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APMG-International Artificial-Intelligence-Foundation Certification Exam covers a range of topics related to artificial intelligence, including machine learning, deep learning, natural language processing, and robotics. Artificial-Intelligence-Foundation exam is designed to test an individual's understanding of the basic concepts and principles of these topics, as well as their ability to apply this knowledge in real-world scenarios. Artificial-Intelligence-Foundation exam is also designed to test an individual's ability to identify the potential benefits and risks associated with the use of AI technologies.
NEW QUESTION # 21
What does TRL stand for?
- A. Technology Readiness Level.
- B. Transform Reinforced Learning
- C. Technical Robotic Level.
- D. Transport Ready Level.
Answer: A
Explanation:
Explanation
Technology Readiness Level (TRL) Technology Readiness Levels (TRL) are a method of estimating the technology maturity of Critical Technology Elements (CTE) of a program during the acquisition process.
https://acqnotes.com/acqnote/tasks/technology-readiness-level#:~:text=Technology%20Development-,Technolog TRL stands for Technology Readiness Level and is a measure of how close a technology is to being ready for use in a real-world environment. TRL is used to assess the progress of research and development of a technology, ranging from basic research (TRL 1) to fully operational (TRL 9). TRL is used to help determine the level of completion of a technology and its potential success in a real-world environment.
References:
[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-international
NEW QUESTION # 22
Which of the following is an example of fitting a curve to a set of data?
- A. Least squares regression.
- B. Bayesian network.
- C. Backward propagation.
- D. Python.
Answer: A
Explanation:
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/
NEW QUESTION # 23
Tensor flow is a typical open source what?
- A. Agent based modelling application
- B. Machine learning library.
- C. Cloud based AI application.
- D. Intelligent robot paradigm.
Answer: B
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 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 # 24
What is an intelligent robot?
- A. A robot that uses Al techniques.
- B. A robot that takes the place of a human.
- C. A robot that acts like a human.
- D. A robot that has consciousness
Answer: A
Explanation:
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.
NEW QUESTION # 25
An Al agent relies on its perceptual input. This is called the agent's what?
- A. Position
- B. World
- C. Percept
- D. Environment
Answer: C
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 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"
NEW QUESTION # 26
What term do computer scientists and economists use to describe how happy an agent is?
- A. Utility.
- B. Return
- C. Index.
- D. Warm.
Answer: A
Explanation:
Explanation
https://griffinshare.fontbonne.edu/cgi/viewcontent.cgi?article=1008&context=ijds Computer scientists and economists use the term "utility" to describe how happy an agent is. Utility is a measure of satisfaction or preference, and it is used to evaluate an agent's satisfaction with a particular outcome. Utility can be used to determine the optimal decision or action for an agent to take in order to maximize its satisfaction. References:
[1] BCS Foundation Certificate In Artificial Intelligence Study Guide, "Decision Making and Planning", p.99-100. [2] APMG-International.com, "Foundations of Artificial Intelligence" [3] EXIN.com, "Foundations of Artificial Intelligence"
NEW QUESTION # 27
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Over-fitting
- B. Boosting.
- C. Iteration.
- D. Activation.
Answer: B
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 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.
NEW QUESTION # 28
The Scrum Master is part of which team?
- A. Data preparation team
- B. Software development team.
- C. Agile project team.
- D. Management team
Answer: C
Explanation:
Explanation
https://www.techtarget.com/whatis/definition/scrum-master#:~:text=A%20Scrum%20Master%20is%20a,in%20a The Scrum Master is part of the agile project team, and is responsible for ensuring that the team is following the Scrum process. The Scrum Master is the facilitator of the team, ensuring that the team is working together and following the Scrum principles. They are also responsible for protecting the team from any external influences and helping resolve any issues that may arise.
References:
[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-international
NEW QUESTION # 29
With a large dataset, limited computational resources or frequent new data to learn from, we can adopt what type of machine learning?
- A. Big Data learning.
- B. Online learning.
- C. Batch learning.
- D. Patchwork learning.
Answer: B
Explanation:
Explanation
Online learning is a type of machine learning that can be used when a large dataset is limited in computational resources or if the data is frequently changing. It allows the system to learn from new data as it is being presented, rather than having to re-train the entire dataset each time new data is added. This makes it more efficient and effective than batch learning, as it only needs to process the new data and not the entire dataset.
Online learning is often used in applications such as fraud detection, where new data is constantly being added and needs to be analyzed quickly.
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 # 30
Professor David Chalmers described consciousness as having two questions. What were these?
- A. What is the sub conscious and what is the conscious?
- B. An easy one and a hard one.
- C. Are only humans conscious and are machines always unconscious?
- D. Can we integrate our knowledge to form consciousness and can we simulate consciousness?
Answer: A
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 # 31
Collaboration, learning and iterative are terms used to describe what?
- A. Waterfall projects.
- B. Rapid software development.
- C. Trustworthy Al.
- D. Agile projects
Answer: D
Explanation:
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
NEW QUESTION # 32
In Machine learning what are a brain's axons called?
- A. Nodes
- B. Tetrahedra.
- C. Dendrites
- D. Edges
Answer: A
Explanation:
Explanation
In Machine Learning, the brain's axons are referred to as nodes. Nodes are the components of a neural network that are responsible for processing the input data and generating the output. A node is a mathematical function that takes input data, performs a computation on it, and produces an output. Each node is connected to other nodes in the network via edges, which represent the strength of the connection between the respective nodes. The strength of the connection between two nodes is determined by the weights assigned to each edge.
The weights are adjusted during the training process to generate the desired results.
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 # 33
What is defined as a philosophy, or set of assumptions and/or techniques, which characterise an approach to a class of problems?
- A. An algorithm.
- B. A set
- C. An approach.
- D. A paradigm.
Answer: D
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 # 34
Splitting data into Training and Test data sets is part of what?
- A. High performance computing strategy.
- B. Batch learning.
- C. Machine learning data preparation.
- D. Machine learning post processing.
Answer: C
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 # 35
If Al undertakes routine and monotonous tasks and takes these away from humans, what will humans do?
- A. Leisure activities
- B. Higher value work.
- C. Sabotage the Al.
- D. Change jobs.
Answer: B
Explanation:
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
NEW QUESTION # 36
The EU's Ethical Guidelines use what to demonstrate trustworthy Al?
- A. A quality assurance plan.
- B. UN's sustainability goals.
- C. Customer feedback.
- D. A human-centric value system.
Answer: D
Explanation:
Explanation
The European Union's Ethical Guidelines for Trustworthy AI use a human-centric value system to demonstrate that Artificial Intelligence (AI) is trustworthy. This value system is based on human rights, autonomy, safety, privacy, transparency, accountability and fairness. The guidelines also state that AI should be designed, developed and used in a manner that respects these values. References:
* https://ec.europa.eu/digital-single-market/en/news/ethical-guidelines-trustworthy-ai
* BCS Foundation Certificate In Artificial Intelligence Study Guide (2019), A.I & Ethics, Chapter 5.
NEW QUESTION # 37
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