2023 Valid AIF FREE EXAM DUMPS QUESTIONS & ANSWERS
Free AIF Exam Braindumps BCS Pratice Exam
BCS AIF certification exam covers a range of topics related to artificial intelligence, such as machine learning, natural language processing, robotics, and ethics. AIF exam is divided into two parts: a multiple-choice test and a practical assessment. The multiple-choice test assesses the candidate's theoretical understanding of AI, while the practical assessment tests their ability to apply AI techniques to real-world problems.
NEW QUESTION # 14
An Al agentrelies on its perceptual input.This is called the agent's what?
- A. World
- B. Percept
- C. Environment
- D. Position
Answer: B
Explanation:
Explanation
* 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
NEW QUESTION # 15
In an Al project the domain expert is the person...
- A. with technical and managerial oversight of the business plan
- B. with special knowledge or skills in the area of endeavour and defines what is fit for purpose'
- C. who measures the trustworthiness of the Al system
- D. who manages the agile project and writes the technical terms of reference
Answer: C
NEW QUESTION # 16
What are monotonous and repetitive tasks, that require accuracy BEST suited to?
- A. Human plus machine.
- B. Human.
- C. Artificial General Intelligence.
- D. Machine.
Answer: D
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 # 17
What does TRL stand for?
- A. Technical Robotic Level.
- B. Technology Readiness Level.
- C. Transform Reinforced Learning
- D. Transport Ready Level.
Answer: B
Explanation:
Explanation
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-,Technolog
NEW QUESTION # 18
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Boosting.
- B. Over-fitting
- C. Activation.
- D. Iteration.
Answer: A
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 # 19
Which of the following is an example of fitting a curve to a set of data?
- A. Bayesian network.
- B. Least squares regression.
- C. Backwardpropagation.
- D. Python.
Answer: B
Explanation:
Explanation
Least Squares Regression is a statistical technique used for fitting a curve to a set of data. It involves minimizing the sum of the squares of the differences between the observed data and the fitted curve. This is done by finding the line of best fit, which is the line that minimizes the sum of the squared residuals. The line of best fit is determined by finding the parameters that give the minimum sum of the squared residuals. This technique is often used in data science and machine learning to create models that can be used to make predictions. References: BCS Foundation Certificate In Artificial Intelligence Study Guide, https://bcs.org/certifications/foundation-certificates/artificial-intelligence/
NEW QUESTION # 20
In an Al project the domain expert is the person...
- A. with technical and managerial oversight of the business plan
- B. who manages the agile projectand writes the technical terms of reference
- C. with special knowledge or skills in the area of endeavour and defines what is fit for purpose'
- D. who measures the trustworthiness of the Al system
Answer: C
Explanation:
Explanation
In an AI project, a domain expert is a person with special knowledge or skills in that particular area of endeavour, and they are responsible for defining what is "fit for purpose" for the project. The domain expert provides insights into the problem and suggests ways to address it. They also provide guidance on evaluating and validating the AI system and its outputs. The domain expert is also responsible for communicating with stakeholders and providing feedback on the progress of the project. References:
* BCS Foundation Certificate In Artificial Intelligence Study Guide (2019), AI & People, Chapter 12.
* https://www.apmg-international.com/en/al-adoption/domain-expert/
NEW QUESTION # 21
If Al undertakes routine and monotonous tasks and takes these away from humans, what will humans do?
- A. Higher value work.
- B. Leisure activities
- C. Change jobs.
- D. Sabotage the Al.
Answer: C
NEW QUESTION # 22
Para View allows large data sets to be visualised on a parallel computer.
Which of the following is one of the techniques used?
- A. Norm calculation.
- B. Dashboard.
- C. Eigen function analysis.
- D. Contour plot
Answer: D
NEW QUESTION # 23
In Machine learning what are a brain's axons called?
- A. Dendrites
- B. Tetrahedra.
- C. Edges
- D. Nodes
Answer: D
Explanation:
Explanation
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).
NEW QUESTION # 24
Which factor of a Waterfall' approach is most likely to result in the failed delivery of an Al project?
- A. Discourages collaboration and cross boundary communication.
- B. Discourages revisiting and revising any prior phase once it is complete.
- C. Takes longer to deliver all functional requirements.
- D. Takes longer to complete the design phase of the project.
Answer: B
Explanation:
Explanation
The Waterfall approach is a sequential design process in which each phase of development must be completed before the next phase can begin. This means that once a phase is complete, it is difficult to go back and make changes, as any changes made to the project could potentially affect all the other phases. As a result, the Waterfall approach can make it difficult to adapt to changing customer requirements or adjust to new technology. This can ultimately lead to the failed delivery of an AI project.
References: [1] BCS Foundation Certificate In Artificial Intelligence Study Guide, Page number 19 [2] APMG International, "What is a Waterfall Model?", https://apmg-international.com/en/blog/what-is-a-waterfall-model/ [3] EXIN, "What is the Waterfall Model?", https://www.exin.com/blog/what-is-the-waterfall-model/
NEW QUESTION # 25
Ensemble learning methods do what with the hypothesis space?
- A. Use stochastic gradient descent to optimise a network.
- B. Select a combination of hypothesis to combine their predictions
- C. Test multiple hypotheses simultaneously.
- D. Extract ergodic solutions.
Answer: B
Explanation:
https://link.springer.com/referenceworkentry/10.1007/978-0-387-73003-5_293#:~:text=Definition,and%20combine%20them%20to%20use.
NEW QUESTION # 26
Who was the pioneer of computer programming?
- A. Karen Spark Jones.
- B. Ada Lovelace.
- C. Dame Wendy Hall.
- D. Sophie Wilson
Answer: B
Explanation:
Explanation
https://www.techopedia.com/2/31564/watercooler/ada-lovelace-enchantress-of-numbers
Ada Lovelace was an English mathematician and writer who is widely credited as the pioneer of computer programming. In 1842, she wrote an article in which she outlined the fundamental principles of computing, making her the first person to recognize the potential of computers and to describe algorithms that could be used to program them. Her work laid the basis for modern computing and is recognized as one of the most significant contributions to the field of computing.
References: https://www.bcs.org/more/certifications/foundation-certificate-in-artificial-intelligence/
NEW QUESTION # 27
Splitting data into Training and Test data sets is part of what?
- A. Machine learning data preparation.
- B. High performance computing strategy.
- C. Batch learning.
- D. Machine learning post processing.
Answer: A
Explanation:
Explanation
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/
NEW QUESTION # 28
How could machine learningmake a robot autonomous?
- A. Use OCR, optical character recognition, to read documents
- B. Learn from sensor data and plan to carry out a task.
- C. Use actuators to modify its environment
- D. Use NLP (Natural Language Processing) to listen
Answer: B
Explanation:
Explanation
Machine learning can be used to make robots autonomous by allowing them to learn from sensor data and plan how to carry out a task. This involves using algorithms to analyze data from sensors and use this data to make decisions and take actions. By using machine learning, robots can learn from their environment and become more autonomous. References:
[1] BCS Foundation Certificate In Artificial Intelligence Study Guide, "Robotics", p.98. [2] APMG-International.com, "Foundations of Artificial Intelligence" [3] EXIN.com, "Foundations of Artificial Intelligence"
NEW QUESTION # 29
Who was the pioneer of computer programming?
- A. Karen Spark Jones.
- B. Ada Lovelace.
- C. Dame Wendy Hall.
- D. Sophie Wilson
Answer: B
Explanation:
Explanation
https://www.techopedia.com/2/31564/watercooler/ada-lovelace-enchantress-of-numbers
NEW QUESTION # 30
The EU and United Nations have made designing for all individuals a core principle. What is this type of
design called?
- A. Utopic design.
- B. Biophilic design.
- C. Core design
- D. Universal design.
Answer: D
Explanation:
Explanation
https://universaldesign.ie/What-is-Universal-Design/
NEW QUESTION # 31
Human-centric trustworthy Al must be...
- A. financially sustainable.
- B. continually assessed and monitored.
- C. quality assurance certified.
- D. tested by humans.
Answer: B
Explanation:
Explanation
Human-centric trustworthy Al must be continually assessed and monitored in order to ensure that it is behaving in a safe and ethical manner. This includes conducting regular tests and audits to ensure that the Al is functioning as intended, and is not taking any actions or decisions that could potentially harm humans or their environment. 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 # 32
In the 1800's the development of statistics led to___________theorem and is used in probabilistic inference. (Select the missing word.)
- A. Bayes'
- B. Kolmogorov's
- C. The central limit
- D. Boltzmann's
Answer: B
NEW QUESTION # 33
The Scrum Master is part of which team?
- A. Management team
- B. Data preparation team
- C. Agile project team.
- D. Software development team.
Answer: C
Explanation:
Explanation
https://www.techtarget.com/whatis/definition/scrum-master#:~:text=A%20Scrum%20Master%20is%20a,in%20a
NEW QUESTION # 34
What is defined as a machine that can carry out a complex series of tasks automatically?
- A. A robot
- B. An autonomous vehicle.
- C. A production line.
- D. A computer.
Answer: A
Explanation:
Explanation
https://en.wikipedia.org/wiki/Robot#:~:text=A%20robot%20is%20a%20machine,control%20may%20be%20em
NEW QUESTION # 35
What is one of the MAIN contributions of Al to the rapid development of The Fourth Industrial Revolution?
- A. Enhanced design.
- B. Al personal assistants.
- C. Automation
- D. Big Data
Answer: D
Explanation:
Explanation
https://research.com/careers/what-is-the-fourth-industrial-revolution
NEW QUESTION # 36
An intelligent robot uses Al to do what?
- A. Sense, plan and act
- B. Plan, act and speak.
- C. Sense, plan and move.
- D. Perceive, plan and act.
Answer: A
NEW QUESTION # 37
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BCS AIF Certification Exam provides individuals with a comprehensive understanding of the fundamental concepts and principles of artificial intelligence. BCS Foundation Certificate In Artificial Intelligence certification program is globally recognized and is designed to equip individuals with the knowledge and skills required to succeed in the field of AI. Whether you are a professional or a student, the BCS AIF Certification Exam can help you build a successful career in this fast-growing field.
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