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Exam Information
Official specifications published by EC-Council
Exam Format
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Skills this practice test covers
- Compare human and artificial intelligence, and trace AI's milestones from early programs to generative AI.
- Identify AI at work and in daily life, from recommendation systems to phishing detection.
- Explain how data, training and testing turn into a working AI model.
- Tell machine learning, deep learning, natural language processing and large language models apart.
- Improve a prompt with clear context, an output format and few-shot examples.
- Spot bias, hallucinations and attacks such as prompt injection in AI systems.
Explore related topics
112-59 exam content
5 domains · 22 topics · 180 questions
Introduction to Artificial Intelligence20%36 questions
This domain sets out what AI is and where it came from. Candidates compare human and artificial intelligence, explain how data, algorithms and models form an AI system, and follow AI from early theory to current trends.
Skills tested
- Compare human intelligence with artificial intelligence, and describe how the two work together.
- Explain how data, algorithms and models combine into an AI system.
- Summarize the milestones of AI, from early programs and AI winters to generative AI.
- Explain how an AI system differs from traditional programming.
Questions by topic
- Similarities, Differences, and Collaboration between Human and Artificial Intelligence10
- Milestones and Developments in the Evolution of AI8
- Advancements and Future Directions Shaping AI Technologies12
- Data, Algorithms, and Models form the Foundation of AI Systems6
Everyday AI Tools and Use Cases20%36 questions
This domain is about AI that people already use. Candidates identify AI in daily life and at work, and explain what it does in manufacturing, transportation, education and cybersecurity.
Skills tested
- Identify AI in daily life, such as recommendation systems and navigation apps.
- Recognize workplace AI tools for writing, meeting summaries and process automation.
- Explain how predictive maintenance and quality control use AI in manufacturing.
- Describe how AI personalizes learning and gives feedback in education.
- Explain how AI detects threats such as phishing and malware.
Questions by topic
- AI Tools in the Workplace11
- AI in Manufacturing and Industrial Processes4
- AI in Cybersecurity6
- Common AI Technologies Used in Daily Life7
- AI in Transportation4
- AI in Education4
Building Blocks of AI24%42 questions
This domain explains how AI systems are made: the role of data, how models are trained, machine learning and neural networks, natural language processing, and generative AI. It ends with choosing the right tools for a project.
Skills tested
- Explain why data quality, labeling and preprocessing decide how well a model works.
- Describe how a model is trained, validated, tested and deployed.
- Compare classification, regression, clustering and reinforcement learning.
- Explain how generative AI and large language models differ from traditional software.
- Select AI tools that match the requirements of a project.
Questions by topic
- Role of Data for Effective AI Systems10
- Machine Learning and Neural Networks9
- Natural Language Processing (NLP) and Its Role in AI5
- AI Models and How They are Developed and Trained7
- Generative AI (GenAI) and Large Language Models (LLMs)5
- Selecting the Right Tools for AI Projects6
Prompt Crafting for Effective AI Interactions20%36 questions
This domain is about writing prompts that give useful output. Candidates learn the principles of prompt engineering, structure prompts with clear context and formats, and apply advanced techniques for specific use cases.
Skills tested
- Set the context, the task and the output format in one prompt.
- Refine a weak prompt step by step until the output is useful.
- Apply few-shot, chain-of-thought and negative prompting.
- Split a large task into a chain of prompts.
Questions by topic
- Basics of Prompt Engineering8
- Crafting Effective Prompts14
- Prompt Engineering Techniques14
AI Ethics and Responsible AI16%30 questions
This domain covers the risks of AI and the rules for using it well. Candidates identify bias, hallucinations and security concerns, apply ethical principles and data transparency, and know the main governance standards.
Skills tested
- Spot bias and hallucinations in AI output.
- Recognize attacks on AI systems, such as prompt injection and data poisoning.
- Apply fairness, accountability and transparency when deciding how to use AI.
- Explain the purpose of the EU AI Act, the NIST AI Risk Management Framework and the OECD AI Principles.
Questions by topic
- Ethical, Societal, and Security Concerns in AI Systems12
- Responsible Practices, Governance, and Global Standards in AI Usage10
- Principles and Importance of using AI Ethically and Fairly8
Requirements
- No formal prerequisites: EC-Council sets no eligibility criteria for the program.
- No coding or technical background is required.
- Recommended: basic familiarity with computers and the internet, and an interest in AI concepts and tools.
About the 112-59 exam
112-59 checks that a beginner understands what AI is, how it works and how to use it with care. EC-Council sets no eligibility criteria, and no coding or technical background is required. The five domains run from the history and building blocks of AI, such as machine learning and large language models, to everyday tools, prompt crafting and AI ethics. Passing earns the Artificial Intelligence Essentials (AIE) certification at EC-Council's foundational level.
Building Blocks of AI has the most questions in the PlanetCert practice test. Study Mode suits the prompt crafting questions, where the reason one prompt beats another matters more than the answer itself. Flashcard mode helps with the many named ideas, from AI winters to the EU AI Act. Brainy AI Assistant can rewrite a weak prompt for practice, or explain how data poisoning differs from prompt injection.
How to earn the certification
- Earns
- Artificial Intelligence Essentials (AIE)Foundational
The certification stays valid for three years.
- After
- CAIPM Certified AI Program Manager (C|AIPM)
The management path, for those who plan and run AI programs in an organization.
- 612-51 Certified Responsible AI Governance & Ethics Professional (C|RAGE)
The governance path, which goes deeper into the ethics and regulation topics of the last AIE domain.
- 312-52 Certified Offensive AI Security Professional (C|OASP)
The security path in EC-Council's AI certification suite.
- CAIPM Certified AI Program Manager (C|AIPM)
How to prepare for 112-59
- 1
List the key terms of each domain
Write each domain's terms on one page, from narrow AI and AI winters to the EU AI Act. Then explain each one in a single plain sentence, because the exam checks understanding, not code.
- 2
Use three AI tools on real tasks
Try a chatbot, an image generator and a meeting summary tool on everyday work. Note what each one does well and where it fails, since the syllabus asks about uses and limits.
- 3
Rewrite one prompt five ways
Take a vague request and improve it with context, a role, an output format, examples and a chain of steps. Compare the outputs and note which change helped, and why.
- 4
Follow one model from data to deployment
Building Blocks of AI has the most practice questions. Trace one example, such as a spam filter, through data collection, labeling, training, testing and deployment.
- 5
Work the practice test by domain
Work through the 112-59 practice test in Study Mode one domain at a time, then take a Timed Mode run. Ask Brainy AI Assistant about any ethics or governance answer that went wrong.
Start a timed 112-59run
Who 112-59 is for
- Students who want to understand AI before they choose a field.
- Office staff in HR, operations or project roles who now use AI tools every day.
- Teachers and trainers who need to explain AI and its risks to others.
- Managers and business owners who decide where AI fits in their teams.
Typical job titles: Business analyst · Project coordinator · Operations specialist · HR specialist · Trainer
112-59 glossary
18 terms from the exam objectives and the vendor's official material, each defined in a sentence.
- AI winters
- Periods when interest and funding for AI research fell, after early promises were not met.
- Augmented intelligence
- Using AI to support human decisions instead of replacing the people who make them.
- Chain-of-thought prompting
- Asking a model to work through a problem step by step before it gives its answer.
- Context windows
- The amount of text a language model can take into account at once, including the prompt and its own reply.
- Data labeling
- Adding the right answer, such as spam or not spam, to each training example so a model can learn from it.
- Data poisoning
- An attack that slips bad examples into training data, so that the model learns the wrong behavior.
- Deep learning
- Machine learning with neural networks that have many layers, used for images, speech and language.
- Deepfakes
- Fake images, audio or video, made with AI, that show a real person saying or doing something they did not.
- EU AI Act
- The European Union law that sets rules for AI systems according to how much risk they carry.
- Few-shot prompting
- Giving a model a few worked examples in the prompt, so that it follows the same pattern.
- Hallucinations in AI
- Confident AI output that is false or made up, such as an invented fact or a source that does not exist.
- Human-in-the-loop systems
- Systems in which a person reviews or approves the output of an AI before it takes effect.
- Large Language Models (LLMs)
- AI models trained on very large amounts of text to predict and generate language, such as the models behind chatbots.
- Narrow AI
- AI that is built for one kind of task, such as translating text or recognizing faces.
- Natural Language Processing (NLP)
- The branch of AI that lets computers read, understand and produce human language.
- Overfitting
- When a model learns its training data so closely that it does poorly on new data.
- Prompt chaining
- Splitting a task into several prompts, where the output of each prompt feeds the next one.
- Prompt injection attacks
- Attacks that hide instructions in input text, so that an AI system ignores the instructions it was given.