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  • Exam Code: AIF2.0
  • Exam Name: BCS Foundation Certificate in Artificial Intelligence V2.0
  • Updated: Oct 26, 2026
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  • Exam Code: AIF2.0
  • Exam Name: BCS Foundation Certificate in Artificial Intelligence V2.0
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Sporadic time is the only kind most candidates own — lunch breaks, commutes, the hour after midnight. In 2026, Prep4SureReview builds BICSI BCS Foundation Certificate in Artificial Intelligence V2.0 preparation around exactly those fragments: AIF2.0 practice questions that reward accumulation.

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
Related Certifications:BCS Essentials Certificate in Artificial Intelligence
BCS Foundation Certificate in Artificial Intelligence
BCS Foundation Diploma in Artificial Intelligence
Exam Price:£200 (UK exam-only price, including £35 remote proctoring fee, excluding VAT; international prices vary by region)
Passing Score:65% (26 out of 40)
Exam Format:Supervised online examination, Single correct answer, Closed-book, Multiple-choice questions
Available Languages:English
Real Exam Qty:40
Exam Duration:60 minutes
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:

SectionWeightObjectives
Finding and Using Data in Artificial Intelligence20%- Describe key generative AI terms
  • 1. Generative AI and large language models
    - Describe data quality characteristics and their importance in AI
    • 1. Accuracy, completeness, uniqueness, consistency and timeliness
      • 2. Consequences of poor-quality data, including errors, bias, loss of trust and financial penalties
        - Explain data visualisation techniques and tools
        • 1. Written, verbal, pictorial and sound-based representations
          • 2. Dashboards, infographics, virtual reality and augmented reality
            - Explain risks associated with handling data in AI and how to minimise them
            • 1. UK GDPR, Data Protection Act 2018, source reliability and the scientific method
              • 2. Data bias, misinformation, processing restrictions and legal restrictions
                - Describe key data terms
                • 1. Big data, data visualisation, structured data, semi-structured data and unstructured data
                  - Describe the purpose and use of big data
                  • 1. Data storage and use, understanding users, process improvement and user experience
                    - 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 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
                            Ethical and Legal Considerations15%- Explain strategies for addressing ethical challenges in AI projects
                            • 1. Self-interest, self-review, conflicts of interest, intimidation and advocacy
                              • 2. Bias management, openness, transparency, trustworthiness and explainability
                                - 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
                                    - Describe the importance of guiding principles in ethical AI development
                                    • 1. Ethics, legislation and responsible AI governance
                                      • 2. UK AI Principles: safety, security, robustness, transparency, explainability, fairness, accountability, governance, contestability and redress
                                        - Describe ethical concerns in AI
                                        • 1. Bias, unfairness, discrimination, data privacy and protection
                                          • 2. Employment, economic impacts, autonomous weapons, autonomous vehicles and liability
                                            - Explain the role of regulation in AI
                                            • 1. AI regulation, professional standards, UK GDPR and Data Protection Act 2018
                                              • 2. International standards, ISO, NIST and consequences of unregulated AI
                                                Future Planning and Impact - Human Plus Machine15%- 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. Future AI advancements and human-machine collaboration
                                                      • 2. Environmental sustainability, climate change and economic impacts
                                                        • 3. Benefits, challenges, potential problems and societal impacts
                                                          - 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. Ethical challenges associated with artificial consciousness
                                                              • 2. Human consciousness and artificial consciousness
                                                                • 3. The technological singularity and theories of consciousness
                                                                  • 4. Functional capabilities versus human consciousness
                                                                    Using AI in Your Organisation20%- 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
                                                                            - 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. Introduction, executive summary and current-state description
                                                                                  • 2. Recommendations, appendices and supporting information
                                                                                    • 3. Options analysis, costs and benefits, impact assessment and risk assessment
                                                                                      - Identify and categorise stakeholders relevant to an AI project
                                                                                      • 1. Power-interest grids and stakeholder wheels
                                                                                        • 2. Stakeholder definitions and categorisation
                                                                                          - Identify opportunities for AI in your organisation
                                                                                          • 1. Process automation, repetitive tasks and generative AI for content creation
                                                                                            Enablers of Artificial Intelligence15%- Identify common machine learning concepts
                                                                                            • 1. Prediction, object recognition, classification, random decision forests, clustering and recommendations
                                                                                              - List common examples of AI
                                                                                              • 1. Human-compatible and wearable AI, edge AI, IoT, personal care, self-driving vehicles and generative AI tools
                                                                                                - Describe the role of robotics in AI
                                                                                                • 1. Robotic process automation
                                                                                                  • 2. Intelligent and non-intelligent robots
                                                                                                    • 3. Industrial, personal, autonomous, nanobot and humanoid robots
                                                                                                      - Describe supervised and unsupervised learning
                                                                                                      • 1. Unsupervised learning using unlabelled data
                                                                                                        • 2. Semi-supervised learning
                                                                                                          • 3. Supervised learning using labelled data
                                                                                                            - Describe machine learning
                                                                                                            • 1. Machine learning, neural networks, deep learning and large language models
                                                                                                              An Introduction to AI and Historical Development15%- 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. Asilomar principles and the Dartmouth Conference of 1956
                                                                                                                    • 2. AI winters, big data, the Internet of Things and large language models
                                                                                                                      - Identify key definitions of artificial intelligence terms
                                                                                                                      • 1. Human intelligence, artificial intelligence, machine learning and the scientific method
                                                                                                                        - 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

                                                                                                                                The Honest BICSI BCS Foundation Certificate in Artificial Intelligence V2.0 FAQ

                                                                                                                                The AIF2.0 exam is how BICSI decides who earns the BICSI Certification certification, positioned at the Foundation level. It checks whether you can apply the BICSI BCS Foundation Certificate in Artificial Intelligence V2.0 objectives to realistic problems — the skill employers are actually hiring for. It also sits within a broader certification ecosystem covering BCS Essentials Certificate in Artificial Intelligence, BCS Foundation Certificate in Artificial Intelligence, BCS Foundation Diploma in Artificial Intelligence, so the effort compounds across your career. In a crowded global market, that badge is a competitive edge you can point to.

                                                                                                                                The BICSI BCS Foundation Certificate in Artificial Intelligence V2.0 blueprint spans 6 domains, led by An Introduction to AI and Historical Development (15%), Finding and Using Data in Artificial Intelligence (20%), and Future Planning and Impact - Human Plus Machine (15%). Use the weightings as a budget: heavier domains earn heavier slices of your week. The full topic breakdown is in the exam topics section above — plan against it, not against hunches.

                                                                                                                                The BICSI BCS Foundation Certificate in Artificial Intelligence V2.0 exam fits 40 questions into 60 minutes. Do the arithmetic before test day: minutes divided by questions is your per-item budget, and anything eating double its budget gets flagged for the end-of-exam sweep. Rehearse the pacing against a live timer — every Prep4SureReview engine session enforces one — until finishing with spare review time is routine.

                                                                                                                                Passing BICSI BCS Foundation Certificate in Artificial Intelligence V2.0 takes 65% (26 out of 40), and each attempt costs £200 (UK exam-only price, including £35 remote proctoring fee, excluding VAT; international prices vary by region) — retakes included, at full price. The rational strategy is arriving over-prepared: drill with Prep4SureReview practice questions until your timed scores clear the mark several sessions running, then register. You're not being timid; you're refusing to buy the same exam twice.

                                                                                                                                BICSI lists the BICSI BCS Foundation Certificate in Artificial Intelligence V2.0 question formats as: Multiple-choice questions, Single correct answer, Closed-book, Supervised online examination. Format shapes tactics — find the real question sentence inside long scenario stems before reading the details, and re-verify every selection on multi-answer items. Prep4SureReview's 0 practice questions exercise these formats until they feel like old acquaintances.

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