes recommended for standard delivery.
The pass threshold is 70%. Each question carries equal weight, and there is no negative marking.
Candidates may retake up to two times within six months of the first attempt, with a recommended waiting period of seven days between attempts. Retakes may require a separate purchase.
Prove AI fluency with a practitioner-grade baseline.
Register for the CAIP assessment, review the syllabus weightage, and sit the exam when you’re prepared. Designed for professionals who need confident AI judgment—across foundations, methods, data, and responsible practice.
Career Path
A progression from entry to leadership, aligned with certifications.
Entry-Level Roles
- AI Practitioner
- Junior AI Engineer
- Data Analyst / AI Technician
Intermediate Roles
- Machine Learning Engineer
- Data Scientist
- Generative AI Developer
- Computer Vision Engineer
- Cloud AI Engineer
- Responsible AI Specialist
Advanced Roles
- AI Consultant (Finance, Healthcare, Manufacturing, etc.)
- AI Solutions Architect
- AI Research Engineer
- AI Security & Privacy Specialist
Leadership Roles
- AI Product Manager
- Head of AI / Director of Data Science
- AI Transformation Officer
- Chief AI Officer (CAIO)
- AI Governance & Risk Director
Entry-Level Roles
- AI Practitioner
- Junior AI Engineer
- Data Analyst / AI Technician
Advanced Roles
- AI Consultant (Finance, Healthcare, Manufacturing, etc.)
- AI Solutions Architect
- AI Research Engineer
- AI Security & Privacy Specialist
Intermediate Roles
- Machine Learning Engineer
- Data Scientist
- Generative AI Developer
- Computer Vision Engineer
- Cloud AI Engineer
- Responsible AI Specialist
Leadership Roles
- AI Product Manager
- Head of AI / Director of Data Science
- AI Transformation Officer
- Chief AI Officer (CAIO)
- AI Governance & Risk Director
Career Growth Ladder
| Level | Typical Roles | Relevant Certifications |
|---|---|---|
| Entry-Level | AI Technician, Data Analyst | CAIP |
| Mid-Level | AI Engineer, ML Engineer, AI Developer | CAIE, CRAIS, CGAIS |
| Advanced | AI Architect, AI Consultant, Security Specialist | CLLMS, CCVE, CAICE, CAISPS, Industry Certs |
| Leadership | Head of AI, AI Product Manager, CAIO | CAIL, CAITO, CAIGR, CEAIR |
Certification Pathway
GlofAI Learning Path (Certification Progression)
Stage 1 — Foundation
Stage 2 — Core AI Competency
- Certified AI Engineer (CAIE) – Core technical certification.
- Certified Responsible AI Specialist (CRAIS) – Ethics, bias mitigation, compliance.
- Certified Generative AI Specialist (CGAIS) – Prompt engineering, generative AI tools.
Stage 3 — Specialized Technical Expertise
- Certified LLM Specialist (CLLMS) – NLP & Large Language Models.
- Certified Computer Vision Expert (CCVE) – Imaging, video analytics, AR/VR.
- Certified AI Cloud Engineer (CAICE) – AI on AWS, Azure, GCP, Databricks.
- Certified AI Security & Privacy Specialist (CAISPS) – AI risks, privacy, cybersecurity.
Stage 4 — Industry-Specific Applications
- Finance & Banking: Certified AI in Financial Services (CAIFS)
- Healthcare: Certified AI in Healthcare (CAIH)
- Retail & Supply Chain: Certified AI in Retail & Supply Chain (CAIRSC)
- Manufacturing: Certified AI in Manufacturing (CAIM)
- Public Sector & Policy: Certified AI in Policy & Governance (CAIPG)
Stage 5 — Executive & Enterprise Leadership
- Certified AI Leader (CAIL) – Strategic, leadership-level AI management.
- Certified AI Transformation Officer (CAITO) – C-suite strategy & transformation.
- Certified Enterprise AI Ready (CEAIR) – AI governance framework for organizations.
- Certified AI Governance & Risk Expert (CAIGR) – Compliance & regulatory strategy.
Stage 1 — Foundation
Goal: Build baseline AI knowledge and confidence.
Certified AI Practitioner (CAIP) – Foundational skills in AI, data, and ML.
Stage 2 — Core AI Competency
Goal: Master model development and deployment.
- Certified AI Engineer (CAIE) – Core technical certification.
- Certified Responsible AI Specialist (CRAIS) – Ethics, bias mitigation, compliance.
- Certified Generative AI Specialist (CGAIS) – Prompt engineering, generative AI tools.
Stage 3 — Specialized Technical Expertise
Goal: Deep dive into specific domains of AI technology.
- Certified LLM Specialist (CLLMS) – NLP & Large Language Models.
- Certified Computer Vision Expert (CCVE) – Imaging, video analytics, AR/VR.
- Certified AI Cloud Engineer (CAICE) – AI on AWS, Azure, GCP, Databricks.
- Certified AI Security & Privacy Specialist (CAISPS) – AI risks, privacy, cybersecurity.
Stage 4 — Industry-Specific Applications
Goal: Apply AI to real-world sectors.
- Finance & Banking: Certified AI in Financial Services (CAIFS)
- Healthcare: Certified AI in Healthcare (CAIH)
- Retail & Supply Chain: Certified AI in Retail & Supply Chain (CAIRSC)
- Manufacturing: Certified AI in Manufacturing (CAIM)
- Public Sector & Policy: Certified AI in Policy & Governance (CAIPG)
Stage 5 — Executive & Enterprise Leadership
Goal: Lead AI strategy and enterprise adoption.
- Certified AI Leader (CAIL) – Strategic, leadership-level AI management.
- Certified AI Transformation Officer (CAITO) – C-suite strategy & transformation.
- Certified Enterprise AI Ready (CEAIR) – AI governance framework for organizations.
- Certified AI Governance & Risk Expert (CAIGR) – Compliance & regulatory strategy.
| Level | Focus | Example Certifications |
|---|---|---|
| Beginner | Foundations | CAIP |
| Intermediate | Core AI Skills | CAIE, CRAIS, CGAIS |
| Advanced | Specialized Technical | CLLMS, CCVE, CAICE, CAISPS |
| Expert | Industry Applications | CAIFS, CAIH, CAIRSC, CAIM, CAIPG |
| Leader | Strategic & Enterprise | CAIL, CAITO, CEAIR, CAIGR |