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質問 # 15
An intelligent robot uses Al to do what?
- A. Sense, plan and move.
- B. Plan, act and speak.
- C. Perceive, plan and act.
- D. Sense, plan and act
正解：C
解説：
Explanation
An intelligent robot uses Artificial Intelligence (AI) to perceive its environment, plan its actions and then act on them. This is sometimes referred to as the "sense, plan, act" cycle, and is at the heart of what makes a robot intelligent. By using AI, robots can sense their environment, plan their actions accordingly and then act on them in order to complete their tasks.
For more information, please refer to the BCS Foundation Certificate in Artificial Intelligence Study Guide: https://www.bcs.org/category/18076/bcs-foundation-certificate-in-artificial-intelligence-study-guide.
質問 # 16
What is an intelligent robot?
- A. A robot that acts like a human.
- B. A robot that uses Al techniques.
- C. A robot that takes the place of a human.
- D. A robot that has consciousness
正解：B
解説：
Explanation
An intelligent robot is one that uses AI techniques, such as machine learning and natural language processing, to perceive, plan and act on its environment. Intelligent robots are able to process large amounts of data quickly and accurately, allowing them to make decisions and carry out tasks autonomously. Intelligent robots can be used in a variety of applications, from industrial automation to healthcare.
質問 # 17
Collaboration, learning and iterative are terms used to describe what?
- A. Waterfall projects.
- B. Agile projects
- C. Trustworthy Al.
- D. Rapid software development.
正解：B
解説：
Explanation
Collaboration, learning, and iterative are terms used to describe agile projects. Agile projects are designed to be adaptive and flexible, allowing teams to incorporate feedback and learn from their mistakes. This process encourages collaboration between team members, and emphasizes the importance of iterative development and continual improvement. Agile projects focus on delivering value quickly and efficiently, allowing teams to make changes and adapt to changing customer needs.
References:
[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-international
質問 # 18
Which of the following is an example of fitting a curve to a set of data?
- A. Python.
- B. Least squares regression.
- C. Bayesian network.
- D. Backward propagation.
正解：B
解説：
Explanation
Least Squares Regression is a statistical technique used for fitting a curve to a set of data. It involves minimizing the sum of the squares of the differences between the observed data and the fitted curve. This is done by finding the line of best fit, which is the line that minimizes the sum of the squared residuals. The line of best fit is determined by finding the parameters that give the minimum sum of the squared residuals. This technique is often used in data science and machine learning to create models that can be used to make predictions. References: BCS Foundation Certificate In Artificial Intelligence Study Guide, https://bcs.org/certifications/foundation-certificates/artificial-intelligence/
質問 # 19
What is defined as a philosophy, or set of assumptions and/or techniques, which characterise an approach to a class of problems?
- A. A paradigm.
- B. An algorithm.
- C. A set
- D. An approach.
正解：A
解説：
Explanation
A paradigm is defined as a philosophy, or set of assumptions and/or techniques, which characterise an approach to a class of problems. Paradigms are often used in Artificial Intelligence to provide a structure for problem solving, allowing for better understanding of the problem and providing a framework for developing a solution. For example, the logic-based approach is a paradigm that uses logical reasoning to solve problems.
For more information, please refer to the BCS Foundation Certificate in Artificial Intelligence Study Guide: https://www.bcs.org/category/18076/bcs-foundation-certificate-in-artificial-intelligence-study-guide.
質問 # 20
Narrow or weak Al can be useful to robots.
Which of the following is an example of narrow Al?
- A. Artificial General Al.
- B. Conscious simul-ation.
- C. Conscious integration.
- D. NLP - Natural Language Processing.
正解：D
解説：
Explanation
NLP - Natural Language Processing is an example of narrow AI. It is a type of AI system that is able to understand, interpret, and generate natural language. NLP has become increasingly popular over the past few years, as it has been used to create applications such as chatbots, virtual assistants, and search engines. NLP systems are able to learn language and the context in which it is used, and they are able to understand the nuances of language and its different meanings. References: BCS Foundation Certificate In Artificial Intelligence Study Guide, https://bcs.org/certifications/foundation-certificates/artificial-intelligence/
質問 # 21
If Al undertakes routine and monotonous tasks and takes these away from humans, what will humans do?
- A. Change jobs.
- B. Sabotage the Al.
- C. Leisure activities
- D. Higher value work.
正解：D
解説：
Explanation
Al is designed to take on routine and monotonous tasks, freeing up humans to take on more complex, higher value work. This can include tasks such as research, problem-solving, and decision-making. This shift in work roles is expected to increase productivity and efficiency, allowing humans to focus on more creative and innovative tasks. For example, robots can be used to automate mundane manufacturing processes, freeing up human workers to take on jobs that require more creative thinking and problem-solving.
References:
[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-international
質問 # 22
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Over-fitting
- B. Iteration.
- C. Boosting.
- D. Activation.
正解：C
解説：
Explanation
* Weak Learner: Colloquially, a model that performs slightly better than a naive model.
More formally, the notion has been generalized to multi-class classification and has a different meaning beyond better than 50 percent accuracy.
For binary classification, it is well known that the exact requirement for weak learners is to be better than random guess. [...] Notice that requiring base learners to be better than random guess is too weak for multi-class problems, yet requiring better than 50% accuracy is too stringent.
- Page 46, Ensemble Methods, 2012.
It is based on formal computational learning theory that proposes a class of learning methods that possess weakly learnability, meaning that they perform better than random guessing. Weak learnability is proposed as a simplification of the more desirable strong learnability, where a learnable achieved arbitrary good classification accuracy.
A weaker model of learnability, called weak learnability, drops the requirement that the learner be able to achieve arbitrarily high accuracy; a weak learning algorithm needs only output an hypothesis that performs slightly better (by an inverse polynomial) than random guessing.
- The Strength of Weak Learnability, 1990.
It is a useful concept as it is often used to describe the capabilities of contributing members of ensemble learning algorithms. For example, sometimes members of a bootstrap aggregation are referred to as weak learners as opposed to strong, at least in the colloquial meaning of the term.
More specifically, weak learners are the basis for the boosting class of ensemble learning algorithms.
The term boosting refers to a family of algorithms that are able to convert weak learners to strong learners.
https://machinelearningmastery.com/strong-learners-vs-weak-learners-for-ensemble-learning/ The best technique to adopt when a weak learner's hypothesis accuracy is only slightly better than 50% is boosting. Boosting is an ensemble learning technique that combines multiple weak learners (i.e., models with a low accuracy) to create a more powerful model. Boosting works by iteratively learning a series of weak learners, each of which is slightly better than random guessing. The output of each weak learner is then combined to form a more accurate model. Boosting is a powerful technique that has been proven to improve the accuracy of a wide range of machine learning tasks. For more information, please see the BCS Foundation Certificate In Artificial Intelligence Study Guide or the resources listed above.
質問 # 23
Reflex and Model-based Reflex are two types of what?
- A. Compilers.
- B. Algorithms.
- C. Robot
- D. Artificial intelligent agents.
正解：D
解説：
Explanation
Reflex and Model-based Reflex are two types of Artificial Intelligent Agents. Artificial Intelligent Agents are computer systems designed to act and think in a manner similar to humans,incorporating elements of problem solving, decision-making, communication, and learning. Reflex agents are reactive agents which act based on the current environment and conditions, while Model-based Reflex agents use a model of the environment to make decisions. References: BCS Foundation Certificate In Artificial Intelligence Study Guide, https://bcs.org/ai/certificate/ and APMG International, https://www.apmg-international.com/qualifications/artificial-intelligence-foundation-certificate.
質問 # 24
Ensemble learning methods do what with the hypothesis space?
- A. Use stochastic gradient descent to optimise a network.
- B. Select a combination of hypothesis to combine their predictions
- C. Extract ergodic solutions.
- D. Test multiple hypotheses simultaneously.
正解：B
解説：
Explanation
https://link.springer.com/referenceworkentry/10.1007/978-0-387-73003-5_293#:~:text=Definition,and%20comb It works by selecting different subsets of the data, or different combinations of the hypothesis, and combining the results of each prediction in order to create a single, more accurate result. This is useful in situations where different hypothesis may be accurate in different parts of the data, or where a single hypothesis may not be accurate in all cases. Ensemble learning is used in a variety of applications, from computer vision to natural language processing.
References: [1] BCS Foundation Certificate In Artificial Intelligence Study Guide, BCS [2] Apmg-international.com, "What is Ensemble Learning?", APMG International, https://apmg-international.com/en/about-apmg/blog/what-is-ensemble-learning/ [3] Exin.com,
"Ensemble Learning", EXIN, https://www.exin.com/en-us/learn/ensemble-learning
質問 # 25
Which of the following is an advantage of a machine based system?
- A. Undertakes monotonous tasks reliably and accurately.
- B. Capable of sympathising with humans.
- C. Able to judge ambiguous and unknown situations.
- D. Can explain the output of an Al system
正解：A
解説：
Explanation
One of the main advantages of a machine-based system is its ability to reliably and accurately undertake monotonous and repetitive tasks. This is especially useful for tasks that require a high level of accuracy and precision, such as data entry or analysis. Machine-based systems are also able to process large amounts of data quickly, meaning that they are able to complete tasks more quickly and efficiently than humans. Additionally, machine-based systems can be programmed to take certain decisions and actions based on the input data, allowing them to automate certain processes without the need for human intervention. References:
* BCS Foundation Certificate In Artificial Intelligence Study Guide (2019), AI Systems, Chapter 8.
* https://www.apmg-international.com/en/al-adoption/advantages-of-al/
質問 # 26
Which factor of a Waterfall' approach is most likely to result in the failed delivery of an Al project?
- A. Takes longer to complete the design phase of the project.
- B. Discourages collaboration and cross boundary communication.
- C. Discourages revisiting and revising any prior phase once it is complete.
- D. Takes longer to deliver all functional requirements.
正解：C
解説：
Explanation
The Waterfall approach is a sequential design process in which each phase of development must be completed before the next phase can begin. This means that once a phase is complete, it is difficult to go back and make changes, as any changes made to the project could potentially affect all the other phases. As a result, the Waterfall approach can make it difficult to adapt to changing customer requirements or adjust to new technology. This can ultimately lead to the failed delivery of an AI project.
References: [1] BCS Foundation Certificate In Artificial Intelligence Study Guide, Page number 19 [2] APMG International, "What is a Waterfall Model?", https://apmg-international.com/en/blog/what-is-a-waterfall-model/ [3] EXIN, "What is the Waterfall Model?", https://www.exin.com/blog/what-is-the-waterfall-model/
質問 # 27
In the 1800's the development of statistics led to___________theorem and is used in probabilistic inference.
(Select the missing word.)
- A. Boltzmann's
- B. Kolmogorov's
- C. Bayes'
- D. The central limit
正解：C
解説：
Explanation
The development of statistics in the 1800s led to the development of the Bayes' theorem, named after Reverend Thomas Bayes. This theorem is used in probabilistic inference, which is the process of using data to calculate the likelihood of a hypothesis or outcome. The theorem is used for determining the probability of an event occurring given its prior probability, as well as its associated conditions. The Bayes' theorem is also used in a variety of fields, such as machine learning, artificial intelligence, economics, and medical research.
Sources:
* BCS Foundation Certificate In Artificial Intelligence Study Guide: https://www.bcs.org/category/18071
* APMG
International: https://www.apmg-international.com/en/qualifications/qualification-resources/bcs-foundatio
* EXIN: https://www.exin.com/en/certification/bcs-foundation-certificate-in-artificial-intelligence
質問 # 28
Who was the pioneer of computer programming?
- A. Ada Lovelace.
- B. Karen Spark Jones.
- C. Sophie Wilson
- D. Dame Wendy Hall.
正解：A
解説：
Explanation
https://www.techopedia.com/2/31564/watercooler/ada-lovelace-enchantress-of-numbers Ada Lovelace was an English mathematician and writer who is widely credited as the pioneer of computer programming. In 1842, she wrote an article in which she outlined the fundamental principles of computing, making her the first person to recognize the potential of computers and to describe algorithms that could be used to program them. Her work laid the basis for modern computing and is recognized as one of the most significant contributions to the field of computing.
References: https://www.bcs.org/more/certifications/foundation-certificate-in-artificial-intelligence/ https://www
質問 # 29
From the Ell's ethics guidelines for Al, what does 'The Principle of Autonomy,' mean?
- A. Robots will have freewill.
- B. Al agents will behave as humans.
- C. Al systems will preserve human agency.
- D. Al systems will be human-centric
正解：C
解説：
Explanation
The Principle of Autonomy from the ELL's ethics guidelines for Al states that Al systems should be designed in a way that preserves human agency and responsibility. This means that Al systems should be designed in a way that allows humans to remain in control of their decisions, and that the Al system should not be able to act without human input or permission. References: BCS Foundation Certificate In Artificial Intelligence Study Guide, https://bcs.org/ai/certificate/ and APMG International, https://www.apmg-international.com/qualifications/artificial-intelligence-foundation-certificate.
質問 # 30
An Al agent relies on its perceptual input. This is called the agent's what?
- A. Percept
- B. Position
- C. Environment
- D. World
正解：A
解説：
Explanation
* Performance Measure of Agent It is the criteria, which determines how successful an agent is.
* Behavior of Agent It is the action that agent performs after any given sequence of percepts.
* Percept It is agent's perceptual inputs at a given instance.
* Percept Sequence It is the history of all that an agent has perceived till date.
* Agent Function It is a map from the precept sequence to an action.
Agent Terminology
https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_agents_and_environments.htm An AI agent relies on its perceptual input, which is referred to as the agent's percept. This is the data that the agent collects through its sensors about its environment. The percept allows the agent to make decisions and take actions based on its environment. The agent's percept is important for Artificial Intelligence systems to be able to operate effectively. References:
[1] BCS Foundation Certificate In Artificial Intelligence Study Guide, "Reinforcement Learning", p.96-97. [2] APMG-International.com, "Foundations of Artificial Intelligence" [3] EXIN.com, "Foundations of Artificial Intelligence"
質問 # 31
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