Most UptoDate BCS AIF Exam Dumps PDF 2023

100% Free Artificial intelligence (AI) AIF Dumps PDF Demo Cert Guide Cover

NEW QUESTION 19
Reflex andModel-based Reflex are two types of what?

• A. Algorithms.
• B. Robot
• C. Compilers.
• D. Artificial intelligent agents.

Answer: D

Explanation:
Explanation
Reflex and Model-based Reflex are two types of Artificial Intelligent Agents. Artificial Intelligent Agents are computer systems designed to act and think in a manner similar to humans, incorporating elements of problem solving, decision-making, communication, and learning. Reflex agents are reactive agents which act based on the current environment and conditions, while Model-based Reflex agents use a model of the environment to make decisions. 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 20
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?

• A. Over-fitting
• B. Boosting.
• C. Iteration.
• D. Activation.

Answer: B

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 wellknown 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 21
Human-centric trustworthy Al must be...

• A. financially sustainable.
• B. continually assessed and monitored.
• C. quality assurance certified.
• D. tested by humans.

Answer: C

 

NEW QUESTION 22
The EU and United Nations have made designing for all individuals a core principle. What is this type of design called?

• A. Universal design.
• B. Utopic design.
• C. Biophilic design.
• D. Core design

Answer: A

Explanation:
Explanation
https://universaldesign.ie/What-is-Universal-Design/
Universal design is a type of design that takes into account the needs of all individuals, regardless of age, ability, or physical condition. It is a principle that is embraced by the European Union and the United Nations, and it is based on the idea that products, services, and environments should be designed to be usable by the widest range of people possible. Universal design emphasizes accessibility, usability, and inclusivity, and it is often used to create products and services that are easy to use for people of all ages and abilities.
References: https://www.bcs.org/more/certifications/foundation-certificate-in-artificial-intelligence/ https://www

 

NEW QUESTION 23
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?

• A. Over-fitting
• B. Boosting.
• C. Iteration.
• D. Activation.

Answer: B

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/

 

NEW QUESTION 24
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. Machine Learning.
• D. A spreadsheet

Answer: C

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 25
In the 1800's the development of statistics led to___________theorem and is used in probabilistic inference.
(Select the missing word.)

• A. The central limit
• B. Boltzmann's
• C. Kolmogorov's
• D. Bayes'

Answer: C

 

NEW QUESTION 26
An Al agent relies on its perceptual input. This is called the agent's what?

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

Answer: D

Explanation:
Agent Terminology
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.
https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_agents_and_environments.htm

 

NEW QUESTION 27
Narrow or weak Al can be useful to robots.
Which of the following is an example of narrow Al?

• A. NLP - Natural LanguageProcessing.
• B. Artificial General Al.
• C. Conscious simul-ation.
• D. Conscious integration.

Answer: A

Explanation:
Explanation
NLP - Natural Language Processing is an example of narrow AI. It is a type of AI system that is able to understand, interpret, and generate natural language. NLP has become increasingly popular over the past few years, as it has been used to create applications such as chatbots, virtual assistants, and search engines. NLP systems are able to learn language and the context in which it is used, and they are able to understand the nuances of language and its different meanings. References: BCS Foundation Certificate In Artificial Intelligence Study Guide, https://bcs.org/certifications/foundation-certificates/artificial-intelligence/

 

NEW QUESTION 28
How could machine learning make a robot autonomous?

• A. Use NLP (Natural Language Processing) to listen
• B. Use actuators to modify its environment
• C. Learn from sensor data and plan to carry out a task.
• D. Use OCR, optical character recognition, to read documents

Answer: A

Explanation:
Explanation
https://arxiv.org/pdf/1803.10813

 

NEW QUESTION 29
What term do computer scientists and economists use to describe how happy an agent is?

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

Answer: D

Explanation:
https://griffinshare.fontbonne.edu/cgi/viewcontent.cgi?article=1008&context=ijds

 

NEW QUESTION 30
Sustainability focuses on which three core areas?

• A. Scientific, Environmental and Economic.
• B. Social, Economic and Entrepreneurial.
• C. Social, Economic and Environmental.
• D. Social, Entrepreneurial and Environmental.

Answer: C

Explanation:
The term sustainability is broadly used to indicate programs, initiatives and actions aimed at the preservation of a particular resource. However, it actually refers to four distinct areas: human, social, economic and environmental - known as the four pillars of sustainability.
https://www.futurelearn.com/info/courses/sustainable-business/0/steps/78337#:~:text=However%2C%20it%20actually%20refers%20to,the%20four%20pillars%20of%20sustainability.&text=Human%20sustainability%20aims%20to%20maintain%20and%20improve%20the%20human%20capital%20in%20society.

 

NEW QUESTION 31
If Al undertakes routine and monotonous tasks and takes these away from humans, what will humans do?

• A. Sabotage the Al.
• B. Leisure activities
• C. Change jobs.
• D. Higher value work.

Answer: D

 

NEW QUESTION 32
Professor David Chalmers described consciousness as having two questions. What were these?

• A. Are only humans conscious and are machines always unconscious?
• 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. An easy one and a hard one.

Answer: B

 

NEW QUESTION 33
Tensor flow is a typical open source what?

• A. Machine learning library.
• B. Agent based modelling application
• C. Intelligent robot paradigm.
• D. Cloud based AI application.

Answer: A

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

 

NEW QUESTION 34
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

Explanation:
Explanation
Monotonous and repetitive tasks that require accuracy are best suited to machines. Machines are able to accurately and quickly perform tasks that require little to no creativity, such as data entry or image recognition.
This is because machines are able to process large amounts of data quickly and accurately, and are less likely to make mistakes than humans. Additionally, machines are able to process large amounts of data without becoming bored or distracted, making them ideal for tasks that require consistent accuracy. For more information, please see the BCS Foundation Certificate In Artificial Intelligence Study Guide or the resources listed above.
Search results: BCS Foundation Certificate in Artificial Intelligence Study Guide, Chapter 4: Machine Learning: https://www.bcs.org/category/19669

 

NEW QUESTION 35
What is defined as a philosophy, or set of assumptions and/or techniques, which characterise an approach to a
class of problems?

• A. An algorithm.
• B. A set
• C. An approach.
• D. A paradigm.

Answer: D

 

NEW QUESTION 36
A vector in vector calculus is a quantity that has magnitude and direction.
What is a vector in computer programming?

• A. A two-dimensional array of scalars.
• B. An array of complex numbers
• C. A constant
• D. An array with one dimension.

Answer: A

 

NEW QUESTION 37
From the Ell's ethics guidelines for Al, what does 'The Principle of Autonomy,' mean?

• A. Robots will have freewill.
• B. Al systems will be human-centric
• C. Al systems will preserve human agency.
• D. Al agents will behave as humans.

Answer: B

 

NEW QUESTION 38
Narrow or weak Al can be useful to robots.
Which of the following is an example of narrow Al?

• A. Artificial General Al.
• B. NLP - Natural Language Processing.
• C. Conscious integration.
• D. Conscioussimul-ation.

Answer: B

 

NEW QUESTION 39
......

BCS AIF Exam Syllabus Topics:

Topic	Details
Topic 1	Describe how we learn from data – functionality, software and hardware Identify the relationship of AI agents with Machine Learning (ML)
Topic 2	Applying the benefits of AI - challenges and risks Describe the challenges of Artificial Intelligence
Topic 3	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 4	List future directions of humans and machines working together Describe what are Ethics and Trustworthy AI, in particular
Topic 5	Describe a ‘learning from experience’ Agile approach to projects Describe the type of team members needed for an Agile project
Topic 6	Recall that the Human Centric Ethical Purpose Trustworthy AI is continually assessed and monitored Describe the difference between waterfall and agile projects
Topic 7	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 8	Recall which typical, narrow AI capability is useful in ML and AI agents’ functionality The Management, Roles and Responsibilities of humans and machines
Topic 9	Describe agents in terms of performance measure, environment, actuators and sensors Recall the general definition of Human and Artificial Intelligence (AI)
Topic 10	List common open source machine learning functionality, software and hardware Relate intelligent robotics to intelligent agents
Topic 11	Recall that Ethical Purpose AI is delivered using Trustworthy AI that is technically robust Recall the general definition of Ethics

 

Updated BCS AIF Dumps – PDF & Online Engine: https://www.dumptorrent.com/AIF-braindumps-torrent.html

PDF Exam Material 2023 Realistic AIF Dumps Questions: https://drive.google.com/open?id=13C6Ya3lLvb8fl-K9RFu6sV5XeHs81ahZ