APMG International ARTIFICIAL-INTELLIGENCE-FOUNDATION Online Practice Questions and Exam Preparation

APMG International ARTIFICIAL-INTELLIGENCE-FOUNDATION Online Questions & Answers

• Question 1:

  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 be human-centric
  D. Al systems will preserve human agency.
  D. Al systems will preserve human agency.

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  explanation:

  Explanation/Reference:

  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.

• Question 2:

  Ensemble learning methods do what with the hypothesis space?

  A. Select a combination of hypothesis to combine their predictions
  B. Use stochastic gradient descent to optimise a network.
  C. Extract ergodic solutions.
  D. Test multiple hypotheses simultaneously.
  A. Select a combination of hypothesis to combine their predictions

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  explanation:

  Explanation/Reference:

  https://link.springer.com/referenceworkentry/10.1007/978-0-387-73003- 5_293#:~:text=Definition,and%20combine%20them%20to%20use. 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

• Question 3:

  Splitting data into Training and Test data sets is part of what?

  A. Machine learning data preparation.
  B. Batch learning.
  C. Machine learning post processing.
  D. High performance computing strategy.
  A. Machine learning data preparation.

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  explanation:

  Explanation/Reference:

  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/

• Question 4:

  Which of the following is an advantage of a machine based system?

  A. Able to judge ambiguous and unknown situations.
  B. Capable of sympathising with humans.
  C. Undertakes monotonous tasks reliably and accurately.
  D. Can explain the output of an Al system
  C. Undertakes monotonous tasks reliably and accurately.

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  explanation:

  Explanation/Reference:

  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/

• Question 5:

  What term do computer scientists and economists use to describe how happy an agent is?

  A. Index.
  B. Warm.
  C. Return
  D. Utility.
  D. Utility.

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  explanation:

  Explanation/Reference:

  https://griffinshare.fontbonne.edu/cgi/viewcontent.cgi?article=1008andcontext=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"

• Question 6:

  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
  C. Bayes'

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  explanation:

  Explanation/Reference:

  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-foundation-certificate-in-artificial-intelligence/

  EXIN: https://www.exin.com/en/certification/bcs-foundation-certificate-in-artificial- intelligence

• Question 7:

  What does TRL stand for?

  A. Technical Robotic Level.
  B. Transform Reinforced Learning
  C. Technology Readiness Level.
  D. Transport Ready Level.
  C. Technology Readiness Level.

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  explanation:

  Explanation/Reference:

  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,Technology%20Readiness%20Level%20(TRL),program%20during%20the%20acquisition %20process.

  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.com/en/qualifications-and-certifications/bc-foundation-certificate-in-artificial-intelligence/

  [3] https://www.exin.com/en/certifications/bc-foundation-certificate-in-artificial-intelligence/

  [4] https://www.acq.osd.mil/rd/nii/trl.html

• Question 8:

  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. Machine Learning.
  C. Python.
  D. A spreadsheet
  B. Machine Learning.

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  explanation:

  Explanation/Reference:

  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"

• Question 9:

  In Machine learning what are a brain's axons called?

  A. Dendrites
  B. Edges
  C. Tetrahedra.
  D. Nodes
  D. Nodes

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  explanation:

  Explanation/Reference:

  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).

• Question 10:

  An Al agent relies on its perceptual input. This is called the agent's what?

  A. Position
  B. Environment
  C. World
  D. Percept
  D. Percept

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  explanation:

  Explanation/Reference:

  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"