Banking Exam PCI202519 Welcome to your Banking Exam PCI202519 Name Email DIRECTIONS (Qs. 1-5) : Study the following graph to answer the given questions. 1. For Company A, what is the percent decrease in production from 2008 to 2009? (a) 75 (b) 50 (c) 25% (d) 10 (e) None of these None 2. In 2015, the production of Company B is approximately what per cent of that of in 2013? (a) 60 (b) 157 (c) 192 (d) 50 (e) 92 None 3. For Company A, in which year is the percentage increase/ decrease in the production from the previous year the highest? (a) 2014 (b) 2005 (c) 2012 (d) 2008 (e) None of these None 4. What is the difference in the total production of the two Companies for the given years? (a) 27,00,000 (b) 31,00,000 (c) 28,00,000 (d) 3,10,000 (e) None of these None 5. Which of the following is the closest average production in lakh units of Company B for the given years? (a) 4.1 (b) 3.5 (c) 4.3 (d) 3.7 (e) 3.9 None DIRECTIONS (Qs. 1-5): Answer the questions based on the following two graphs, assuming that there is not fixed cost component and all the units produced are sold in the same year. 1. In which year per unit cost in highest? (a) 2006 (b) 2005 (c) 2009 (d) 2011 (e) 2012 None 2. What is the approximate average quantity sold during the period 2004-2014? (a) 64 units (b) 70 units (c) 77 units (d) 81 units (e) 87 units None 3. (a) Price per unit has highest volatility (b) Cost per unit has highest volatility (c) Total profit has highest volatility (d) Revenue has highest volatility (e) None of the above None 4. If the price per unit decrease by 20% during 2004-2008 and cost per unit increase by 20% during 2009-2014, then duringhow many number of years there is loss? (a) 3 yr (b) 4 yr (c) 5 yr (d) 7 yr (e) None of these None 5. If the price per unit decrease by 20% during 2000-2004 and cost per unit increase by 20% during 2005-2010, then thecumulative profit for the entire period 2000-2010 decrease by (a) 1650 (b) 1550 (c) 1300 (d) 1250 (e) Data inadequate None DIRECTIONS (Qs. 1 -5): Study the following information carefully and answer the questions: Ten persons are sitting in 2 parallel rows containing 5 persons in each row. In 1st row A, B, C, D and E are seated and are facing south. In 2nd row, U, V, X, Y and Z are seated and are facing north. Therefore in the given seating arrangement, each member seated in a row faces another member of the other row. They like different colours Red, Orange, Blue, Brown, Black, White, Yellow, Pink, Peach, and Grey (not necessarily in same order). A doesn’t like brown and D likes black. Y sits third to the left of U, who likes yellow. A faces immediate neighbour of Y, who likes orange. The one who likes peach sits at extreme end. C sits second to the right of A. The one who likes red faces the one who likes pink but A doesn’t like pink. Only one person sits between B and D. V and Z are immediate neighbours. Z does not face A and B, who doesn’t like grey. The one who faces U likes white. The one who faces an immediate neighbour of Y likes brown.1. How many persons are seated between B and the one who likes white? (a) None (b) One (c) Two (d) Three (e) None of these None 2. Who amongst the following faces D? Ten persons are sitting in 2 parallel rows containing 5 persons in each row. In 1st row A, B, C, D and E are seated and are facing south. In 2nd row, U, V, X, Y and Z are seated and are facing north. Therefore in the given seating arrangement, each member seated in a row faces another member of the other row. They like different colours Red, Orange, Blue, Brown, Black, White, Yellow, Pink, Peach, and Grey (not necessarily in same order). A doesn’t like brown and D likes black. Y sits third to the left of U, who likes yellow. A faces immediate neighbour of Y, who likes orange. The one who likes peach sits at extreme end. C sits second to the right of A. The one who likes red faces the one who likes pink but A doesn’t like pink. Only one person sits between B and D. V and Z are immediate neighbours. Z does not face A and B, who doesn’t like grey. The one who faces U likes white. The one who faces an immediate neighbour of Y likes brown. (a) U (b) The one who likes pink (c) X (d) B (e) The one who likes grey None 3. Which of the following is true regarding A? Ten persons are sitting in 2 parallel rows containing 5 persons in each row. In 1st row A, B, C, D and E are seated and are facing south. In 2nd row, U, V, X, Y and Z are seated and are facing north. Therefore in the given seating arrangement, each member seated in a row faces another member of the other row. They like different colours Red, Orange, Blue, Brown, Black, White, Yellow, Pink, Peach, and Grey (not necessarily in same order). A doesn’t like brown and D likes black. Y sits third to the left of U, who likes yellow. A faces immediate neighbour of Y, who likes orange. The one who likes peach sits at extreme end. C sits second to the right of A. The one who likes red faces the one who likes pink but A doesn’t like pink. Only one person sits between B and D. V and Z are immediate neighbours. Z does not face A and B, who doesn’t like grey. The one who faces U likes white. The one who faces an immediate neighbour of Y likes brown. (a) B and X are immediate neighbours of A (b) A sits at one of the extreme ends of the line. (c) A likes black. (d) D sits immediate left of A (e) None of these None 4. Who amongst the following pair sits exactly in the middle of the rows? Ten persons are sitting in 2 parallel rows containing 5 persons in each row. In 1st row A, B, C, D and E are seated and are facing south. In 2nd row, U, V, X, Y and Z are seated and are facing north. Therefore in the given seating arrangement, each member seated in a row faces another member of the other row. They like different colours Red, Orange, Blue, Brown, Black, White, Yellow, Pink, Peach, and Grey (not necessarily in same order). A doesn’t like brown and D likes black. Y sits third to the left of U, who likes yellow. A faces immediate neighbour of Y, who likes orange. The one who likes peach sits at extreme end. C sits second to the right of A. The one who likes red faces the one who likes pink but A doesn’t like pink. Only one person sits between B and D. V and Z are immediate neighbours. Z does not face A and B, who doesn’t like grey. The one who faces U likes white. The one who faces an immediate neighbour of Y likes brown. (a) A, Z (b) D, Y (c) None of these (d) U, B (e) A, V None 5. V likes which of the following colour? Ten persons are sitting in 2 parallel rows containing 5 persons in each row. In 1st row A, B, C, D and E are seated and are facing south. In 2nd row, U, V, X, Y and Z are seated and are facing north. Therefore in the given seating arrangement, each member seated in a row faces another member of the other row. They like different colours Red, Orange, Blue, Brown, Black, White, Yellow, Pink, Peach, and Grey (not necessarily in same order). A doesn’t like brown and D likes black. Y sits third to the left of U, who likes yellow. A faces immediate neighbour of Y, who likes orange. The one who likes peach sits at extreme end. C sits second to the right of A. The one who likes red faces the one who likes pink but A doesn’t like pink. Only one person sits between B and D. V and Z are immediate neighbours. Z does not face A and B, who doesn’t like grey. The one who faces U likes white. The one who faces an immediate neighbour of Y likes brown. (a) Brown (b) Pink (c) Black (d) White (e) None of these None DIRECTIONS (Qs. 1-5): Study the following information carefully and answer the given questions. Twelve friends are sitting in two parallel rows at equal distance facing each other. A, B, C, D, E and F are sitting in Row 1 facing south. M, N, O, P, Q and R are sitting in Row 2 facing north (but not necessarily in the same order). R sits third to the right of M and one of them sits at the end of the row. A sits at the right end of the row. Three persons sit between A and D. Q sits to the immediate left of R. Two persons sit between Q and N. N who faces B sits to the immediate right of P. C faces O. E sits to the immediate left of C.1. Which of the following pair sits at the extreme ends of the Row 2? (a) M & P (b) R & P (c) M & N (d) R & N (e) None of these None 2. Who sits second to the left of the friend facing F? Twelve friends are sitting in two parallel rows at equal distance facing each other. A, B, C, D, E and F are sitting in Row 1 facing south. M, N, O, P, Q and R are sitting in Row 2 facing north (but not necessarily in the same order). R sits third to the right of M and one of them sits at the end of the row. A sits at the right end of the row. Three persons sit between A and D. Q sits to the immediate left of R. Two persons sit between Q and N. N who faces B sits to the immediate right of P. C faces O. E sits to the immediate left of C. (a) M (b) O (c) Q (d) N (e) R None 3. What is the position of E with respect to B? Twelve friends are sitting in two parallel rows at equal distance facing each other. A, B, C, D, E and F are sitting in Row 1 facing south. M, N, O, P, Q and R are sitting in Row 2 facing north (but not necessarily in the same order). R sits third to the right of M and one of them sits at the end of the row. A sits at the right end of the row. Three persons sit between A and D. Q sits to the immediate left of R. Two persons sit between Q and N. N who faces B sits to the immediate right of P. C faces O. E sits to the immediate left of C. (a) Third to the left (b) Second to the left (c) Second to the right (d) Third to the right (e) None of these None 4. Who is facing Q? Twelve friends are sitting in two parallel rows at equal distance facing each other. A, B, C, D, E and F are sitting in Row 1 facing south. M, N, O, P, Q and R are sitting in Row 2 facing north (but not necessarily in the same order). R sits third to the right of M and one of them sits at the end of the row. A sits at the right end of the row. Three persons sit between A and D. Q sits to the immediate left of R. Two persons sit between Q and N. N who faces B sits to the immediate right of P. C faces O. E sits to the immediate left of C. (a) D (b) E (c) A (d) C (e) None of these None 5. If the positions of all friends sitting in Row 2 are arranged as per the English alphabetical order from left to right, then who among the following faces M? Twelve friends are sitting in two parallel rows at equal distance facing each other. A, B, C, D, E and F are sitting in Row 1 facing south. M, N, O, P, Q and R are sitting in Row 2 facing north (but not necessarily in the same order). R sits third to the right of M and one of them sits at the end of the row. A sits at the right end of the row. Three persons sit between A and D. Q sits to the immediate left of R. Two persons sit between Q and N. N who faces B sits to the immediate right of P. C faces O. E sits to the immediate left of C. (a) A (b) B (c) C (d) D (e) None of these None DIRECTIONS (Qs. 1-7): Read the following passage and answer the questions that follow. In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors — agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It’s an idea with great potential: India has 30 million farmers with smart phones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a dataguzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like “deep learning”, attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. In fact, the lack of data means that deep learning doesn’t work for all companies in India. One example is Climate-Connect, a Delhi based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn’t worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. Another problem for AI firms today is finding the right people. NITI Aayog’s report has bleak news: only about 50 Indian scientists carry out “serious research” and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation. I. DepressingII. DismalIII. Congenial IV. Stark1. Which of the following is/are synonym/s of the word bleak? (a) Only III (b) Only I and III (c) Only I, II and IV (d) Only II, III and IV (e) Only I, II and III None 2. Which of the following is/are antonym/s of the word sparse? In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors — agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It’s an idea with great potential: India has 30 million farmers with smart phones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a dataguzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like “deep learning”, attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. In fact, the lack of data means that deep learning doesn’t work for all companies in India. One example is Climate-Connect, a Delhi based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn’t worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. Another problem for AI firms today is finding the right people. NITI Aayog’s report has bleak news: only about 50 Indian scientists carry out “serious research” and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation. I. Scant II. FewIII. Sporadic IV. Abundant (a) Only II (b) Only IV (c) Only I, II and IV (d) Only I, II and III (e) Only II, III and IV None 3. What can be some steps that can be taken by India to improve its AI capabilities? In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors — agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It’s an idea with great potential: India has 30 million farmers with smart phones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a dataguzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like “deep learning”, attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. In fact, the lack of data means that deep learning doesn’t work for all companies in India. One example is Climate-Connect, a Delhi based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn’t worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. Another problem for AI firms today is finding the right people. NITI Aayog’s report has bleak news: only about 50 Indian scientists carry out “serious research” and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation. I. The government must collect and digitize data it has access to due to running numerous schemes.II. Set up institutes to churn out more skilled people in this field.III. There should be adequate funding and also fixed deadlines to gauge performance. (a) Only I (b) Only III (c) Only I and II (d) Only II and III (e) All of the above None 4. Which of the following weakens the argument for using more of AI powered tools in the future in India? In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors — agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It’s an idea with great potential: India has 30 million farmers with smart phones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a dataguzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like “deep learning”, attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. In fact, the lack of data means that deep learning doesn’t work for all companies in India. One example is Climate-Connect, a Delhi based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn’t worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. Another problem for AI firms today is finding the right people. NITI Aayog’s report has bleak news: only about 50 Indian scientists carry out “serious research” and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation. I. The AI sector uses a tremendous amount of electricity so as to process huge amounts of datawhich is not sustainable.II. It is tough to collect, validate, standardize, correlate, archive and distribute AI-relevant data and make it accessible to organizations, people and systems.III. Although AI will create more jobs than it would destroy. (a) Only I (b) Only II (c) Only I and II (d) Only II and III (e) All of the above None 5. Which of the following statements weakens the argument about using ‘Open Libraries’ of machine learning code? In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors — agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It’s an idea with great potential: India has 30 million farmers with smart phones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a dataguzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like “deep learning”, attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. In fact, the lack of data means that deep learning doesn’t work for all companies in India. One example is Climate-Connect, a Delhi based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn’t worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. Another problem for AI firms today is finding the right people. NITI Aayog’s report has bleak news: only about 50 Indian scientists carry out “serious research” and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation. (a) They contain material that can be used to solve issues. (b) Using such libraries is not a difficult job and does not need a higher level of understanding of coding. (c) It is possible to do a respectable amount of machine learning without mathematics. (d) These are not helpful in cases where there is neither a fixed algorithm nor a standard procedure. (e) None of the above None 6. As per the passage, which of the following could be a/ some reason/s for AI to be full of ‘formidable obstacles’? In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors — agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It’s an idea with great potential: India has 30 million farmers with smart phones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a dataguzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like “deep learning”, attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. In fact, the lack of data means that deep learning doesn’t work for all companies in India. One example is Climate-Connect, a Delhi based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn’t worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. Another problem for AI firms today is finding the right people. NITI Aayog’s report has bleak news: only about 50 Indian scientists carry out “serious research” and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation. I. The need for a huge amount of data to make predictions.II. The entire chain of operation faces bottlenecks pertaining to fundingIII. A scarcity of adequately qualified people in India. (a) Only II (b) Only I and III (c) Only II and III (d) Only III (e) All of the above None 7. As per your understanding of the passage, which of the following can be said to be example/s of AI usage in Industries? In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors — agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It’s an idea with great potential: India has 30 million farmers with smart phones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a dataguzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like “deep learning”, attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. In fact, the lack of data means that deep learning doesn’t work for all companies in India. One example is Climate-Connect, a Delhi based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn’t worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. Another problem for AI firms today is finding the right people. NITI Aayog’s report has bleak news: only about 50 Indian scientists carry out “serious research” and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation. I. Data processing of students based on some parameters to find predictive patterns as to whowould quit.II. A Bank teller using computer to help solve customer queries with respect to their respective accounts.III. Use of unmanned tanks, vessels, aerial vehicles and drones in the armed forces. (a) Only I (b) Only II (c) Only I and III (d) Only II and III (e) All of the above None DIRECTIONS (Qs. 1-5): In the following passage, some of the words have been left out, each of which is indicated by a letter. Find the suitable word from the options given against each letter and fill up the blanks with appropriate words to make the paragraph meaningful. India is considering revising its foreign investment rules for e-commerce, three sources and a government spokesman told Reuters, a move that could ___ (A) players, including Amazon.com Inc., to restructure ties with some major sellers. The government discussions ____ (B) with a growing number of complaints from India’s bricks-and-mortar retailers, which have for years accused Amazon and Walmart Inc.-controlled Flipkart of creating complex structures to bypass federal rules, allegations the U.S. companies deny. India only allows foreign e-commerce players to operate as a marketplace to connect buyers and sellers. It ____ (C) them from holding inventories of goods and directly selling them on their platforms. Amazon and Walmart’s Flipkart were last hit in December 2018 by investment rule changes that ____(D) foreign e-commerce players from offering products from sellers in which they have an equity stake. Now, the government is considering adjusting some provisions to prevent those arrangements, even if the e-commerce firm holds an indirect stake in a seller through its parent, the sources said. The changes could ___ (E) Amazon as it holds indirect equity stakes in two of its biggest online sellers in India. 1. Which of the following words should fill in the blank (A) to make a contextually correct and meaningful sentence? (a) Temper (b) Intimate (c) Compel (d) Undertake (e) Assert None 2. India is considering revising its foreign investment rules for e-commerce, three sources and a government spokesman told Reuters, a move that could ___ (A) players, including Amazon.com Inc., to restructure ties with some major sellers. The government discussions ____ (B) with a growing number of complaints from India’s bricks-and-mortar retailers, which have for years accused Amazon and Walmart Inc.-controlled Flipkart of creating complex structures to bypass federal rules, allegations the U.S. companies deny. India only allows foreign e-commerce players to operate as a marketplace to connect buyers and sellers. It ____ (C) them from holding inventories of goods and directly selling them on their platforms. Amazon and Walmart’s Flipkart were last hit in December 2018 by investment rule changes that ____(D) foreign e-commerce players from offering products from sellers in which they have an equity stake. Now, the government is considering adjusting some provisions to prevent those arrangements, even if the e-commerce firm holds an indirect stake in a seller through its parent, the sources said. The changes could ___ (E) Amazon as it holds indirect equity stakes in two of its biggest online sellers in India. Which of the following words should fill in the blank (B) to make a contextually correct and meaningful sentence? (a) Despair (b) Justify (c) Manifest (d) Scale (e) Coincide None 3. India is considering revising its foreign investment rules for e-commerce, three sources and a government spokesman told Reuters, a move that could ___ (A) players, including Amazon.com Inc., to restructure ties with some major sellers. The government discussions ____ (B) with a growing number of complaints from India’s bricks-and-mortar retailers, which have for years accused Amazon and Walmart Inc.-controlled Flipkart of creating complex structures to bypass federal rules, allegations the U.S. companies deny. India only allows foreign e-commerce players to operate as a marketplace to connect buyers and sellers. It ____ (C) them from holding inventories of goods and directly selling them on their platforms. Amazon and Walmart’s Flipkart were last hit in December 2018 by investment rule changes that ____(D) foreign e-commerce players from offering products from sellers in which they have an equity stake. Now, the government is considering adjusting some provisions to prevent those arrangements, even if the e-commerce firm holds an indirect stake in a seller through its parent, the sources said. The changes could ___ (E) Amazon as it holds indirect equity stakes in two of its biggest online sellers in India. Which of the following words should fill in the blank (C) to make a contextually correct and meaningful sentence? (a) Prohibits (b) Persists (c) Pleads (d) Weighs (e) Inclines None 4. India is considering revising its foreign investment rules for e-commerce, three sources and a government spokesman told Reuters, a move that could ___ (A) players, including Amazon.com Inc., to restructure ties with some major sellers. The government discussions ____ (B) with a growing number of complaints from India’s bricks-and-mortar retailers, which have for years accused Amazon and Walmart Inc.-controlled Flipkart of creating complex structures to bypass federal rules, allegations the U.S. companies deny. India only allows foreign e-commerce players to operate as a marketplace to connect buyers and sellers. It ____ (C) them from holding inventories of goods and directly selling them on their platforms. Amazon and Walmart’s Flipkart were last hit in December 2018 by investment rule changes that ____(D) foreign e-commerce players from offering products from sellers in which they have an equity stake. Now, the government is considering adjusting some provisions to prevent those arrangements, even if the e-commerce firm holds an indirect stake in a seller through its parent, the sources said. The changes could ___ (E) Amazon as it holds indirect equity stakes in two of its biggest online sellers in India. Which of the following words should fill in the blank (D) to make a contextually correct and meaningful sentence? (a) Attributed (b) Exerted (c) Oppressed (d) Barred (e) Contended None 5. India is considering revising its foreign investment rules for e-commerce, three sources and a government spokesman told Reuters, a move that could ___ (A) players, including Amazon.com Inc., to restructure ties with some major sellers. The government discussions ____ (B) with a growing number of complaints from India’s bricks-and-mortar retailers, which have for years accused Amazon and Walmart Inc.-controlled Flipkart of creating complex structures to bypass federal rules, allegations the U.S. companies deny. India only allows foreign e-commerce players to operate as a marketplace to connect buyers and sellers. It ____ (C) them from holding inventories of goods and directly selling them on their platforms. Amazon and Walmart’s Flipkart were last hit in December 2018 by investment rule changes that ____(D) foreign e-commerce players from offering products from sellers in which they have an equity stake. Now, the government is considering adjusting some provisions to prevent those arrangements, even if the e-commerce firm holds an indirect stake in a seller through its parent, the sources said. The changes could ___ (E) Amazon as it holds indirect equity stakes in two of its biggest online sellers in India. Which of the following words should fill in the blank (E) to make a contextually correct and meaningful sentence? (a) Toil (b) Rail (c) Hurt (d) Boast (e) Lofty None Put the following sentences in order and make meaningful and coherent paragraph. A. The aroma of freshly baked bread wafted through the air, enticing passersby. B. Inside the bakery, rows of golden brown loaves sat nestled on display shelves. C. A small, family-owned bakery nestled on a quiet street corner. D. Unable to resist the tempting smell, I found myself drawn towards the bakery. E. The sight of warm, crusty bread proved too much to bear. What is the most logical order of the sentences? (a) C, A, D, B, E (b) C, B, A, D, E (c) A, C, B, D, E (d) B, C, A, D, E (e) E, A, C, B, D None Time's up