Question #1
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.
Correct Answer: C
Question #2
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 . Big Data learning.
- C . Patchwork learning.
- D . Online learning.
Correct Answer: A
Question #3
Tensor flow is a typical open source what?
- A . Cloud based AI application.
- B . Intelligent robot paradigm.
- C . Machine learning library.
- D . Agent based modelling application
Correct Answer: C
C
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%20powered%20applications.
C
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%20powered%20applications.
Question #4
Who was the pioneer of computer programming?
- A . Dame Wendy Hall.
- B . Karen Spark Jones.
- C . Ada Lovelace.
- D . Sophie Wilson
Correct Answer: C
C
Explanation:
https://www.techopedia.com/2/31564/watercooler/ada-lovelace-enchantress-of-numbers
C
Explanation:
https://www.techopedia.com/2/31564/watercooler/ada-lovelace-enchantress-of-numbers
Question #5
An agent based model is a simul-ation of autonomous agents (individual and collective).
What can be used to learn from the data generated by the simul-ations?
- A . Paraview.
- B . Machine Learning.
- C . Python.
- D . A spreadsheet
Correct Answer: D
D
Explanation:
https://www.pnas.org/doi/10.1073/pnas.082080899
D
Explanation:
https://www.pnas.org/doi/10.1073/pnas.082080899
Question #6
What is defined as a philosophy, or set of assumptions and/or techniques, which characterise an approach to a class of problems?
- A . An approach.
- B . A set
- C . A paradigm.
- D . An algorithm.
Correct Answer: C
Question #7
What is defined as a machine that can carry out a complex series of tasks automatically?
- A . A robot
- B . A production line.
- C . A computer.
- D . An autonomous vehicle.
Correct Answer: A
A
Explanation:
https://en.wikipedia.org/wiki/Robot#:~:text=A%20robot%20is%20a%20machine,control%20 may%20be%20embedded%20within.
A
Explanation:
https://en.wikipedia.org/wiki/Robot#:~:text=A%20robot%20is%20a%20machine,control%20 may%20be%20embedded%20within.
Question #8
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A . Over-fitting
- B . Activation.
- C . Iteration.
- D . Boosting.
Correct Answer: D
D
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/
D
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/
Question #9
From the Ell’s ethics guidelines for Al, what does ‘The Principle of Autonomy,’ mean?
- A . Robots will have freewill.
- B . Al agents will behave as humans.
- C . Al systems will be human-centric
- D . Al systems will preserve human agency.
Correct Answer: C
Question #10
Healthcare can benefit from Al, and in particular Machine Learning, an example of which is?
- A . Autonomous wheelchairs.
- B . Automated blood sampling.
- C . Autonomous vehicles.
- D . Diagnostic image analysis
Correct Answer: D