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BCS Foundation Certificate In Artificial Intelligence Sample Questions (Q14-Q19):

NEW QUESTION # 14
Professor David Chalmers described consciousness as having two questions. What were these?

• A. An easy one and a hard one.
• B. Can we integrate our knowledge to form consciousness and can we simulate consciousness?
• C. What is the sub conscious and what is the conscious?
• D. Are only humans conscious and are machines always unconscious?

Answer: D
NEW QUESTION # 15
The EU's Ethical Guidelines use what to demonstrate trustworthy Al?

• A. A quality assurance plan.
• B. UN's sustainability goals.
• C. A human-centric value system.
• D. Customer feedback.

Answer: B
NEW QUESTION # 16
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. Python.
• B. Machine Learning.
• C. A spreadsheet
• D. Paraview.

Answer: C Explanation:
https://www.pnas.org/doi/10.1073/pnas.082080899
NEW QUESTION # 17
Collaboration, learning and iterative are terms used to describe what?

• A. Trustworthy Al.
• B. Agile projects
• C. Waterfall projects.
• D. Rapid software development.

Answer: B 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 # 18
What function is used in a Neural Network?

• A. Trigonometric.
• B. Linear.
• C. Activation.
• D. Statistical.

Answer: C Explanation:
Explanation
Activation Functions
An activation function in a neural network defines how the weighted sum of the input is transformed into an output from a node or nodes in a layer of the network.
https://machinelearningmastery.com/choose-an-activation-function-for-deep-learning/#:~:text=An%20activation An activation function is a mathematical function used in a neural network to determine the output of a neuron. Activation functions are used to transform the inputs into an output signal and can range from simple linear functions to complex non-linear functions. Activation functions are an important part of neural networks and help the network learn patterns and generalize data. Types of activation functions include sigmoid, ReLU, tanh, and softmax. References: BCS Foundation Certificate In Artificial Intelligence Study Guide, https://bcs.org/certifications/foundation-certificates/artificial-intelligence/
NEW QUESTION # 19
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