Questões

Total de Questões Encontradas: 88

Ano: 2017 Banca: FEPESE Órgão: CIASC Prova: Analista - Analista de Sistemas (Desenvolvedor)
Match the words on column 1 with their meanings on column 2.

Column 1 Words

   1. hay
   2. waste
   3. rear
   4. burp
   5. cattle
Column 2 Meanings

( ) the back part of something.
( ) cows and bulls that are kept for their milk or meat.
( ) grass that is cut and dried and used as animal food.
( ) unwanted matter or material of any type.
( ) to allow air from the stomach to come out through the mouth in anoisy way.

Choose the alternative which presents the correct sequence:
A
1 • 4 • 5 • 3 • 2
B
2 • 1 • 5 • 4 • 3
C
3 • 5 • 1 • 2 • 4
D
4 • 3 • 2 • 1 • 5
E
5 • 1 • 4 • 2 • 3
Ano: 2017 Banca: FEPESE Órgão: CIASC Prova: Analista - Analista de Informática e Suporte
Texto Associado Texto Associado
Cow Threat
Cows are walking machines. They transform materials
(grass, hay, water, and feed) into finished products
(milk, beef, leather, and so on).
As any factory, cows produce waste. Solid waste is
eliminated through the rear end of these ‘complex
machines’, and it is used as fertilizer.
The fermentation process in their four stomachs produces
gas. These walking machines have two chimneys:
one in the front end, and other in the rear end.
Gaseous emissions through the front end chimney are
called burps. Cows burp a lot. Every minute and half
these burps release methane gas. Methane is dangerous
to the planet because it contributes to the greenhouse
effect.
The world population is growing very fast. That means
there are more people eating beef. Consequently,
there is more cattle – more walking machines – producing
more methane gas.
This is the problem, but very few people want to
change their eating habits. What about you?
Analyze these sentences:
1. The words people and cattle are being used in the text as nouns in the singular form.
2. In “…and it is used as fertilizer.”, the underlined word is an example of the comparative of equality.
3. The word in bold in “Consequently, there is more cattle” can be replaced by therefore without changing its meaning.

Choose the alternative which presents the correct ones.
A
The only correct one is 1.
B
The only correct one is 2.
C
The only correct one is 3.
D
The only correct ones are 1 and 3.
E
The only correct ones are 2 and 3.
Ano: 2017 Banca: FEPESE Órgão: CIASC Prova: Analista - Analista de Informática e Suporte
Match the words on column 1 with their meanings on column 2.

Column 1 Words

    1. hay
     2. waste
     3. rear
     4. burp
     5. cattle
Column 2 Meanings

( ) the back part of something.
( ) cows and bulls that are kept for their milk or meat.
( ) grass that is cut and dried and used as animal food.
( ) unwanted matter or material of any type.
( ) to allow air from the stomach to come out through the mouth in anoisy way.

Choose the alternative which presents the correct sequence:
A
1 • 4 • 5 • 3 • 2
B
2 • 1 • 5 • 4 • 3
C
3 • 5 • 1 • 2 • 4
D
4 • 3 • 2 • 1 • 5
E
5 • 1 • 4 • 2 • 3
Ano: 2016 Banca: CESPE Órgão: TRE-PE Prova: Técnico Judiciário - Operação de Computadores
Texto Associado Texto Associado
1 One day Mrs Jones went shopping. When her husband

came home in the evening, she began to tell him about a

modern computer. ‘I saw it in a shop this morning,’ she said,

4 ‘and…’

‘And you want to buy it’, said her husband. ‘How

much does it cost?’

7 ‘Five hundred Euros.’

‘Five hundred Euros for a computer? That’s too

much!’

10 But every evening, when Mr Jones came back from

work, his wife continued to speak only about the computer, and

at last, after a week, he said, ‘Oh, buy the computer! Here is the

13 money!’ She was very happy.

But the next evening, when Mr Jones came home and

asked, ‘Have you got the famous computer?’ She said, ‘No’.

16 ‘Why not?’ He said.

‘Well, it was still in the window of the shop after a

week so I thought, “Nobody else wants this computer, so I

19 don’t want it either”.’
L.A Hill. Elementary Stories for Reproduction. 27th edn.,

Oxford: Oxford University Press,1995 (adapted).
In the text, “at last” (L. 12) means
A
at least
B
finally
C
often
D
soon
E
just
Ano: 2016 Banca: CESPE Órgão: TRE-PE Prova: Técnico Judiciário - Programação de Sistemas
Texto Associado Texto Associado
1 One day Mrs Jones went shopping. When her husband

came home in the evening, she began to tell him about a

modern computer. ‘I saw it in a shop this morning,’ she said,

4 ‘and…’

‘And you want to buy it’, said her husband. ‘How

much does it cost?’

7 ‘Five hundred Euros.’

‘Five hundred Euros for a computer? That’s too

much!’

10 But every evening, when Mr Jones came back from

work, his wife continued to speak only about the computer, and

at last, after a week, he said, ‘Oh, buy the computer! Here is the

13 money!’ She was very happy.

But the next evening, when Mr Jones came home and

asked, ‘Have you got the famous computer?’ She said, ‘No’.

16 ‘Why not?’ He said.

‘Well, it was still in the window of the shop after a

week so I thought, “Nobody else wants this computer, so I

19 don’t want it either”.’


L.A Hill. Elementary Stories for Reproduction. 27th edn.,

Oxford: Oxford University Press,1995 (adapted).
In the text, “at last” (L. 12) means
A
at least
B
finally
C
often
D
soon
E
just
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 phrase “lots of data to chew on” in Text II makes use of figurative language and shares some common characteristics with: 
A
eating
B
drawing
C
chatting
D
thinking
E
counting
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 use of the phrase “the backlash” in the title of Text II means the: 
A
backing of
B
support for
C
decision for
D
resistance to
E
overpowering of
Texto Associado Texto Associado
TEXT I
Will computers ever truly understand what we’re saying? 

Date: January 11, 2016
Source University of California - Berkeley
Summary:
If you think computers are quickly approaching true human communication, think again. Computers like Siri often get confused because they judge meaning by looking at a word’s statistical regularity. This is unlike humans, for whom context is more important than the word or signal, according to a researcher who invented a communication game allowing only nonverbal cues, and used it to pinpoint regions of the brain where mutual understanding takes place.  

From Apple’s Siri to Honda’s robot Asimo, machines seem to be getting better and better at communicating with humans. But some neuroscientists caution that today’s computers will never truly understand what we’re saying because they do not take into account the context of a conversation the way people do. 

Specifically, say University of California, Berkeley, postdoctoral fellow Arjen Stolk and his Dutch colleagues, machines don’t develop a shared understanding of the people, place and situation - often including a long social history - that is key to human communication. Without such common ground, a computer cannot help but be confused.  

“People tend to think of communication as an exchange of linguistic signs or gestures, forgetting that much of communication is about the social context, about who you are communicating with,” Stolk said. 

The word “bank,” for example, would be interpreted one way if you’re holding a credit card but a different way if you’re holding a fishing pole. Without context, making a “V” with two fingers could mean victory, the number two, or “these are the two fingers I broke.” 

“All these subtleties are quite crucial to understanding one another,” Stolk said, perhaps more so than the words and signals that computers and many neuroscientists focus on as the key to communication. “In fact, we can understand one another without language, without words and signs that already have a shared meaning.” 
(Adapted from http://www.sciencedaily.com/releases/2016/01/1 60111135231.htm)
The word “so” in “perhaps more so than the words and signals” is used to refer to something already stated in Text I. In this context, it refers to:
A
key
B
crucial
C
subtleties
D
understanding
E
communication
Texto Associado Texto Associado
TEXT I
Will computers ever truly understand what we’re saying? 

Date: January 11, 2016
Source University of California - Berkeley
Summary:
If you think computers are quickly approaching true human communication, think again. Computers like Siri often get confused because they judge meaning by looking at a word’s statistical regularity. This is unlike humans, for whom context is more important than the word or signal, according to a researcher who invented a communication game allowing only nonverbal cues, and used it to pinpoint regions of the brain where mutual understanding takes place.  

From Apple’s Siri to Honda’s robot Asimo, machines seem to be getting better and better at communicating with humans. But some neuroscientists caution that today’s computers will never truly understand what we’re saying because they do not take into account the context of a conversation the way people do. 

Specifically, say University of California, Berkeley, postdoctoral fellow Arjen Stolk and his Dutch colleagues, machines don’t develop a shared understanding of the people, place and situation - often including a long social history - that is key to human communication. Without such common ground, a computer cannot help but be confused.  

“People tend to think of communication as an exchange of linguistic signs or gestures, forgetting that much of communication is about the social context, about who you are communicating with,” Stolk said. 

The word “bank,” for example, would be interpreted one way if you’re holding a credit card but a different way if you’re holding a fishing pole. Without context, making a “V” with two fingers could mean victory, the number two, or “these are the two fingers I broke.” 

“All these subtleties are quite crucial to understanding one another,” Stolk said, perhaps more so than the words and signals that computers and many neuroscientists focus on as the key to communication. “In fact, we can understand one another without language, without words and signs that already have a shared meaning.” 
(Adapted from http://www.sciencedaily.com/releases/2016/01/1 60111135231.htm)
If you are holding a fishing pole, the word “bank” means a: 
A
safe
B
seat
C
boat
D
building
E
coastline
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 phrase “lots of data to chew on” in Text II makes use of figurative language and shares some common characteristics with: 
A
eating
B
drawing
C
chatting
D
thinking
E
counting
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