Artificial Intelligence Awareness Practice Exam
Artificial Intelligence Awareness Practice Exam
About Artificial Intelligence Awareness Exam
The Artificial Intelligence (AI) Awareness Exam is designed to introduce individuals to the foundational concepts and practical implications of AI technologies. AI encompasses a broad set of tools and techniques that enable machines to mimic human intelligence, such as learning, reasoning, and problem-solving. This exam is ideal for beginners, professionals from non-technical backgrounds, and decision-makers who seek to understand how AI impacts industries and daily life.
Who should take the Exam?
This exam is ideal for:
- Business professionals seeking AI literacy
- Students from non-technical disciplines
- Managers and team leads involved in AI projects
- Policy makers and educators exploring AI ethics
- Anyone curious about how AI works and is applied
Skills Required
- Basic digital literacy
- Interest in emerging technologies
- No programming knowledge required
Knowledge Gained
- Understanding what AI is and is not
- Familiarity with machine learning, neural networks, and data science
- Applications of AI across sectors like healthcare, finance, and education
- Ethical, social, and economic implications of AI
- Trends shaping the future of AI
Course Outline
The Artificial Intelligence Awareness Exam covers the following topics -
Domain 1 – Introduction to AI
- Definition and scope of AI
- Types of AI: Narrow, General, Superintelligence
- Brief history and evolution
Domain 2 – Core Concepts
- Machine learning basics
- Deep learning and neural networks
- Natural language processing and computer vision
Domain 3 – Applications of AI
- AI in daily life (voice assistants, recommendations)
- AI in business, medicine, finance, and transportation
- Emerging tools and platforms
Domain 4 – Ethical and Social Considerations
- AI bias and fairness
- Data privacy and surveillance
- Automation, employment, and the economy
Domain 5 – AI in the Real World
- Case studies from global industries
- Challenges in AI implementation
- Future trends and opportunities
