Artificial-Intelligence-Foundation Review Guide | Artificial-Intelligence-Foundation Exam Dumps.zip by jihede6915 jihede6915

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The certification is awarded by APMG International, a leading global accreditation and training organization. The certification helps individuals and businesses to demonstrate their AI knowledge and understanding. The certification is aimed at individuals who wish to start or advance their career in AI, businesses who want to develop their AI capability, and individuals who want to gain a better understanding of AI concepts.

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This version is designed especially for those Artificial-Intelligence-Foundation test takers who cannot go through extensive APMG-International Artificial-Intelligence-Foundation practice sessions due to a shortage of time. Since the APMG-International Artificial-Intelligence-Foundation PDF file works on smartphones, laptops, and tablets, one can use APMG-International Artificial-Intelligence-Foundation dumps without limitations of place and time. Additionally, these APMG-International Artificial-Intelligence-Foundation PDF questions are printable as well.

The APMG-International Artificial-Intelligence-Foundation Exam is a computer-based exam that consists of 40 multiple-choice questions. The exam duration is 60 minutes, and candidates need to score a minimum of 65% to pass. The exam is available in multiple languages, and candidates can take it online or at a testing center. Upon passing the exam, individuals receive a digital certificate that demonstrates their knowledge and understanding of AI concepts, which can be used to advance their careers and pursue new opportunities in the field.

The Foundation Certification Artificial Intelligence Exam covers various topics, including AI programming fundamentals, machine learning, natural language processing, and robotics. The exam consists of 40 multiple-choice questions that need to be completed within 60 minutes. The passing score for this certification is 65%. Upon completion of the exam, individuals will receive a certificate that demonstrates their proficiency in AI and enhances their career prospects in the industry.

APMG-International Foundation Certification Artificial Intelligence Sample Questions (Q22-Q27):

NEW QUESTION # 22
If Al undertakes routine and monotonous tasks and takes these away from humans, what will humans do?

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

Answer: B

Explanation:
Explanation
Al is designed to take on routine and monotonous tasks, freeing up humans to take on more complex, higher value work. This can include tasks such as research, problem-solving, and decision-making. This shift in work roles is expected to increase productivity and efficiency, allowing humans to focus on more creative and innovative tasks. For example, robots can be used to automate mundane manufacturing processes, freeing up human workers to take on jobs that require more creative thinking and problem-solving.
References:
[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-international

NEW QUESTION # 23
What does TRL stand for?

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

Answer: C

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 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

NEW QUESTION # 24
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?

A. Iteration.B. Over-fittingC. Activation.D. Boosting.

Answer: D

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

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

Answer: C

Explanation:
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).

NEW QUESTION # 26
Who was the pioneer of computer programming?

A. Dame Wendy Hall.B. Karen Spark Jones.C. Sophie WilsonD. Ada Lovelace.

Answer: D

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/ https://www

NEW QUESTION # 27
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