Machine learning models are prone to | CAT VARC Questions–Verbal Ability

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The CAT VARC section has grown increasingly difficult since 2015. In order to tackle the tougher CAT Level VARC questions for the CAT Exam, it is important to understand the basics of Reading Comprehension, Para Jumbles, Para summary, Para completion etc. To obtain a great CAT score, make use of MBAP Free Study material with detailed solutions and video explanations. Check out MBAP free Mock test to take these questions in a test format for free. The purpose of this page is to help you prepare for that. Take a crack at those VARC Section!

You may also find remaining question solution of CAT 2020, slot 3, by searching the question in the search bar.

The question below is from previous year CAT question from CAT 2020 exam comes from sub-section CAT Verbal Ability: Machine learning models are prone to. Find out by answering this question which tests an aspirant’s CAT VARC skills:

CAT 2020 – Slot -3-Verbal Ability - Question 26 - Machine learning models are prone to

Q. 26. Five jumbled up sentences, related to a topic, are given below. Four of them can be put together to form a coherent paragraph. Identify the odd one out and key in the number of the sentence as your answer:

A. Machine learning models are prone to learning human-like biases from the training data that feeds these algorithms.

B. Hate speech detection is part of the on-going effort against oppressive and abusive language on social media.

C. The current automatic detection models miss out on something vital: context.

D. It uses complex algorithms to flag racist or violent speech faster and better than human beings alone.

E. For instance, algorithms struggle to determine if group identifiers like “gay” or “black” are used in offensive or prejudiced ways because they’re trained on imbalanced datasets with unusually high rates of hate speech.

Show Answer

26. Correct Answer: C

If the sentences were to be arranged in a paragraph, B would be the best opening sentence as it sets the context. BD is a link: B talks about hate speech detection while D explains how it works. AE is also a link: sentence A talks about biases arising due to training data. E gives an example of how flawed training data affects algorithms. BDAE is a possible sequence. C is the odd one out.

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Machine learning models are prone to | CAT VARC Questions–Verbal Ability

CAT Quantitative Aptitude

CAT DILR

CAT Verbal Ability

CAT 2020 – Slot -3-Verbal Ability - Question 26 - Machine learning models are prone to

A. Machine learning models are prone to learning human-like biases from the training data that feeds these algorithms.

B. Hate speech detection is part of the on-going effort against oppressive and abusive language on social media.

C. The current automatic detection models miss out on something vital: context.

D. It uses complex algorithms to flag racist or violent speech faster and better than human beings alone.

E. For instance, algorithms struggle to determine if group identifiers like “gay” or “black” are used in offensive or prejudiced ways because they’re trained on imbalanced datasets with unusually high rates of hate speech.

Past Year Question Paper & Solutions

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