Artificial-Intelligence-Foundation Dumps Updated Feb 19, 2023 Practice Test and 40 unique questions
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NEW QUESTION 22
Splitting data into Training and Test data sets is part of what?
- A. High performance computing strategy.
- B. Batch learning.
- C. Machine learning post processing.
- D. Machine learning data preparation.
Answer: D
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 23
Para View allows large data sets to be visualised on a parallel computer.
Which of the following is one of the techniques used?
- A. Eigen function analysis.
- B. Norm calculation.
- C. Contour plot
- D. Dashboard.
Answer: C
Explanation:
Explanation
ParaView is an open-source, multi-platform visualization application that allows large data sets to be visualized on a parallel computer. ParaView uses a variety of techniques to visualize data, including contour plots, which are useful for visualizing 3D data sets. Contour plots are created by plotting a set of curves connecting points of equal value, with each curve representing a particular value. This allows 3D data sets to be visualized in a 2D format, making it easier to understand the data.
References: [1] BCS Foundation Certificate In Artificial Intelligence Study Guide, Page number 19 [2] APMG International, "What is ParaView?", https://apmg-international.com/en/blog/what-is-paraview/ [3] EXIN,
"What is ParaView?", https://www.exin.com/blog/what-is-paraview/
NEW QUESTION 24
Tensor flow is a typical open source what?
- A. Agent based modelling application
- B. Cloud based AI 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 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 25
What is defined as a machine that can carry out a complex series of tasks automatically?
- A. A robot
- B. An autonomous vehicle.
- C. A production line.
- D. A computer.
Answer: D
Explanation:
Explanation
https://en.wikipedia.org/wiki/Robot#:~:text=A%20robot%20is%20a%20machine,control%20may%20be%20em A computer is defined as a machine that can carry out a complex series of tasks automatically. Computers are used in a variety of applications, including artificial intelligence (AI), robotics, production lines, and autonomous vehicles. Computers are able to carry out complex tasks thanks to their ability to process large amounts of data quickly and accurately.
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 26
An intelligent robot uses Al to do what?
- A. Plan, act and speak.
- B. Sense, plan and move.
- C. Sense, plan and act
- D. Perceive, plan and act.
Answer: D
Explanation:
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.
NEW QUESTION 27
An agent based model is a simul-ation of autonomous agents (individual and collective). What can be used to learn from the data generated by the simul-ations?
- A. Paraview.
- B. Python.
- C. A spreadsheet
- D. Machine Learning.
Answer: D
Explanation:
Explanation
An agent based model is a simulation of autonomous agents (individual and collective). Machine learning can be used to learn from the data generated by the simulations. Machine learning algorithms can analyze the data generated by simulations and identify patterns, which can then be used to help the agent make decisions and take actions. References:
[1] BCS Foundation Certificate In Artificial Intelligence Study Guide, "Simulation and Modelling", p.101-104.
[2] APMG-International.com, "Foundations of Artificial Intelligence" [3] EXIN.com, "Foundations of Artificial Intelligence"
NEW QUESTION 28
Human-centric trustworthy Al must be...
- A. continually assessed and monitored.
- B. financially sustainable.
- C. quality assurance certified.
- D. tested by humans.
Answer: A
Explanation:
Explanation
Human-centric trustworthy Al must be continually assessed and monitored in order to ensure that it is behaving in a safe and ethical manner. This includes conducting regular tests and audits to ensure that the Al is functioning as intended, and is not taking any actions or decisions that could potentially harm humans or their environment. 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.
NEW QUESTION 29
The Scrum Master is part of which team?
- A. Data preparation team
- B. Agile project team.
- C. Management team
- D. Software development team.
Answer: B
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 30
What term do computer scientists and economists use to describe how happy an agent is?
- A. Warm.
- B. Return
- C. Utility.
- D. Index.
Answer: C
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 31
A vector in vector calculus is a quantity that has magnitude and direction.
What is a vector in computer programming?
- A. An array with one dimension.
- B. A constant
- C. An array of complex numbers
- D. A two-dimensional array of scalars.
Answer: A
Explanation:
Explanation
In computer programming, a vector is a data structure that contains a collection of elements that are all of the same type. Each element in the vector has an associated index, which can be used to access and modify the element at that index. Vectors are commonly used to store collections of numerical values (e.g., integers or floating-point numbers) or strings, but they can also be used to store any type of data.
References: [1] BCS Foundation Certificate In Artificial Intelligence Study Guide, Page number 36 [2] APMG International, "What is a Vector in Computer Programming?", https://apmg-international.com/en/blog/what-is-a-vector-in-computer-programming/ [3] EXIN, "What is a Vector in Computer Programming?", https://www.exin.com/blog/what-is-a-vector-in-computer-programming/
NEW QUESTION 32
Healthcare can benefit from Al, and in particular Machine Learning, an example of which is?
- A. Autonomous wheelchairs.
- B. Diagnostic image analysis
- C. Automated blood sampling.
- D. Autonomous vehicles.
Answer: B
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 33
How could machine learning make a robot autonomous?
- A. Learn from sensor data and plan to carry out a task.
- B. Use OCR, optical character recognition, to read documents
- C. Use actuators to modify its environment
- D. Use NLP (Natural Language Processing) to listen
Answer: A
Explanation:
Explanation
Machine learning can be used to make robots autonomous by allowing them to learn from sensor data and plan how to carry out a task. This involves using algorithms to analyze data from sensors and use this data to make decisions and take actions. By using machine learning, robots can learn from their environment and become more autonomous. References:
[1] BCS Foundation Certificate In Artificial Intelligence Study Guide, "Robotics", p.98. [2] APMG-International.com, "Foundations of Artificial Intelligence" [3] EXIN.com, "Foundations of Artificial Intelligence"
NEW QUESTION 34
Who was the pioneer of computer programming?
- A. Karen Spark Jones.
- B. Ada Lovelace.
- C. Sophie Wilson
- D. Dame Wendy Hall.
Answer: B
Explanation:
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
NEW QUESTION 35
Ensemble learning methods do what with the hypothesis space?
- A. Extract ergodic solutions.
- B. Select a combination of hypothesis to combine their predictions
- C. Use stochastic gradient descent to optimise a network.
- D. Test multiple hypotheses simultaneously.
Answer: B
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 36
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Boosting.
- B. Iteration.
- C. Over-fitting
- D. Activation.
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 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 37
An Al agent relies on its perceptual input. This is called the agent's what?
- A. Position
- B. World
- C. Environment
- 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 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 38
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