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BCS AIF Exam Questions
- Topic 1: Recall that Ethical Purpose AI is delivered using Trustworthy AI that is technically robust/ Recall the general definition of Ethics
- Topic 2: 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 3: Describe agents in terms of performance measure, environment, actuators and sensors/ Recall the general definition of Human and Artificial Intelligence (AI)
- Topic 4: Describe four types of agent: reflex, model-based reflex, goal-based and utility-based/ Explain the benefits of Artificial Intelligence
- Topic 5: Demonstrate understanding of the risks of AI project/ Ethical and Sustainable Human and Artificial Intelligence
- Topic 6: Demonstrate understanding of the AI intelligent agent description/ Starting AI how to build a Machine Learning Toolbox - Theory and Practice
- Topic 7: Recall which typical, narrow AI capability is useful in ML and AI agents’ functionality/ The Management, Roles and Responsibilities of humans and machines
- Topic 8: Describe a ‘learning from experience’ Agile approach to projects/ Describe the type of team members needed for an Agile project
- Topic 9: 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 10: Describe how we learn from data – functionality, software and hardware/ Identify the relationship of AI agents with Machine Learning (ML)
- Topic 11: List common open source machine learning functionality, software and hardware/ Relate intelligent robotics to intelligent agents
- Topic 12: 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 13: Recall that the Human Centric Ethical Purpose Trustworthy AI is continually assessed and monitored/ Describe the difference between waterfall and agile projects
- Topic 14: List future directions of humans and machines working together/ Describe what are Ethics and Trustworthy AI, in particular
- Topic 15: Applying the benefits of AI - challenges and risks/ Describe the challenges of Artificial Intelligence
Free BCS AIF Exam Actual Questions
Note: Premium Questions for AIF were last updated On Nov. 17, 2024 (see below)
Healthcare can benefit from Al, and in particular Machine Learning, an example of which is?
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.
The EU and United Nations have made designing for all individuals a core principle. What is this type of design called?
In Machine learning what are a brain's axons called?
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.
Tensor flow is a typical open source what?
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.
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.
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