Questões

Total de Questões Encontradas: 19

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TEXT II 

The backlash against big data 

 […]
Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voicerecognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data. 

The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem. 

There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers.
The base form, past tense and past participle of the verb “fall” in “The criticisms fall into three areas” are, respectively: 
A
fall-fell-fell
B
fall-fall-fallen
C
fall-fell-fallen
D
fall-falled-fell
E
fall-felled-falling
Ano: 2016 Banca: FGV Órgão: IBGE Prova: Análise de Sistemas - Desenvolvimento de Sistemas
Texto Associado Texto Associado
TEXT II 

The backlash against big data 

 […]
Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voicerecognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data. 

The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem. 

There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers. 
The base form, past tense and past participle of the verb “fall” in “The criticisms fall into three areas” are, respectively: 
A
fall-fell-fell
B
fall-fall-fallen
C
fall-fell-fallen
D
fall-falled-fell
E
fall-felled-falling
Ano: 2016 Banca: FGV Órgão: IBGE Prova: Análise de Sistemas - Suporte Operacional
Texto Associado Texto Associado
TEXT II 

The backlash against big data 

 […]
Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voicerecognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data. 

The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem. 

There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers. (From

http://www.economist.com/blogs/economist explains/201 4/04/economist-explains-10)
The base form, past tense and past participle of the verb “fall” in “The criticisms fall into three areas” are, respectively: 
A
fall-fell-fell
B
fall-fall-fallen
C
fall-fell-fallen
D
fall-falled-fell
E
fall-felled-falling
Ano: 2016 Banca: FGV Órgão: IBGE Prova: Tecnologista - Programação Visual - WebDesign
Texto Associado Texto Associado
TEXT II 

The backlash against big data 

 […] Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voicerecognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data. 

The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem. 

There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers.
(From http://www.economist.com/blogs/economist explains/201 4/04/economist-explains-10)
The base form, past tense and past participle of the verb “fall” in “The criticisms fall into three areas” are, respectively: 
A
 fall-fell-fell
B
fall-fall-fallen
C
fall-fell-fallen
D
 fall-falled-fell
E
fall-felled-falling
Ano: 2014 Banca: FUMARC Órgão: AL-MG Prova: Analista de Sistemas - Conhecimentos Básicos
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The passive voice of the sentence “It requires a sound engineering education,” is
A
A sound engineering education is required.
B
It is required a sound engineering education.
C
It will be required a sound engineering education.
D
A sound engineering education would be required.
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Based on the text above, judge the items below.
The results of the studies of vitamin E are yet inconclusive, as it is indicated by the use of the verb form “have found” (l.27).
C
Certo
E
Errado
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Read the text to answer 17, 18, 19 and 20.

        A man stepped onto the overnight train and told the conductor, “I need you to wake me up in Philadelphia. I’m a  deep sleeper and can be angry when I get up, but no matter what, I want you to help me make that stop. Here’s $100 to  make sure”.
        The conductor agreed. The man fell asleep, and when he awoke he heard the announcement that the train was  approaching New York, which meant they had passed Philadelphia a long time ago.  
        Furious, he ran to the conductor. “I gave you $100 to make sure I got off in Philadelphia, you idiot!”  
        “Wow,” another passenger said to his traveling companion. “Is that guy mad!”  
        “Yeah,” his companion replied. “But not half as mad as that guy they forced off the train in Philadelphia.”
In “ ...the train was approaching New York” a gerund is used as a/an 
A
verb.     
B
noun.  
C
article.   
D
adjective.   
E
quantifier. 
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Read the text to answer 17, 18, 19 and 20.

        A man stepped onto the overnight train and told the conductor, “I need you to wake me up in Philadelphia. I’m a  deep sleeper and can be angry when I get up, but no matter what, I want you to help me make that stop. Here’s $100 to  make sure”.
        The conductor agreed. The man fell asleep, and when he awoke he heard the announcement that the train was  approaching New York, which meant they had passed Philadelphia a long time ago.  
        Furious, he ran to the conductor. “I gave you $100 to make sure I got off in Philadelphia, you idiot!”  
        “Wow,” another passenger said to his traveling companion. “Is that guy mad!”  
        “Yeah,” his companion replied. “But not half as mad as that guy they forced off the train in Philadelphia.”
In “They had passed Philadelphia a long time ago” the verb tense is a:
A
 Past perfect.            
B
Simple past.             
C
Present perfect.
D
Past progressive.  
E
Conditional perfect. 
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Read the text to answer 17, 18, 19 and 20.

        A man stepped onto the overnight train and told the conductor, “I need you to wake me up in Philadelphia. I’m a  deep sleeper and can be angry when I get up, but no matter what, I want you to help me make that stop. Here’s $100 to  make sure”.
        The conductor agreed. The man fell asleep, and when he awoke he heard the announcement that the train was  approaching New York, which meant they had passed Philadelphia a long time ago.  
        Furious, he ran to the conductor. “I gave you $100 to make sure I got off in Philadelphia, you idiot!”  
        “Wow,” another passenger said to his traveling companion. “Is that guy mad!”  
        “Yeah,” his companion replied. “But not half as mad as that guy they forced off the train in Philadelphia.”
Choose the item that does NOT belong in the group. 
A
Fell.     
B
Heard.   
C
Meant.   
D
Replied.   
E
Awoke.  
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O verbo que preenche corretamente a lacuna [MODAL], no 1o parágrafo, é 
A
May. 
B
Will. 
C
Must. 
D
Could. 
E
Should.
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