Which was first colour movie to win an Academy Award for Best Picture? ?->(Show Answer!)

1. Which was first colour movie to win an Academy Award for Best Picture?

Answer: Gone with the Wind

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MCQ-> Study the following information to answer the given questions : Eight people — L, M, 0, P, 9, R and S — are sitting around a circular table facing the centre. Each of them likes different colours, viz., Red Orange, Blue, Pink, Black, Purple, Brown and Green, but not necessarily in the same order. S is sitting second to the left of N. There are two persons between S and the person who likes Orange colour. M is second to the left of the person who likes Orange colour. L is the immediate neighbour of S. R is the third to the right of P. 0 likes Purple colour. The person who likes Pink colour is second to the right of P. The person who likes Brown colour is the third to the left of the person who likes Blue colour. Neither S nor P likes Brown colour. N likes neither Green nor Blue colour. L likes Red colour.Who among the following is second to the right of the person who likes Orange colour ?
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MCQ-> Study the following information to answer the given questions. A word and number arrangement machine when given an input line of words and numbers rearranges them following a particular rule in each step. The following is an illustration of input and rearrangement. ‘’(All the numbers are two digits numbers and are arranged as per some logic based on the value of the number)’’. Input : win 56 32 93 bat for 46 him 28 11 give chance. Step I : 93 56 32 bat for 46 him 28 11 give chance win Step II : 11 93 56 32 bat for 46 28 give chance win him Step III: 56 11 93 32 bat for 46 28 chance win him give Step IV: 28 56 11 93 32 bat 46 chance win him give for Step V: 46 28 56 11 93 32 bat win him give for chance Step V: 32 46 28 56 11 93 win him give for chance bat and Step VI is last step of the arrangement of the above input as the intended arrangement is obtained. As per the rules followed in the above steps, find out in each of the following questions the appropriate steps for the given input, Input for the questions: Input : ‘’fun 89 at the 28 16 base camp 35 53 here 68’’ (All the numbers given in the arrangement are two digit numbers.)Which of the following would be the Step II?
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MCQ-> Study the following information to answer the given questions: A word and number arrangement machine when given an input line of words and numbers rearranges them following a particular rule in each step. The following is an illustration of input and rearrangement.(All the numbers are two-digit numbers and are arranged as per some logic based on the value of the numbers.) Input:win 56 32 93 bat for 46 him 28 11 give chance Step I:93 56 32 bat for 46 him 28 11 give chance win StepII:11 93 56 32 bat for 46 28 give chance win him Step III:56 11 93 32 bat for 46 28 chance win him give Step IV:28 56 11 93 32 bat win him give for chance bat Step V:46 28 56 11 93 32 bat win him give for chance Step VI:32 46 28 56 11 93 win him give for chance bat Step VI is the last step of the arrangement the above input. Input for the question: Input:fun 89 at the 28 16 base camp 35 53 here 68 (All the number given in the arrangement are two digit numbers.)Which of the following would be step II ?
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MCQ-> Study the following information carefully and answer the question given below: A, M, P, J, H D and K are seven students of a school. They study in Std. III, IV, and V with at least two in any one standard.Each of them has different choice of colour from blue, red, green, yellow, black, white and brown not necessarily in the same order. M studies in Std. IV with only D who likes red colour. A studies in Std. V and does not like either blue or green. H does not study in Std. V and likes yellow colour. P and J study in the same Std. but not with A. None of these who study in Std.III likes white.The one who likes black studies in Std. IV. J likes brown colour. P does not like blue colour.Which colour does P like ?
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MCQ-> Read the passage carefully and answer the given questionsThe complexity of modern problems often precludes any one person from fully understanding them. Factors contributing to rising obesity levels, for example, include transportation systems and infrastructure, media, convenience foods, changing social norms, human biology and psychological factors. . . . The multidimensional or layered character of complex problems also undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is no best person. When putting together an oncological research team, a biotech company such as Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire people whose resumes score highest according to some performance criteria. Instead, they would seek diversity. They would build a team of people who bring diverse knowledge bases, tools and analytic skills. . . .Believers in a meritocracy might grant that teams ought to be diverse but then argue that meritocratic principles should apply within each category. Thus the team should consist of the ‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria applied to individuals will produce the best team. Each of these domains possesses such depth and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers were published last year covering various techniques, domains of enquiry and levels of analysis, ranging from molecules and synapses up through networks of neurons. Given that complexity, any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors in the 50-metre butterfly, must fail. What could be true is that given a specific task and the composition of a particular team, one scientist would be more likely to contribute than another. Optimal hiring depends on context. Optimal teams will be diverse.Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas tend to rank as high-impact. It can also be found in the structure of the so-called random decision forest, a state-of-the-art machine-learning algorithm. Random forests consist of ensembles of decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? A weighted majority rules. Random forests can serve many ends. They can identify bank fraud and diseases, recommend ceiling fans and predict online dating behaviour. When building a forest, you do not select the best trees as they tend to make similar classifications. You want diversity. Programmers achieve that diversity by training each tree on different data, a technique known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases - those that the current forest gets wrong. This ensures even more diversity and accurate forests.Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. Ranking people by common criteria produces homogeneity. . . . That’s not likely to lead to breakthroughs.Which of the following conditions, if true, would invalidate the passage’s main argument?
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