Free Practice Questions for EXIN BCS Artificial Intelligence Foundation Certification

To build their understanding of AI, it is essential for candidates to be able to know the definitions of the key AI terms listed.

- Human intelligence – "The mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment." - Artificial Intelligence – "Intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals." - Machine learning – "The study of computer algorithms that allow computer programs to automatically improve through experience". - Scientific method – "An empirical method for acquiring knowledge that has characterized the development of science."

Candidates will be able to describe the events that took place to create these key milestones in the evolution of AI.

Asilomar principles are a set of guidelines for responsible AI development. The Dartmouth conference which took place in 1956, is considered to be the starting point of AI as a field of practice. Candidates should understand the concept of AI winters (from 1974-1980 and from 1987-1993) as well as the rise of big data and the development of generative AI.

Big data refers to the access to enormous amounts of data from a wide variety of sources, including social media, sensors, and other connected devices. Candidates should understand the widespread use of LLMs in 2022, which made AI a matter of public interest like never before.

Candidates will be able to describe the differences between narrow AI (weak AI) and general AI (strong AI).

They will be able to provide real-world examples to illustrate each type and explain their strengths and weaknesses for example, spam filtering, image recognition in medical diagnostics, generative AI.

Narrow AI (ANI), also known as weak AI, is task-specific and operates within well-defined domains. Examples include: - Image recognition: Identifying objects or patterns in images. - Speech recognition: Converting spoken language into text. - Language translation: Translating text from one language to another. - Virtual assistants like Siri or Alexa.

General AI (AGI) also known as strong AI aims to replicate human intelligence. It is the hypothetical intelligence of a machine that has the capacity to understand or learn any intellectual task that a human being can understand or learn.

Candidates should understand different sources of basic principles which guard AI development and use, such as; - Floridi & Cowls’ principles of beneficence, non- maleficence, autonomy, justice, and explicability. - AI UK principles of safety, security and robustness, transparency and explainability, fairness, accountability and governance, and contestability and redress.

Candidates should understand these guiding principles and be able to explain their impact in the ethical development and use of AI.

The world of AI is constantly changing, and the social, economic, and environmental impact is of growing concern.

Candidates will be able to outline some key aspects of the impact e.g. energy consumption (the AI industry, particularly generative AI systems, consumes vast amounts of energy), water usage (generative AI systems necessitate substantial water resources for cooling their processors and generating electricity), and job security, ways of working and need to develop new skills.

The development and running of AI can require significant computational power and consume substantial amounts of energy. Candidates should understand the environmental considerations of AI and the different measures that can be taken throughout the AI lifecycle to reduce its environmental impact.

AI offers huge opportunities however there are also commonly held ethical concerns about its increasingly widespread use.

Ethics relate to the moral principles that govern a person’s behavior or the conducting of an activity.

Candidates will be able to state the general definition of ethics, describe the differences between ethics and law, and describe the different areas of concern.

Guiding principles in ethical AI development work to ensure that AI technologies are designed and implemented responsibly.

AI governance is a set of practices to keep AI systems under control so that they remain safe and ethical e.g. policies and standards to adhere to in organizations, AI steering committees.

Candidates should understand these guiding principles and be able to describe their impact in the ethical development and use of AI.

Addressing ethical challenges in AI projects is crucial for ensuring responsible and trustworthy deployment. Ethical considerations should be integrated into every stage of AI development, from data collection to deployment with the use of guidelines and frameworks that address ethical concerns e.g. ethical risk framework.

Candidates will be able to identify the challenges to ethical behavior and the ways in which they can be minimized.

Regulation has an important role to play in the development and use of AI technology. It ensures there is clear legal accountability that governs its effective management.

Candidates will be able to explain the need for regulation, professional standards (ethical, accountable, competent, inclusive). They will understand the current and proposed regulations that will influence the continued development and use of AI in the UK and the EU.

Candidates will be able to identify risks, risk management techniques and risk mitigation strategies including the importance of minimizing risk, in relation to AI adoption.

They will be able to explain AI-related regulations and standards.

There are countless examples of AI in everyday life, and candidates should be able to recognize examples of and describe those listed.

Candidates should be able to state the definition of robots as stated and differentiate between intelligent and non-intelligent robots. They should explain that RPA refers to a machine that can carry out a complex series of tasks automatically, either with or without intelligence, usually with a goal of improving processes.

Various types of robots exist, and candidates should be familiar with each of these and what they are used for.

Candidates should understand that machine learning is a subset of AI.

AI itself is not a new concept; machine learning is another step in the evolution of AI. Machine learning is used within data science and is the application of algorithms to derive insight from data and big data.

Machine learning can be used in several contexts to complete different types of tasks. Candidates should be encouraged to explore different examples and applications of machine learning.

It is useful for candidates to have a basic understanding of the different types of approaches to machine learning to understand how it can be used to work with different types of data and where different algorithms are best used.

Supervised learning involves the application of an algorithm to labeled data to solve a problem, for example, classification, where we know what the output will be.

Unsupervised learning involves the application of an algorithm to unlabeled data to solve a problem, for example, clustering (grouping data based on similarities).

Semi-supervised learning involves the application of an algorithm where during the training of the algorithm we begin with a small amount of labeled data and then introduce a larger amount of unlabeled data.

Candidates should be able to identify and describe the key terminology listed.

Candidates should be able to describe the five characteristics of good-quality data and explain the importance of each. Good-quality data, which demonstrates all five of these characteristics, provides accurate information about its subject, and in turn, this helps to inform good decision making and reliable business intelligence. When poor-quality data is used to train AI, it can have a negative impact on the performance of the AI model, affecting user confidence.

Throughout the data lifecycle, there are various risks to consider, including how data is legally gathered and stored, to ensuring it is processed in line with its intended use, and is free from bias or misinformation.

Candidates should be aware of these risks and explain the use of mitigation measures listed. Risks are useful in helping AI to learn, using the scientific method of learning from experience. Candidates should have an awareness of the scientific method and how it relates to AI.

Big data is used to drive insight and improvement. Candidates should understand that through harnessing big data, organizations have huge insight into customer or user behavior and preferences, this can allow for targeted marketing and personalized experiences. Organizing and analyzing big data also supports in business decision making and process improvement, by helping organizations to understand more of the bigger picture.

Data visualization is required to format data in a manner which is meaningful and digestible to the intended audience. Good data visualization means that data can be consumed, analyzed, summarized, and used easily, which supports decision making.

Candidates should be able to describe the terms generative AI and large language model and identify them in use.

Generative AI models output text or images in response to a user prompt, or request.

LLMs are a generative AI tool, designed to generate a written response to a user query, in a way which mimics a human response. Candidates should understand that these models are trained using enormous volumes of data, which it uses to predict the most suitable word – chain of words – to respond to a user query. By using prompt engineering (designing a more specific, detailed request and building on it), a more specific or robust response can be generated.

The machine learning process allows us to define the solution based on the problem that has been identified through the process of data selection, pre-processing, visualization and testing of data with specific algorithms.

There is no de facto method within machine learning, learning through experience is vitally important. Testing involves creating the correct test data, creating bodies of data to learn from and parameters for what you wish to test.

Candidates should be able to identify simple opportunities for AI in an organization, such as an opportunity to automate a process, or minimize the human input into a repetitive task.

A business case would be required to provide insight and justification for undertaking a project and is used to secure funding.

A business case should contain each of these elements, providing decision makers with enough detail to evaluate the proposed recommendations.

Candidates should be familiar with this structure and the type of information which would be included in each section.

Identifying stakeholders is a key first step in stakeholder management, and the stakeholder wheel and PI grid can be used to appropriately categorize them. This is necessary to understand who has influence and input into a project and to ensure they have the appropriate level of management.

Candidates should be able to identify descriptions of stakeholders and the relevant categories.

Candidates should be able to describe the key characteristics of these project management approaches, their suitability for a given project and recognize them in use.

Candidates should be able to identify basic risks, costs and benefits of implementing an AI project or solution. It is necessary to identify and assess potential risks, to ensure suitable mitigation and owners are assigned, and to ensure the risks align with the organizations risk strategy.

A cost-benefit analysis is a systematic process that businesses use to analyze which decisions to make and which to forgo. The cost-benefit analysis sums the potential rewards expected from a situation or action and then subtracts the total costs associated with that action.

The three areas that governance must address are: - Compliance to satisfy regulations - Risk management to proactively detect and mitigate risk - Lifecycle governance to manage, monitor and govern AI models. (10 things governments should know about responsible AI, IBM 2024)

AI is a rapidly evolving field, and new roles emerge regularly.

Candidates will be able to describe the various career opportunities evolving in this field – they will not be assessed on the names or duties of specific job roles.

AI tools and services are now part of the real world.

Candidates will be able to describe practical examples of AI applications in different sectors e.g. AI-powered recommendation algorithms in entertainment, instantly converting a web page from a foreign language to your own, banks leveraging AI models to detect fraud, conduct audits and evaluate customers for loans, self-driving cars, chatbots, AI-powered digital assistants etc.

AI is evolving rapidly. This rapid technological advancement comes with benefits and challenges at societal level. Candidates should be able to explain these benefits and challenges and the impact on society. They should also be able to discuss the potential future of AI.

Benefits include reducing human error through task automation, processing and analyzing vast amounts of data for informed decisions (AI algorithms) and AI-powered tools in assistance in in medical diagnosis.

Challenges include ethical concerns about algorithm bias and privacy, job loss, lack of creativity and empathy, security risks from hacking, socio-economic inequality, market volatility because of AI-driven trading algorithms and AI systems rapid self-improvement.

Potential future advancements and direction of AI e.g. increased computing power, availability of more data, better algorithms, improved tools.

Artificial consciousness is consciousness hypothesized to be possible in artificial intelligence. Can AI have autonomous intentions and make conscious decisions, and how would this ability affect their ethical behavior?

Candidates should be able to describe the concept of consciousness and explain the difference between functional capabilities which may mimic consciousness, and genuine human consciousness. They should consider the impact and potential ethical implications of artificial consciousness being used in AI. Should people feel like they are interacting with a human when they are not?