Free Practice Questions for EXIN BCS Artificial Intelligence Foundation Certification
Study with 323 exam-style practice questions designed to help you prepare for the EXIN BCS Artificial Intelligence Foundation.
Start Practicing
All Domains
Practice with randomly mixed questions from all topics
Domain Mode
Practice questions from a specific topic area
Quiz History
Exam Details
Key information about EXIN BCS Artificial Intelligence Foundation
- Multiple choice
No
No
65%
1 and 2 (Remembering, Understanding)
Knowledge of AI terminology, for instance through the EXIN BCS Artificial Intelligence Essentials exam or a BCS Artificial Intelligence Award exam, is strongly recommended.
Individuals with an interest in exploring the functions and abilities of AI, and how these can be used in an organization. Relevant roles include developers, project managers, product managers, chief information officers, chief finance officers, change practitioners, business consultants, and leaders of people.
Multiple-choice questions
40
60
No
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
An introduction to AI and historical development(15%)
Identify the key definitions of key AI terms
- Human intelligence
- Artificial Intelligence
- Machine learning
- Scientific method
Describe key milestones in the development of AI
- Asilomar principles
- Dartmouth conference of 1956
- AI winters
- Big data
- Internet of Things (IoT)
- Large language models (LLMs)
Describe different types of AI
- Narrow AI (weak AI)
- General AI (strong AI)
- Image recognition
- Speech recognition
- Language translation
- Virtual assistants
- Generative AI
Explain the impact of AI on society
- Ethical principles
- Social impact
- Economic impact
- Environmental impact
- UN 17 Sustainable Development Goals (SDGs)
- EU AI Act (2024)
- Floridi & Cowls’ principles
- AI UK principles
Describe sustainability measures to help reduce the environmental impact of AI
- Green IT initiatives
- Data center energy and efficiency
- Sustainable supply chain
- Choice of algorithm
- Low-code/no-code programming
- Monitoring and reporting environmental impact
Ethical and legal considerations(15%)
Describe ethical concerns, including bias and privacy, in AI
- Ethics
- Law
- Bias
- Unfairness
- Discrimination
- Data privacy
- Data protection
- Employment impact
- Autonomous weapons
- Autonomous vehicles
- Liability framework
Describe the importance of guiding principles in ethical AI development
- UK AI principles
- Safety, security and robustness
- Transparency and explainability
- Fairness
- Accountability and governance
- Contestability and redress
- AI governance
Explain strategies for addressing ethical challenges in AI projects
- Self-interest
- Self-review
- Conflict of interest
- Intimidation
- Advocacy
- Dealing with bias
- Openness
- Transparency
- Trustworthiness
- Explainability
- Ethical risk framework
Explain the role of regulation in AI
- WCAG
- Data Protection Act 2018
- UK GDPR
- International Standards Organization (ISO)
- NIST
- Professional standards
Explain the process of risk management in AI
- Risk
- Risk management
- Risk analysis
- SWOT analysis
- PESTLE
- Cynefin
- UK AI principles
- Risk mitigation strategies
Enablers of AI(15%)
List common examples of AI
- Human compatible AI
- Wearable AI
- Edge AI
- Internet of Things (IoT)
- Personal care AI
- Self-driving vehicles
- Generative AI tools
Describe the role of robotics in AI
- Robotics
- Intelligent robots
- Non-intelligent robots
- Industrial robots
- Personal robots
- Autonomous robots
- Nanobots
- Humanoids
- Robotic process automation (RPA)
Describe machine learning
- Machine learning
- Neural networks
- Deep learning
- Large language models (LLMs)
- Data science
- Algorithms
Identify common machine learning concepts
- Prediction
- Object recognition
- Classification
- Random decision forests
- Clustering
- Recommendations (Netflix, Spotify)
Describe supervised and unsupervised learning
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Classification
- Clustering
Finding and using data in AI(20%)
Describe key data terms
- Big data
- Data visualization
- Structured data
- Semi-structured data
- Unstructured data
Describe the characteristics of data quality and why it is important in AI
- Data quality characteristics (Accuracy, Completeness, Uniqueness, Consistency, Timeliness)
- Errors
- Inaccuracies
- Bias
- Loss of trust
- Financial penalties
Explain the risks associated with handling data in AI and how to minimize them
- Bias (Multiple sources, Diversity, Fairness metrics)
- Misinformation (Reliability checks, Subject matter experts)
- Processing restrictions
- Legal restrictions (UK GDPR, DPA 2018)
- Scientific method
Describe the purpose and use of big data
- Storage and use
- Understanding the user
- Improving process
- Improving experience
- Targeted marketing
- Personalized experiences
- Business decision making
Explain data visualization techniques and tools
- Written
- Verbal
- Pictorial
- Sounds
- Dashboards
- Infographics
- Virtual reality
- Augmented reality
Describe key generative AI terms
- Generative AI
- Large language models (LLMs)
Describe the purpose and use of generative AI including large language models (LLMs)
- Prompt engineering
- Natural language processing (NLP)
- Image generation
Describe how data is used to train AI in the machine learning process
- Machine learning process (Analyze problem, Data selection, Data pre-processing, Data visualization, Select ML model, Train model, Test model, Review)
- Algorithms
- Test data
Using AI in your organization(20%)
Identify opportunities for AI in your organization
- Automation
- Repetitive tasks
- Content creation
- Generative AI
List the contents and structure of a business case
- Business case (Introduction, Executive summary, Current state, Options, Analysis of costs/benefits, Impact assessment, Risk assessment, Recommendations, Appendices)
Identify and categorize stakeholders relevant to an AI project
- Stakeholder definition
- Stakeholder categorization (Power/interest grid, Stakeholder wheel)
Describe project management approaches
- Agile
- Waterfall
- Hybrid
Identify the risks, costs and benefits associated with a proposed solution
- Risk analysis
- Risk assessment
- Risk owners
- Risk appetite
- Risk management strategies (Accept, Mitigate, Avoid, Transfer)
- Financial costs
- Financial benefits
- Forecasting
- Socio-economic benefits
- Triple bottom line
- Cost-benefit analysis
Describe the ongoing governance activities required when implementing AI
- Compliance
- Risk management
- Lifecycle governance (Manage, Monitor, Govern)
Future planning and impact – human plus machine(15%)
Describe the roles and career opportunities presented by AI
- AI-specific roles (Machine learning engineer, Data scientist, AI research scientist, Computer vision engineer, NLP engineer, Robotics engineer, AI ethics specialist, AI anthropologist)
- Training
- Efficiency
- Automation
Identify AI uses in the real world
- Marketing
- Healthcare
- Finance
- Transportation
- Education
- Manufacturing
- Entertainment
- IT
- Recommendation algorithms
- Language translation
- Fraud detection
- Self-driving cars
- Chatbots
- Digital assistants
Explain AI’s impact on society, and the future of AI
- Benefits of AI (Reducing human error, Data analysis, Medical diagnosis)
- Challenges of AI (Ethical concerns, Algorithm bias, Privacy, Job loss, Lack of creativity/empathy, Security risks, Socio-economic inequality, Market volatility, Rapid self-improvement)
- Societal impact
- Environmental impact
- Economic impact
- Future advancements
Describe consciousness and its impact on ethical AI
- Human consciousness (sentience)
- AI consciousness
- Kurzweil Singularity
- Seth’s theory of human consciousness
- Functional capabilities
- Ethical challenges of artificial consciousness
Techniques & products