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NEW QUESTION 24 Which factor of a Waterfall' approach is most likely to result in the failed delivery of an Al project?

• A. Takes longer to deliver all functional requirements.
• B. Discourages collaboration and cross boundary communication.
• C. Takes longer to complete the design phase of the project.
• D. Discourages revisiting and revising any prior phase once it is complete.

Answer: C   NEW QUESTION 25 What is an intelligent robot?

• A. A robot that takes the place of a human.
• B. A robot that acts like a human.
• C. A robot that uses Al techniques.
• D. A robot that has consciousness

Answer: C   NEW QUESTION 26 Tensor flow is a typical open source what?

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

Answer: B 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 27 A human manipulates what using their intelligence?

• A. Environment
• B. Objective
• C. Mission
• D. Space

Answer: D   NEW QUESTION 28 What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?

• A. Over-fitting
• B. Iteration.
• C. 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/   NEW QUESTION 29 ......