Em muitas narrativas, ocorre a interferência do narrador. No texto “Vamos começar pelo nascimento do nosso herói!”, a interferência é corretamente identificada como:


“e descobriram que, nelas, ‘chovem’ mais de 1.000 toneladas de microplásticos por ano, o equivalente a 120 milhões de garrafas PET.” (2º parágrafo)
“e concluiu que cada adulto ingere em média 20 gramas de microplásticos, o equivalente a uma pecinha de Lego, por mês” (3º parágrafo)
As duas passagens acima ilustram o emprego de uma mesma estratégia discursiva, bastante comum em textos que buscam gerar impacto emocional.
Essa estratégia consiste no recurso a:
Texto 4
Assim que toca o sinal indicando o fim das aulas, um grupo de alunos sai correndo das salas. Eles não estão com pressa de ir embora, como seria de se esperar após nove horas e meia de atividade escolar, mas para ir ao pátio, onde vão ensaiar para a fanfarra ou treinar handebol.
Em um colégio onde 30% dos alunos repetiam ou abandonavam os estudos, houve um receio inicial em aumentar o tempo de classe, com o período integral. A solução surpreendeu, fez aumentar o interesse dos jovens pelos estudos e melhorou os indicadores educacionais da unidade.
O primeiro parágrafo do texto 4 mistura dois tipos de textos, que são:

“Hoje, esse termo denota, além da agressão física, diversos tipos de imposição sobre a vida civil, como a repressão política, familiar ou de gênero, ou a censura da fala e do pensamento de determinados indivíduos e, ainda, o desgaste causado pelas condições de trabalho e condições econômicas”. Sobre os componentes desse segmento do texto 2, é correto afirmar que:
Texto 3
Em uma carta de um jesuíta espanhol sobre o Brasil de 1500, aparecia o seguinte texto:
“Assim, chegamos a uma aldeia onde achamos os gentios todos embriagados, porque aqui tem uma maneira de vinho de raízes que embriaga muito, e quando eles estão assim bêbados ficam tão brutos e feros que não perdoam a nenhuma pessoa, e, quando não podem mais, põem fogo na casa onde estão os estrangeiros”.
Observe a frase a seguir.
O Brasil está tentando superar dificuldades; torçamos para que o maior país da América do Sul tenha sucesso.
Nesse caso substituiu-se o primeiro termo, para evitar-se a repetição de palavras, por uma qualificação; ocorre o mesmo processo na seguinte frase:
O célebre e falecido cantor Elvis Presley disse certa vez: “Não entendo nada de música. Na minha área você não precisa disso”.
Em relação aos componentes e estruturação dessa frase, é correto afirmar que:
Todos os itens abaixo são períodos compostos por duas orações, separadas por um sinal de pontuação; o item em que a inclusão de um conectivo entre essas duas orações foi feita de forma adequada ao sentido original é:
How facial recognition technology aids police

Police officers’ ability to recognize and locate individuals with a history of committing crime is vital to their work. In fact, it is so important that officers believe possessing it is fundamental to the craft of effective street policing, crime prevention and investigation. However, with the total police workforce falling by almost 20 percent since 2010 and recorded crime rising, police forces are turning to new technological solutions to help enhance their capability and capacity to monitor and track individuals about whom they have concerns.
One such technology is Automated Facial Recognition (known as AFR). This works by analyzing key facial features, generating a mathematical representation of them, and then comparing them against known faces in a database, to determine possible matches. While a number of UK and international police forces have been enthusiastically exploring the potential of AFR, some groups have spoken about its legal and ethical status. They are concerned that the technology significantly extends the reach and depth of surveillance by the state.
Until now, however, there has been no robust evidence about what AFR systems can and cannot deliver for policing. Although AFR has become increasingly familiar to the public through its use at airports to help manage passport checks, the environment in such settings is quite controlled. Applying similar procedures to street policing is far more complex. Individuals on the street will be moving and may not look directly towards the camera. Levels of lighting change, too, and the system will have to cope with the vagaries of the British weather.
[…]
As with all innovative policing technologies there are important legal and ethical concerns and issues that still need to be considered. But in order for these to be meaningfully debated and assessed by citizens, regulators and law-makers, we need a detailed understanding of precisely what the technology can realistically accomplish. Sound evidence, rather than references to science fiction technology --- as seen in films such as Minority Report --- is essential.
With this in mind, one of our conclusions is that in terms of describing how AFR is being applied in policing currently, it is more accurate to think of it as “assisted facial recognition,” as opposed to a fully automated system. Unlike border control functions -- where the facial recognition is more of an automated system -- when supporting street policing, the algorithm is not deciding whether there is a match between a person and what is stored in the database. Rather, the system makes suggestions to a police operator about possible similarities. It is then down to the operator to confirm or refute them.
By Bethan Davies, Andrew Dawson, Martin Innes (Source: https://gcn.com/articles/2018/11/30/facial-recognitionpolicing.aspx, accessed May 30th, 2020)
READ TEXT II AND ANSWER QUESTIONS 16 TO 20:
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)
How facial recognition technology aids police

Police officers’ ability to recognize and locate individuals with a history of committing crime is vital to their work. In fact, it is so important that officers believe possessing it is fundamental to the craft of effective street policing, crime prevention and investigation. However, with the total police workforce falling by almost 20 percent since 2010 and recorded crime rising, police forces are turning to new technological solutions to help enhance their capability and capacity to monitor and track individuals about whom they have concerns.
One such technology is Automated Facial Recognition (known as AFR). This works by analyzing key facial features, generating a mathematical representation of them, and then comparing them against known faces in a database, to determine possible matches. While a number of UK and international police forces have been enthusiastically exploring the potential of AFR, some groups have spoken about its legal and ethical status. They are concerned that the technology significantly extends the reach and depth of surveillance by the state.
Until now, however, there has been no robust evidence about what AFR systems can and cannot deliver for policing. Although AFR has become increasingly familiar to the public through its use at airports to help manage passport checks, the environment in such settings is quite controlled. Applying similar procedures to street policing is far more complex. Individuals on the street will be moving and may not look directly towards the camera. Levels of lighting change, too, and the system will have to cope with the vagaries of the British weather.
[…]
As with all innovative policing technologies there are important legal and ethical concerns and issues that still need to be considered. But in order for these to be meaningfully debated and assessed by citizens, regulators and law-makers, we need a detailed understanding of precisely what the technology can realistically accomplish. Sound evidence, rather than references to science fiction technology --- as seen in films such as Minority Report --- is essential.
With this in mind, one of our conclusions is that in terms of describing how AFR is being applied in policing currently, it is more accurate to think of it as “assisted facial recognition,” as opposed to a fully automated system. Unlike border control functions -- where the facial recognition is more of an automated system -- when supporting street policing, the algorithm is not deciding whether there is a match between a person and what is stored in the database. Rather, the system makes suggestions to a police operator about possible similarities. It is then down to the operator to confirm or refute them.
By Bethan Davies, Andrew Dawson, Martin Innes (Source: https://gcn.com/articles/2018/11/30/facial-recognitionpolicing.aspx, accessed May 30th, 2020)
How facial recognition technology aids police

Police officers’ ability to recognize and locate individuals with a history of committing crime is vital to their work. In fact, it is so important that officers believe possessing it is fundamental to the craft of effective street policing, crime prevention and investigation. However, with the total police workforce falling by almost 20 percent since 2010 and recorded crime rising, police forces are turning to new technological solutions to help enhance their capability and capacity to monitor and track individuals about whom they have concerns.
One such technology is Automated Facial Recognition (known as AFR). This works by analyzing key facial features, generating a mathematical representation of them, and then comparing them against known faces in a database, to determine possible matches. While a number of UK and international police forces have been enthusiastically exploring the potential of AFR, some groups have spoken about its legal and ethical status. They are concerned that the technology significantly extends the reach and depth of surveillance by the state.
Until now, however, there has been no robust evidence about what AFR systems can and cannot deliver for policing. Although AFR has become increasingly familiar to the public through its use at airports to help manage passport checks, the environment in such settings is quite controlled. Applying similar procedures to street policing is far more complex. Individuals on the street will be moving and may not look directly towards the camera. Levels of lighting change, too, and the system will have to cope with the vagaries of the British weather.
[…]
As with all innovative policing technologies there are important legal and ethical concerns and issues that still need to be considered. But in order for these to be meaningfully debated and assessed by citizens, regulators and law-makers, we need a detailed understanding of precisely what the technology can realistically accomplish. Sound evidence, rather than references to science fiction technology --- as seen in films such as Minority Report --- is essential.
With this in mind, one of our conclusions is that in terms of describing how AFR is being applied in policing currently, it is more accurate to think of it as “assisted facial recognition,” as opposed to a fully automated system. Unlike border control functions -- where the facial recognition is more of an automated system -- when supporting street policing, the algorithm is not deciding whether there is a match between a person and what is stored in the database. Rather, the system makes suggestions to a police operator about possible similarities. It is then down to the operator to confirm or refute them.
By Bethan Davies, Andrew Dawson, Martin Innes (Source: https://gcn.com/articles/2018/11/30/facial-recognitionpolicing.aspx, accessed May 30th, 2020)
Uma empresa criou um arquivo com uma sequência de pastas identificadas com uma letra do alfabeto e um número escrito com dois dígitos, como se vê a seguir.
A00, A01, A02, A03, ..., A99, B00, B01, B02, ..., Z99
A quantidade de pastas depois de D37 e antes de F23 é:
Três caixas, despachadas pelo correio, tinham os pesos a seguir:

A sequência das caixas em ordem crescente de seus pesos é:
Antônio, Beto e Carlos combinaram dividir igualmente as despesas de uma viagem que os três fizeram juntos.
Durante a viagem, Antônio pagou R$ 750,00, Beto pagou R$ 480,00 e Carlos pagou R$ 420,00. Ao final da viagem, para dividir igualmente as despesas, Beto deu x reais para Antônio, e Carlos deu y reais para Antônio.
O valor de x + y é
Considere a sentença: “Se Amazonino é amazonense e Reno não é alagoano, então Carlota não é carioca”.
Uma sentença logicamente equivalente à sentença dada é
Um antigo ditado diz: “Se há fumaça então há fogo”.
Uma sentença logicamente equivalente é
Determinado operador, pessoa natural que realizou o tratamento de dados pessoais em nome de controlador, pessoa jurídica de direito público, em razão do exercício de atividade de tratamento de dados pessoais, causou dano patrimonial e moral a Caio, tendo sido comprovada, judicialmente, a violação à legislação de proteção de dados pessoais.
A partir da legislação em vigor, é correto afirmar que:
No dia 1 de junho de 2017, jornais de várias partes do mundo deram a manchete:
Trump anuncia retirada dos EUA do Acordo de Paris sobre o clima
A justificativa dada por Trump para a saída do Acordo de París foi que
Em passado recente as três grandes agências internacionais de classificação de risco voltaram suas atenções para a economia brasileira. Sobre esse fato considere as afirmações:
I. A classificação de risco (rating) soberano é a nota dada por agências classificadoras de risco que avaliam a capacidade e a disposição de um país em honrar, pontual e integralmente, os pagamentos de sua dívida.
II. As agências atribuem as notas de risco de crédito apenas a Estados nacionais, mas excepcionalmente podem avaliar empresas, especialmente estatais que estão em vias de desestatização.
III. Desde final de 2016 as principais agências de risco incluíram o Brasil no grupo de países com classificação A-, isto é, país com baixo grau de investimento financeiro.
IV. Quanto pior for a classificação de risco maior são os juros cobrados pelos investidores para emprestar dinheiro, o que amplia a crise econômica do país endividado.
Está correto o que se afirma APENAS em

Um dos fatores que contribuiu para o crescimento do PIB foi a
Um dia histórico para o mundo da música. A cantora conquistou o Grammy Latino de “Melhor Álbum de Música Popular Brasileira” nesta quinta-feira (17.11) e se tornou a primeira artista assumidamente trans a levar esse prêmio. Ao ter seu nome revelado na cerimônia, que acontece em Las Vegas, todos os convidados a aplaudiram de pé.
(Disponível em: https://cultura.uol.com.br)
Apesar de ser um evento que abarca diversos países e culturas do planeta, a Copa do Mundo de Futebol Masculino de 2022, realizada no Catar, tem gerado polêmicas entre jogadores e organização do torneio.
Uma das principais polêmicas se refere