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BCS AIF Exam Syllabus Topics:

Topic	Details
Topic 1	Demonstrate understanding of the AI intelligent agent description Starting AI how to build a Machine Learning Toolbox - Theory and Practice
Topic 2	Identify a typical funding source for AI projects and relate to the NASA Technology Readiness Levels (TRLs) Describe a modern approach to Human logical levels of thinking using Robert Dilt’s Model
Topic 3	Understand that ML is a significant contribution to the growth of Artificial Intelligence Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution
Topic 4	Demonstrate understanding of the risks of AI project Ethical and Sustainable Human and Artificial Intelligence
Topic 5	Recall which typical, narrow AI capability is useful in ML and AI agents’ functionality The Management, Roles and Responsibilities of humans and machines
Topic 6	List future directions of humans and machines working together Describe what are Ethics and Trustworthy AI, in particular
Topic 7	Recall that Ethical Purpose AI is delivered using Trustworthy AI that is technically robust Recall the general definition of Ethics
Topic 8	Describe agents in terms of performance measure, environment, actuators and sensors Recall the general definition of Human and Artificial Intelligence (AI)
Topic 9	Recall that the Human Centric Ethical Purpose Trustworthy AI is continually assessed and monitored Describe the difference between waterfall and agile projects
Topic 10	Describe a ‘learning from experience’ Agile approach to projects Describe the type of team members needed for an Agile project
Topic 11	Describe four types of agent: reflex, model-based reflex, goal-based and utility-based Explain the benefits of Artificial Intelligence
Topic 12	Describe the three fundamental areas of sustainability and the United Nation’s seventeen sustainability goals General examples of the limitations of AI systems compared to human systems
Topic 13	Describe how we learn from data – functionality, software and hardware Identify the relationship of AI agents with Machine Learning (ML)
Topic 14	Applying the benefits of AI - challenges and risks Describe the challenges of Artificial Intelligence

BCS Foundation Certificate In Artificial Intelligence Sample Questions (Q19-Q24):

NEW QUESTION # 19
Who was the pioneer of computer programming?

• A. Karen Spark Jones.
• B. Sophie Wilson
• C. Dame Wendy Hall.
• D. Ada Lovelace.

Answer: D Explanation:
https://www.techopedia.com/2/31564/watercooler/ada-lovelace-enchantress-of-numbers
NEW QUESTION # 20
What function is used in a Neural Network?

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

Answer: A 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 # 21
What are monotonous and repetitive tasks, that require accuracy BEST suited to?

• A. Machine.
• B. Artificial General Intelligence.
• C. Human plus machine.
• D. Human.

Answer: A
NEW QUESTION # 22
What is one of the MAIN contributions of Al to the rapid development of The Fourth Industrial Revolution?

• A. Al personal assistants.
• B. Big Data
• C. Automation
• D. Enhanced design.

Answer: B Explanation:
Explanation
https://research.com/careers/what-is-the-fourth-industrial-revolution
NEW QUESTION # 23
Ensemble learning methods do what with the hypothesis space?

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

Answer: A 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 # 24
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