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Free Artificial intelligence (AI) AIF Official Cert Guide PDF Download
In addition to being a valuable certification for individuals, the BCS AIF exam is also beneficial for organizations that are looking to incorporate AI into their operations. By hiring individuals with this certification, organizations can be confident that they are bringing on team members who have a strong understanding of AI and can help to drive innovation and growth within the organization.
NEW QUESTION # 10
Ensemble learning methods do what with the hypothesis space?
- A. Test multiple hypotheses simultaneously.
- B. Extract ergodic solutions.
- C. Use stochastic gradient descent to optimise a network.
- D. Select a combination of hypothesis to combine theirpredictions
Answer: D
Explanation:
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
NEW QUESTION # 11
What does Prof David Chalmers describe the hard consciousness problem to be as comples as?
- A. Turbulence.
- B. Psychology.
- C. The universe.
- D. Quantum mechanics.
Answer: B
NEW QUESTION # 12
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. A set
- C. An approach.
- D. An algorithm.
Answer: A
NEW QUESTION # 13
Ensemble learning methods do what with the hypothesis space?
- A. Select a combination of hypothesis to combine their predictions
- B. Test multiple hypotheses simultaneously.
- C. Extract ergodic solutions.
- D. Use stochastic gradient descent to optimise a network.
Answer: A
Explanation:
https://link.springer.com/referenceworkentry/10.1007/978-0-387-73003-5_293#:~:text=Definition,and%20combine%20them%20to%20use.
NEW QUESTION # 14
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. A set
- C. An approach.
- D. An algorithm.
Answer: A
Explanation:
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.
NEW QUESTION # 15
From the Ell's ethics guidelines for Al, what does 'The Principle of Autonomy,' mean?
- A. Al agents will behave as humans.
- B. Robots will have freewill.
- C. Al systems will preserve human agency.
- D. Al systems will be human-centric
Answer: D
NEW QUESTION # 16
Tensor flow is a typical open source what?
- A. Cloud based AI application.
- B. Agent based modelling application
- C. Intelligent robot paradigm.
- D. Machine learning library.
Answer: D
Explanation:
Explanation
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible
ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and
developers easily build and deploy ML powered applications.
https://www.tensorflow.org/#:~:text=TensorFlow%20is%20an%20end%2Dto,and%20deploy%20ML%20power
NEW QUESTION # 17
Tensor flow is a typical open source what?
- A. Cloud based AI application.
- B. Agent based modelling application
- C. Intelligent robot paradigm.
- D. Machine learning library.
Answer: D
Explanation:
Explanation
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers pushthe state-of-the-art in ML and developers easily build and deploy ML powered applications.
https://www.tensorflow.org/#:~:text=TensorFlow%20is%20an%20end%2Dto,and%20deploy%20ML%20power TensorFlow is an open source machine learning library created by Google. It is used for dataflow programming and is widely used for a variety of applications, including machine learning and deep learning.
TensorFlow enables developers to build, train and deploy machine learning models easily and quickly. It has built-in support for a variety of deep learning frameworks, such as convolutional neural networks, recurrent neural networks, and autoencoders.
For more information, please refer to the BCS Foundation Certificate In Artificial Intelligence Study Guide (https://www.bcs.org/upload/pdf/bcs-foundation-certificate-in-artificial-intelligence-study-guide.pdf) or the EXIN Artificial Intelligence Foundation Certification (https://www.exin.com/en/exams/artificial-intelligence-foundation).
NEW QUESTION # 18
Healthcare can benefit from Al, and in particular Machine Learning, an example of which is?
- A. Automated blood sampling.
- B. Autonomous vehicles.
- C. Diagnostic image analysis
- D. Autonomous wheelchairs.
Answer: C
NEW QUESTION # 19
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Boosting.
- B. Iteration.
- C. Activation.
- D. Over-fitting
Answer: A
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/
NEW QUESTION # 20
Who was the pioneer of computer programming?
- A. Karen Spark Jones.
- B. Ada Lovelace.
- C. Sophie Wilson
- D. Dame Wendy Hall.
Answer: B
Explanation:
https://www.techopedia.com/2/31564/watercooler/ada-lovelace-enchantress-of-numbers
NEW QUESTION # 21
How could machine learning make a robot autonomous?
- A. Use actuators to modify its environment
- B. Use NLP (Natural Language Processing) to listen
- C. Use OCR, optical character recognition, to read documents
- D. Learn from sensor data and plan to carry out a task.
Answer: B
Explanation:
Explanation
https://arxiv.org/pdf/1803.10813
NEW QUESTION # 22
Sustainability focuses on which three core areas?
- A. Social, Economic and Entrepreneurial.
- B. Social, Entrepreneurial and Environmental.
- C. Social, Economic and Environmental.
- D. Scientific, Environmental and Economic.
Answer: C
Explanation:
The term sustainability is broadly used to indicate programs, initiatives and actions aimed at the preservation of a particular resource. However, it actually refers to four distinct areas: human, social, economic and environmental - known as the four pillars of sustainability.
https://www.futurelearn.com/info/courses/sustainable-business/0/steps/78337#:~:text=However%2C%20it%20actually%20refers%20to,the%20four%20pillars%20of%20sustainability.&text=Human%20sustainability%20aims%20to%20maintain%20and%20improve%20the%20human%20capital%20in%20society.
NEW QUESTION # 23
An Al agentrelies on its perceptual input.This is called the agent's what?
- A. Position
- B. Environment
- C. World
- D. Percept
Answer: D
Explanation:
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
NEW QUESTION # 24
Professor David Chalmers described consciousness as having two questions. What were these?
- A. Are only humans conscious and are machines always unconscious?
- B. What is the sub conscious and what is the conscious?
- C. An easy one and a hard one.
- D. Can we integrate our knowledge to form consciousness and can we simulate consciousness?
Answer: B
Explanation:
Explanation
Professor David Chalmers described consciousness as having two questions: "What is it like to be conscious?" and "Can machines be conscious?". The first question, "What is it like to be conscious?", is an attempt to understand what it is like to experience the subjective aspects of consciousness, such as feeling, emotion, and perception. The second question, "Can machines be conscious?", is an attempt to understand whether or not machines can have the same kinds of subjective experiences as humans. For more information, please see the BCS Foundation Certificate In Artificial Intelligence Study Guide or the resources listed above.
NEW QUESTION # 25
Splitting data into Training and Test data sets is part of what?
- A. High performance computing strategy.
- B. Machine learning data preparation.
- C. Machine learning post processing.
- D. Batch learning.
Answer: B
Explanation:
Explanation
Splitting data into training and test data sets is an important step in the machine learning data preparation process. This process involves splitting the data into subsets, usually in a 70:30 ratio, to create a training set and a test set. The training set is used to train the machine learning model, while the test set is used to evaluate the model's performance. This process allows for the model to be tested and evaluated on data that it has not seen before, in order to ensure that it is accurate and able to generalize to new data. References: BCS Foundation Certificate In Artificial Intelligence Study Guide, https://bcs.org/certifications/foundation-certificates/artificial-intelligence/
NEW QUESTION # 26
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Boosting.
- B. Iteration.
- C. Activation.
- D. Over-fitting
Answer: A
Explanation:
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 wellknown 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.
NEW QUESTION # 27
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.
Answer: C
Explanation:
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/
NEW QUESTION # 28
What are monotonous and repetitive tasks, that require accuracy BEST suited to?
- A. Human plus machine.
- B. Human.
- C. Machine.
- D. Artificial General Intelligence.
Answer: D
NEW QUESTION # 29
Healthcare can benefit from Al, and in particular Machine Learning, an example of which is?
- A. Automated blood sampling.
- B. Autonomous vehicles.
- C. Diagnostic image analysis
- D. Autonomous wheelchairs.
Answer: C
Explanation:
Explanation
Healthcare can benefit from AI, and in particular Machine Learning, in a number of ways. One example is diagnostic image analysis, which can help to automatically identify and classify abnormalities in medical images such as X-rays, CT scans, and MRI scans. Machine Learning algorithms can be used to detect patterns in the data which can be used to accurately diagnose diseases and illnesses.
References:
[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-international
NEW QUESTION # 30
Sustainability focuses on which three core areas?
- A. Social, Economic and Entrepreneurial.
- B. Social, Entrepreneurial and Environmental.
- C. Social, Economic and Environmental.
- D. Scientific, Environmental and Economic.
Answer: C
Explanation:
Explanation
The term sustainability is broadly used to indicate programs, initiatives and actions aimed at the preservation
of a particular resource. However, it actually refers to four distinct areas: human, social, economic and
environmental - known as the four pillars of sustainability.
https://www.futurelearn.com/info/courses/sustainable-business/0/steps/78337#:~:text=However%2C%20it%20ac
NEW QUESTION # 31
The EU and United Nations have made designing for all individuals a core principle. What is this type of design called?
- A. Biophilic design.
- B. Universal design.
- C. Utopic design.
- D. Core design
Answer: B
Explanation:
Explanation
https://universaldesign.ie/What-is-Universal-Design/
Universal design is a type of design that takes into account the needs of all individuals, regardless of age, ability, or physical condition. It is a principle that is embraced by the European Union and the United Nations, and it is based on the idea that products, services, and environments should be designed to be usable by the widest range of people possible. Universal design emphasizes accessibility, usability, and inclusivity, and it is often used to create products and services that are easy to use for people of all ages and abilities.
References: https://www.bcs.org/more/certifications/foundation-certificate-in-artificial-intelligence/ https://www
NEW QUESTION # 32
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