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Exam Information
Official specifications published by EC-Council
Exam Format
Registration
Skills this practice test covers
- Rate an organization's AI readiness and place it on an AI maturity model.
- Rank AI use cases by return on investment and feasibility, then decide whether to build, buy or partner.
- Plan an AI roadmap with dependencies, clear roles and an operating model.
- Lead staff through AI change with the ADKAR and Kotter frameworks.
- Set governance, ethics and compliance rules for AI tools before they go live.
- Run an AI pilot with success metrics, then report value on a dashboard as it scales.
Explore related topics
CAIPM exam content
6 domains · 12 topics · 179 questions
Foundations of Artificial Intelligence
This domain sets the base vocabulary. Candidates define AI and its categories, place robotic process automation (RPA) beside it, and explain the core parts of an AI architecture and the AI lifecycle.
Skills tested
- Define artificial intelligence and sort systems into AI categories.
- Tell robotic process automation (RPA) apart from machine learning.
- Explain where transformers and neural networks fit among AI architecture models.
- Describe the AI lifecycle and the MLOps processes that keep a model in production.
Official topics
- Introduction to AI
- Generative AI Foundations
AI Operations and Data Management
Despite its name, this domain is mostly about people and business value. Candidates find viable AI projects, measure ROI and KPIs, and lead staff to adopt AI through change, tracked with adoption metrics.
Skills tested
- Identify viable AI projects from an organization's needs and business processes.
- Measure AI value with ROI and KPIs.
- Plan change with the change management team and track readiness, cultural impact and usability.
- Explain AI benefits and limits to coworkers in plain terms.
Official topics
- AI Operations Foundations
- Data Management for AI Systems
AI Adoption Leadership
This domain covers automation and data work that an AI program depends on. Candidates plan process automation with AI, then set data management, data quality and governance rules so models perform well.
Skills tested
- List the steps to automate a business process with AI and measure the improvement.
- Choose data handling and storage approaches that suit an AI application.
- Keep AI design and development documents current.
- Use structured data displays to tell a clear story to decision makers.
Official topics
- AI for Business
- Leading AI Adoption
Intelligent Automation and Prompt Engineering
Candidates compare the types of GenAI, choose the right solution for a business need, and review its limits and risks. The second half is prompt engineering: writing strong prompts and judging what comes back.
Skills tested
- Choose a GenAI solution that fits a business task and name its risks.
- Spot ML blind spots and edge cases, and plan remedies against unintended bias.
- Write prompts with clear context, constraints and output format.
- Evaluate prompt outputs and adapt the prompt modality when text alone is not enough.
Official topics
- Intelligent Process Automation
- Prompt Engineering Boot Camp
AI Security
This domain joins AI with cybersecurity and law. Candidates manage AI risk with controls, governance and incident response, and explain how AI helps detect and prevent threats.
Skills tested
- Enact AI controls to manage risk and enforce AI governance and compliance.
- Assign data governance roles and responsibilities.
- Plan AI incident response and monitoring.
- Address privacy, ethical and legal issues in an AI deployment.
- Describe the security challenges and threats that target AI systems.
Official topics
- Cybersecure AI Principles
- AI-based Security Solutions
AI Applications and Future Trends
Candidates study real deployments in manufacturing, healthcare and finance, and how to evaluate an AI solution with users and stakeholders. The domain ends with emerging technologies and their effect on society and the economy.
Skills tested
- Draw lessons from AI deployments in manufacturing, healthcare and finance.
- Test how users interact with an AI solution and gather their feedback.
- Evaluate AI performance against the functional requirements agreed with stakeholders.
- Explain what quantum and neuromorphic computing could change for AI.
Official topics
- Real-world AI Applications and Deployment
- Future AI Predictions
Requirements
- Eligibility: at least two years of work experience in information security, or attendance at official EC-Council training.
- Candidates who take the experience route send an eligibility application to EC-Council and wait for approval before they book.
- Recommended: working knowledge of generative AI and prompt engineering, since one domain is built on both.
About the CAIPM exam
The CAIPM exam checks whether a manager can take AI from an idea to everyday use in an organization. EC-Council's exam blueprint has six domains. They run from AI basics and prompt engineering to adoption leadership, AI security and future trends. Most questions are about decisions, not code: which project to fund, how to bring staff along, and which controls keep AI use safe. Passing earns the Certified AI Program Manager (C|AIPM) certification.
The PlanetCert practice test spends the most questions on two areas: choosing and integrating AI platforms, and governance with responsible AI. Each explanation says why one management choice fits the case better than the others, for example a phased rollout instead of a company-wide launch. Study mode suits the change management questions, where ADKAR and Kotter steps are easy to mix up. Brainy AI Assistant can compare build, buy and partner options for a described use case, or explain how a readiness score differs from a maturity level.
How to earn the certification
- Earns
- Certified AI Program Manager (C|AIPM)
How to prepare for CAIPM
- 1
Read the blueprint topic by topic
EC-Council's blueprint lists the topics under each sub-domain, and some sit under unexpected headings. Change leadership, for example, appears under AI Operations and Data Management. Study by topic, not by heading.
- 2
Map one real AI project
Pick a workplace process and take it through the exam's decisions: viable or not, build or buy, which data it needs, which ROI and KPIs prove it works, and who must change how they work.
- 3
Practise prompts and judge the output
Write prompts for one business task with a GenAI tool, change the context and format, and compare the results. The prompt engineering topics ask for exactly this judgment.
- 4
Learn the AI risk controls
AI Security mixes cybersecurity, privacy, law and governance. List the controls, the data governance roles and the incident response steps until each one fits a short scenario.
- 5
Close with full practice runs
Take the CAIPM practice test in timed mode, then reread each explanation for a missed governance or change management question. Ask Brainy AI Assistant to replay the same case with one detail changed.
Start a timed CAIPMrun
EC-Council runs an official Certified AI Program Manager (C|AIPM) course, and attending it also meets the exam's eligibility rule.
Who CAIPM is for
- Program and product managers asked to lead AI projects across business and technology teams.
- Information security professionals who now oversee how their organization uses AI.
- Transformation and operations leads who must turn AI pilots into daily practice.
Typical job titles: AI program manager · Technical program manager · AI transformation lead · Digital transformation manager · AI governance manager
CAIPM glossary
19 terms from the exam objectives and the vendor's official material, each defined in a sentence.
- ADKAR
- A change model that moves each person through awareness, desire, knowledge, ability and reinforcement.
- Adoption Metrics
- Measures of how well people take up AI; the blueprint names readiness, cultural impact and usability.
- AI Incident Response & Monitoring
- Watching AI systems for failures or attacks and following a set process when one occurs.
- AI Lifecycle
- The stages an AI system passes through, from planning and design to deployment and monitoring.
- AI Ops
- AI Operations: the use of AI inside operational systems, for automation, data management and deployment.
- Big Data
- Data sets too large or varied for ordinary tools, which AI can analyze for insights.
- Data Governance
- The roles, rules and responsibilities that control the quality and use of data in AI systems.
- Data Sampling
- Choosing a subset of data to train or test an AI model, so that the subset represents the whole.
- Decision-support AI
- AI that gives people information or recommendations to help them decide, while a person keeps the final choice.
- Edge Case
- An unusual input that a machine learning model handles badly because it rarely appeared in training.
- Kotter
- Kotter's change framework: a sequence of steps that leads an organization through a large change, starting with a sense of urgency.
- MLOps
- Practices that move machine learning models into production and keep them working there.
- Neuromorphic Computing
- Computer hardware built to work like the brain's neurons, which could run AI with much less power.
- Prompt Engineering
- Designing the input to an AI model so that its output is accurate and useful.
- Prompt Modality
- The form a prompt takes, such as text or an image, and the form of output it asks for.
- Robotic Process Automation (RPA)
- Software that repeats fixed, rule-based steps in a business process, such as copying data between systems, without learning from data.
- ROI and KPIs
- Return on investment and key performance indicators: the figures used to show whether an AI project delivers value.
- Transformer
- A neural network design that processes a whole sequence at once and powers most generative AI models.
- Unintended Bias
- An unfair skew in an AI system's output that its builders did not intend, often caused by the training data.