Exam Code: AIF2.0
Exam Name: BCS Foundation Certificate in Artificial Intelligence V2.0
Certification Provider: BICSI
Corresponding Certification: BICSI Certification

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BICSI AIF2.0 Exam Overview:

Certification Vendor:	BCS (The Chartered Institute for IT)
Exam Name:	BCS Foundation Certificate in Artificial Intelligence V2.0
Exam Number:	AIF2.0
Exam Format:	Closed-book, Single correct answer, Multiple-choice questions, Supervised online examination
Exam Price:	£200 (UK exam-only price, including £35 remote proctoring fee, excluding VAT; international prices vary by region)
Related Certifications:	BCS Essentials Certificate in Artificial Intelligence BCS Foundation Diploma in Artificial Intelligence BCS Foundation Certificate in Artificial Intelligence
Exam Duration:	60 minutes
Real Exam Qty:	40
Passing Score:	65% (26 out of 40)
Available Languages:	English
Exam Way:	Invigilated online examination, delivered digitally at a registered date and time. Closed-book, supervised and not open-book. Candidates may also book through Pearson VUE where available. The published UK exam-only price is £200 including remote proctoring but excluding VAT. International pricing varies by region. Certification validity period and available examination languages are not explicitly specified on the official certification page.
Pre Condition:	No formal prerequisites. Prior achievement of the BCS Essentials Certificate in Artificial Intelligence or a BCS Award from the Artificial Intelligence Pathway is advantageous but not required.
Official Syllabus URL:	https://www.bcs.org/qualifications-and-certifications/certifications-for-professionals/artificial-intelligence-ai-certifications/bcs-foundation-certificate-in-artificial-intelligence/

BICSI AIF2.0 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Finding and Using Data in Artificial Intelligence	20%	- Explain risks associated with handling data in AI and how to minimise them 1. Data bias, misinformation, processing restrictions and legal restrictions 2. UK GDPR, Data Protection Act 2018, source reliability and the scientific method - Describe key data terms 1. Big data, data visualisation, structured data, semi-structured data and unstructured data - Describe how data is used to train AI in the machine learning process 1. Problem analysis, data selection, preprocessing and visualisation 2. Model and algorithm selection, training, testing, iteration and review - Explain data visualisation techniques and tools 1. Dashboards, infographics, virtual reality and augmented reality 2. Written, verbal, pictorial and sound-based representations - Describe the purpose and use of generative AI, including large language models 1. Prompt engineering, natural language processing and image generation 2. Training data, next-word prediction and human-sounding language generation - Describe the purpose and use of big data 1. Data storage and use, understanding users, process improvement and user experience - Describe data quality characteristics and their importance in AI 1. Consequences of poor-quality data, including errors, bias, loss of trust and financial penalties 2. Accuracy, completeness, uniqueness, consistency and timeliness - Describe key generative AI terms 1. Generative AI and large language models
Topic 2: Enablers of Artificial Intelligence	15%	- Describe machine learning 1. Machine learning, neural networks, deep learning and large language models - List common examples of AI 1. Human-compatible and wearable AI, edge AI, IoT, personal care, self-driving vehicles and generative AI tools - Describe supervised and unsupervised learning 1. Semi-supervised learning 2. Supervised learning using labelled data 3. Unsupervised learning using unlabelled data - Identify common machine learning concepts 1. Prediction, object recognition, classification, random decision forests, clustering and recommendations - Describe the role of robotics in AI 1. Intelligent and non-intelligent robots 2. Robotic process automation 3. Industrial, personal, autonomous, nanobot and humanoid robots
Topic 3: An Introduction to AI and Historical Development	15%	- Describe sustainability measures to reduce the environmental impact of AI 1. Algorithm selection, low-code and no-code programming, environmental monitoring and reporting 2. Green IT, data-centre energy efficiency and sustainable supply chains - Describe key milestones in the development of artificial intelligence 1. AI winters, big data, the Internet of Things and large language models 2. Asilomar principles and the Dartmouth Conference of 1956 - Describe different types of AI 1. Narrow AI and general AI 2. Real-world applications, strengths and limitations - Explain the impact of AI on society 1. United Nations Sustainable Development Goals and the EU AI Act 2. Ethical principles and social, economic and environmental impacts - Identify key definitions of artificial intelligence terms 1. Human intelligence, artificial intelligence, machine learning and the scientific method
Topic 4: Using AI in Your Organisation	20%	- Identify and categorise stakeholders relevant to an AI project 1. Power-interest grids and stakeholder wheels 2. Stakeholder definitions and categorisation - Identify risks, costs and benefits associated with a proposed solution 1. Risk analysis, assessment, risk owners and risk appetite 2. Risk acceptance, mitigation, contingency planning, avoidance and transfer 3. Financial forecasting, costs and benefits, socioeconomic benefits and the triple bottom line - Describe project management approaches 1. Agile, waterfall and hybrid approaches - Identify opportunities for AI in your organisation 1. Process automation, repetitive tasks and generative AI for content creation - Describe ongoing governance activities required when implementing AI 1. Compliance and risk management 2. AI lifecycle governance, management, monitoring and oversight - List the contents and structure of a business case 1. Options analysis, costs and benefits, impact assessment and risk assessment 2. Introduction, executive summary and current-state description 3. Recommendations, appendices and supporting information
Topic 5: Ethical and Legal Considerations	15%	- Explain strategies for addressing ethical challenges in AI projects 1. Bias management, openness, transparency, trustworthiness and explainability 2. Self-interest, self-review, conflicts of interest, intimidation and advocacy - Describe the importance of guiding principles in ethical AI development 1. UK AI Principles: safety, security, robustness, transparency, explainability, fairness, accountability, governance, contestability and redress 2. Ethics, legislation and responsible AI governance - Describe ethical concerns in AI 1. Employment, economic impacts, autonomous weapons, autonomous vehicles and liability 2. Bias, unfairness, discrimination, data privacy and protection - Explain the process of risk management in AI 1. Risk analysis, SWOT analysis, PESTLE and Cynefin 2. Risk mitigation, ownership, accountability, stakeholder involvement and subject-matter expertise - Explain the role of regulation in AI 1. International standards, ISO, NIST and consequences of unregulated AI 2. AI regulation, professional standards, UK GDPR and Data Protection Act 2018
Topic 6: Future Planning and Impact - Human Plus Machine	15%	- Describe AI-related roles and opportunities for existing roles 1. Additional training, improved efficiency and automation 2. Machine learning engineers, data scientists, AI research scientists, computer vision engineers, NLP engineers, robotics engineers and AI ethics specialists - Explain AI's impact on society and the future of AI 1. Benefits, challenges, potential problems and societal impacts 2. Environmental sustainability, climate change and economic impacts 3. Future AI advancements and human-machine collaboration - Identify AI uses in the real world 1. Translation, fraud detection, auditing, credit assessment, autonomous vehicles, chatbots and digital assistants - Describe consciousness and its impact on ethical AI 1. Human consciousness and artificial consciousness 2. Ethical challenges associated with artificial consciousness 3. Functional capabilities versus human consciousness 4. The technological singularity and theories of consciousness

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