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BCS AIF (BCS Foundation Certificate In Artificial Intelligence) Certification Exam is a globally recognized certification designed to validate an individual's knowledge and skills in the field of Artificial Intelligence (AI). AIF exam is created and managed by the British Computer Society (BCS), a professional body for IT practitioners.
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NEW QUESTION # 18
With a large dataset, limited computational resources or frequent new data to learn from, we can adopt what type of machine learning?
Answer: B
Explanation:
NEW QUESTION # 19
Who was the pioneer of computer programming?
Answer: B
Explanation:
https://www.techopedia.com/2/31564/watercooler/ada-lovelace-enchantress-of-numbers
NEW QUESTION # 20
Narrow or weak Al can be useful to robots.
Which of the following is an example of narrow Al?
Answer: B
NEW QUESTION # 21
In an Al project the domain expert is the person...
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 # 22
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
Answer: C
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 # 23
......
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