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최신 Artificial Intelligence Artificial-Intelligence-Foundation 무료샘플문제 (Q21-Q26):

질문 # 21
What is defined as a philosophy, or set of assumptions and/or techniques, which characterise an approach to a class of problems?

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

정답：A 설명：
Explanation
A paradigm is defined as a philosophy, or set of assumptions and/or techniques, which characterise an approach to a class of problems. Paradigms are often used in Artificial Intelligence to provide a structure for problem solving, allowing for better understanding of the problem and providing a framework for developing a solution. For example, the logic-based approach is a paradigm that uses logical reasoning to solve problems.
For more information, please refer to the BCS Foundation Certificate in Artificial Intelligence Study Guide: https://www.bcs.org/category/18076/bcs-foundation-certificate-in-artificial-intelligence-study-guide.
질문 # 22
What does TRL stand for?

A. Technical Robotic Level.
B. Transform Reinforced Learning
C. Technology Readiness Level.
D. Transport Ready Level.

정답：C 설명：
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 TRL stands for Technology Readiness Level and is a measure of how close a technology is to being ready for use in a real-world environment. TRL is used to assess the progress of research and development of a technology, ranging from basic research (TRL 1) to fully operational (TRL 9). TRL is used to help determine the level of completion of a technology and its potential success in a real-world environment.
References:
[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-international
질문 # 23
What is defined as a machine that can carry out a complex series of tasks automatically?

A. An autonomous vehicle.
B. A computer.
C. A production line.
D. A robot

정답：B 설명：
Explanation
https://en.wikipedia.org/wiki/Robot#:~:text=A%20robot%20is%20a%20machine,control%20may%20be%20em A computer is defined as a machine that can carry out a complex series of tasks automatically. Computers are used in a variety of applications, including artificial intelligence (AI), robotics, production lines, and autonomous vehicles. Computers are able to carry out complex tasks thanks to their ability to process large amounts of data quickly and accurately.
For more information, please refer to the BCS Foundation Certificate in Artificial Intelligence Study Guide: https://www.bcs.org/category/18076/bcs-foundation-certificate-in-artificial-intelligence-study-guide.
질문 # 24
Ensemble learning methods do what with the hypothesis space?

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

정답：D 설명：
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
질문 # 25
With a large dataset, limited computational resources or frequent new data to learn from, we can adopt what type of machine learning?

A. Big Data learning.
B. Patchwork learning.
C. Online learning.
D. Batch learning.

정답：C 설명：
Explanation

Online learning is a type of machine learning that can be used when a large dataset is limited in computational resources or if the data is frequently changing. It allows the system to learn from new data as it is being presented, rather than having to re-train the entire dataset each time new data is added. This makes it more efficient and effective than batch learning, as it only needs to process the new data and not the entire dataset.
Online learning is often used in applications such as fraud detection, where new data is constantly being added and needs to be analyzed quickly.
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).
질문 # 26
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시험대비 Artificial-Intelligence-Foundation퍼펙트 덤프샘플 다운로드 최신 덤프공부

APMG-International인증 Artificial-Intelligence-Foundation시험을 통과하여 자겨증취득하는 꿈에 더욱 가까이 다가가세요, 보통은APMG-International인증Artificial-Intelligence-Foundation시험을 넘기 위해서는 많은 시간과 신경이 필요합니다, APMG-International 인증Artificial-Intelligence-Foundation덤프 무료샘플을 다운받아 체험해보세요. 더 늦기전에 Artificial-Intelligence-Foundation 덤프로 시험패스하여 다른 분들보다 한걸음 빠르게 자격증을 취득하지 않으실래요?

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최신 Artificial-Intelligence-Foundation퍼펙트 덤프샘플 다운로드 시험덤프자료

최신 Artificial Intelligence Artificial-Intelligence-Foundation 무료샘플문제 (Q21-Q26):

시험대비 Artificial-Intelligence-Foundation퍼펙트 덤프샘플 다운로드 최신 덤프공부