Pular para o conteúdo
Dicas do ENEMEntrar

Questões do ENEM: nível difícil

Use os filtros para chegar às questões que interessam. Cada resultado abre em uma página própria com enunciado, alternativas e gabarito.

Resultados

9.777 questões encontradas. Mostrando a página 315 de 489.

  • 4E424E3A-AF

    Inglês

    Adjetivos | Adjectives    UECE · 2013DifícilEntre para guardar nos favoritos
    TEXT
       
       HARVARD BUSINESS REVIEW calls data science “the sexiest job in the 21st century,” and by most accounts this hot new field promises to revolutionize industries from business to government, health care to academia. 
       The field has been spawned by the enormous amounts of data that modern technologies create — be it the online behavior of Facebook users, tissue samples of cancer patients, purchasing habits of grocery shoppers or crime statistics of cities. Data scientists are the magicians of the Big Data era. They crunch the data, use mathematical models to analyze it and create narratives or visualizations to explain it, then suggest how to use the information to make decisions. 
         In the last few years, dozens of programs under a variety of names have sprung up in response to the excitement about Big Data, not to mention the six-figure salaries for some recent graduates. In the fall, Columbia will offer new master’s and certificate programs heavy on data. The University of San Francisco will soon graduate its charter class of students with a master’s in analytics.
          Rachel Schutt, a senior research scientist at Johnson Research Labs, taught “Introduction to Data Science” last semester at Columbia (its first course with “data science” in the title). She described the data scientist this way: “a hybrid computer scientist software engineer statistician.” And added: “The best tend to be really curious people, thinkers who ask good questions and are O.K. dealing with unstructured situations and trying to find structure in them.”
          Eurry Kim, a 30-year-old “wannabe data scientist,” is studying at Columbia for a master’s in quantitative methods in the social sciences and plans to use her degree for government service. She discovered the possibilities while working as a corporate tax analyst at the Internal Revenue Service. She might, for example, analyze tax return data to develop algorithms that flag fraudulent filings, or cull national security databases to spot suspicious activity.
         Some of her classmates are hoping to apply their skills to e-commerce, where data about users’ browsing history is gold.
         “This is a generation of kids that grew up with data science around them — Netflix telling them what movies they should watch, Amazon telling them what books they should read — so this is an academic interest with real-world applications,” said Chris Wiggins, a professor of applied mathematics at Columbia who is involved in its new Institute for Data Sciences and Engineering. “And,” he added, “they know it will make them employable.”
      Universities can hardly turn out data scientists fast enough. To meet demand from employers, the United States will need to increase the number of graduates with skills handling large amounts of data by as much as 60 percent, according to a report by McKinsey Global Institute. There will be almost half a million jobs in five years, and a shortage of up to 190,000 qualified data scientists, plus a need for 1.5 million executives and support staff who have an understanding of data.
          Because data science is so new, universities are scrambling to define it and develop curriculums. As an academic field, it cuts across disciplines, with courses in statistics, analytics, computer science and math, coupled with the specialty a student wants to analyze, from patterns in marine life to historical texts.
        With the sheer volume, variety and speed of data today, as well as developing technologies, programs are more than a repackaging of existing courses. “Data science is emerging as an academic discipline, defined not by a mere amalgamation of interdisciplinary fields but as a body of knowledge, a set of professional practices, a professional organization and a set of ethical responsibilities,” said Christopher Starr, chairman of the computer science department at the College of Charleston, one of a few institutions offering data science at the undergraduate level.
         Most master’s degree programs in data science require basic programming skills. They start with what Ms. Schutt describes as the “boring” part — scraping and cleaning raw data and “getting it into a nice table where you can actually analyze it.” Many use data sets provided by businesses or government, and pass back their results. Some host competitions to see which student can come up with the best solution to a company’s problem.
         Studying a Web user’s data has privacy implications. Using data to decide someone’s eligibility for a line of credit or health insurance, or even recommending who they friend on Facebook, can affect their lives. “We’re building these models that have impact on human life,” Ms. Schutt said. “How can we do that carefully?” Ethics classes address these questions.
           Finally, students have to learn to communicate their findings, visually and orally, and they need business know-how, perhaps to develop new products.

    From: www.nytimes.com
    The functions of the words purchasing, dealing, filings, programming and recommending in the text are respectively
    Escolha uma alternativa para a questão 4e424e3a-af
  • 4E3AA488-AF

    Inglês

    Interpretação de texto | Reading comprehension    UECE · 2013DifícilEntre para guardar nos favoritos
    TEXT
       
       HARVARD BUSINESS REVIEW calls data science “the sexiest job in the 21st century,” and by most accounts this hot new field promises to revolutionize industries from business to government, health care to academia. 
       The field has been spawned by the enormous amounts of data that modern technologies create — be it the online behavior of Facebook users, tissue samples of cancer patients, purchasing habits of grocery shoppers or crime statistics of cities. Data scientists are the magicians of the Big Data era. They crunch the data, use mathematical models to analyze it and create narratives or visualizations to explain it, then suggest how to use the information to make decisions. 
         In the last few years, dozens of programs under a variety of names have sprung up in response to the excitement about Big Data, not to mention the six-figure salaries for some recent graduates. In the fall, Columbia will offer new master’s and certificate programs heavy on data. The University of San Francisco will soon graduate its charter class of students with a master’s in analytics.
          Rachel Schutt, a senior research scientist at Johnson Research Labs, taught “Introduction to Data Science” last semester at Columbia (its first course with “data science” in the title). She described the data scientist this way: “a hybrid computer scientist software engineer statistician.” And added: “The best tend to be really curious people, thinkers who ask good questions and are O.K. dealing with unstructured situations and trying to find structure in them.”
          Eurry Kim, a 30-year-old “wannabe data scientist,” is studying at Columbia for a master’s in quantitative methods in the social sciences and plans to use her degree for government service. She discovered the possibilities while working as a corporate tax analyst at the Internal Revenue Service. She might, for example, analyze tax return data to develop algorithms that flag fraudulent filings, or cull national security databases to spot suspicious activity.
         Some of her classmates are hoping to apply their skills to e-commerce, where data about users’ browsing history is gold.
         “This is a generation of kids that grew up with data science around them — Netflix telling them what movies they should watch, Amazon telling them what books they should read — so this is an academic interest with real-world applications,” said Chris Wiggins, a professor of applied mathematics at Columbia who is involved in its new Institute for Data Sciences and Engineering. “And,” he added, “they know it will make them employable.”
      Universities can hardly turn out data scientists fast enough. To meet demand from employers, the United States will need to increase the number of graduates with skills handling large amounts of data by as much as 60 percent, according to a report by McKinsey Global Institute. There will be almost half a million jobs in five years, and a shortage of up to 190,000 qualified data scientists, plus a need for 1.5 million executives and support staff who have an understanding of data.
          Because data science is so new, universities are scrambling to define it and develop curriculums. As an academic field, it cuts across disciplines, with courses in statistics, analytics, computer science and math, coupled with the specialty a student wants to analyze, from patterns in marine life to historical texts.
        With the sheer volume, variety and speed of data today, as well as developing technologies, programs are more than a repackaging of existing courses. “Data science is emerging as an academic discipline, defined not by a mere amalgamation of interdisciplinary fields but as a body of knowledge, a set of professional practices, a professional organization and a set of ethical responsibilities,” said Christopher Starr, chairman of the computer science department at the College of Charleston, one of a few institutions offering data science at the undergraduate level.
         Most master’s degree programs in data science require basic programming skills. They start with what Ms. Schutt describes as the “boring” part — scraping and cleaning raw data and “getting it into a nice table where you can actually analyze it.” Many use data sets provided by businesses or government, and pass back their results. Some host competitions to see which student can come up with the best solution to a company’s problem.
         Studying a Web user’s data has privacy implications. Using data to decide someone’s eligibility for a line of credit or health insurance, or even recommending who they friend on Facebook, can affect their lives. “We’re building these models that have impact on human life,” Ms. Schutt said. “How can we do that carefully?” Ethics classes address these questions.
           Finally, students have to learn to communicate their findings, visually and orally, and they need business know-how, perhaps to develop new products.

    From: www.nytimes.com
    Some of Eurry Kim’s peers expect to use their abilities on
    Escolha uma alternativa para a questão 4e3aa488-af
  • 4E367754-AF

    Inglês

    Interpretação de texto | Reading comprehension    UECE · 2013DifícilEntre para guardar nos favoritos
    TEXT
       
       HARVARD BUSINESS REVIEW calls data science “the sexiest job in the 21st century,” and by most accounts this hot new field promises to revolutionize industries from business to government, health care to academia. 
       The field has been spawned by the enormous amounts of data that modern technologies create — be it the online behavior of Facebook users, tissue samples of cancer patients, purchasing habits of grocery shoppers or crime statistics of cities. Data scientists are the magicians of the Big Data era. They crunch the data, use mathematical models to analyze it and create narratives or visualizations to explain it, then suggest how to use the information to make decisions. 
         In the last few years, dozens of programs under a variety of names have sprung up in response to the excitement about Big Data, not to mention the six-figure salaries for some recent graduates. In the fall, Columbia will offer new master’s and certificate programs heavy on data. The University of San Francisco will soon graduate its charter class of students with a master’s in analytics.
          Rachel Schutt, a senior research scientist at Johnson Research Labs, taught “Introduction to Data Science” last semester at Columbia (its first course with “data science” in the title). She described the data scientist this way: “a hybrid computer scientist software engineer statistician.” And added: “The best tend to be really curious people, thinkers who ask good questions and are O.K. dealing with unstructured situations and trying to find structure in them.”
          Eurry Kim, a 30-year-old “wannabe data scientist,” is studying at Columbia for a master’s in quantitative methods in the social sciences and plans to use her degree for government service. She discovered the possibilities while working as a corporate tax analyst at the Internal Revenue Service. She might, for example, analyze tax return data to develop algorithms that flag fraudulent filings, or cull national security databases to spot suspicious activity.
         Some of her classmates are hoping to apply their skills to e-commerce, where data about users’ browsing history is gold.
         “This is a generation of kids that grew up with data science around them — Netflix telling them what movies they should watch, Amazon telling them what books they should read — so this is an academic interest with real-world applications,” said Chris Wiggins, a professor of applied mathematics at Columbia who is involved in its new Institute for Data Sciences and Engineering. “And,” he added, “they know it will make them employable.”
      Universities can hardly turn out data scientists fast enough. To meet demand from employers, the United States will need to increase the number of graduates with skills handling large amounts of data by as much as 60 percent, according to a report by McKinsey Global Institute. There will be almost half a million jobs in five years, and a shortage of up to 190,000 qualified data scientists, plus a need for 1.5 million executives and support staff who have an understanding of data.
          Because data science is so new, universities are scrambling to define it and develop curriculums. As an academic field, it cuts across disciplines, with courses in statistics, analytics, computer science and math, coupled with the specialty a student wants to analyze, from patterns in marine life to historical texts.
        With the sheer volume, variety and speed of data today, as well as developing technologies, programs are more than a repackaging of existing courses. “Data science is emerging as an academic discipline, defined not by a mere amalgamation of interdisciplinary fields but as a body of knowledge, a set of professional practices, a professional organization and a set of ethical responsibilities,” said Christopher Starr, chairman of the computer science department at the College of Charleston, one of a few institutions offering data science at the undergraduate level.
         Most master’s degree programs in data science require basic programming skills. They start with what Ms. Schutt describes as the “boring” part — scraping and cleaning raw data and “getting it into a nice table where you can actually analyze it.” Many use data sets provided by businesses or government, and pass back their results. Some host competitions to see which student can come up with the best solution to a company’s problem.
         Studying a Web user’s data has privacy implications. Using data to decide someone’s eligibility for a line of credit or health insurance, or even recommending who they friend on Facebook, can affect their lives. “We’re building these models that have impact on human life,” Ms. Schutt said. “How can we do that carefully?” Ethics classes address these questions.
           Finally, students have to learn to communicate their findings, visually and orally, and they need business know-how, perhaps to develop new products.

    From: www.nytimes.com
    According to the text, in terms of what is required from a student in order to apply for a master’s degree in the area of data science, one must have
    Escolha uma alternativa para a questão 4e367754-af
  • 4E2F0772-AF

    Inglês

    Interpretação de texto | Reading comprehension    UECE · 2013DifícilEntre para guardar nos favoritos
    TEXT
       
       HARVARD BUSINESS REVIEW calls data science “the sexiest job in the 21st century,” and by most accounts this hot new field promises to revolutionize industries from business to government, health care to academia. 
       The field has been spawned by the enormous amounts of data that modern technologies create — be it the online behavior of Facebook users, tissue samples of cancer patients, purchasing habits of grocery shoppers or crime statistics of cities. Data scientists are the magicians of the Big Data era. They crunch the data, use mathematical models to analyze it and create narratives or visualizations to explain it, then suggest how to use the information to make decisions. 
         In the last few years, dozens of programs under a variety of names have sprung up in response to the excitement about Big Data, not to mention the six-figure salaries for some recent graduates. In the fall, Columbia will offer new master’s and certificate programs heavy on data. The University of San Francisco will soon graduate its charter class of students with a master’s in analytics.
          Rachel Schutt, a senior research scientist at Johnson Research Labs, taught “Introduction to Data Science” last semester at Columbia (its first course with “data science” in the title). She described the data scientist this way: “a hybrid computer scientist software engineer statistician.” And added: “The best tend to be really curious people, thinkers who ask good questions and are O.K. dealing with unstructured situations and trying to find structure in them.”
          Eurry Kim, a 30-year-old “wannabe data scientist,” is studying at Columbia for a master’s in quantitative methods in the social sciences and plans to use her degree for government service. She discovered the possibilities while working as a corporate tax analyst at the Internal Revenue Service. She might, for example, analyze tax return data to develop algorithms that flag fraudulent filings, or cull national security databases to spot suspicious activity.
         Some of her classmates are hoping to apply their skills to e-commerce, where data about users’ browsing history is gold.
         “This is a generation of kids that grew up with data science around them — Netflix telling them what movies they should watch, Amazon telling them what books they should read — so this is an academic interest with real-world applications,” said Chris Wiggins, a professor of applied mathematics at Columbia who is involved in its new Institute for Data Sciences and Engineering. “And,” he added, “they know it will make them employable.”
      Universities can hardly turn out data scientists fast enough. To meet demand from employers, the United States will need to increase the number of graduates with skills handling large amounts of data by as much as 60 percent, according to a report by McKinsey Global Institute. There will be almost half a million jobs in five years, and a shortage of up to 190,000 qualified data scientists, plus a need for 1.5 million executives and support staff who have an understanding of data.
          Because data science is so new, universities are scrambling to define it and develop curriculums. As an academic field, it cuts across disciplines, with courses in statistics, analytics, computer science and math, coupled with the specialty a student wants to analyze, from patterns in marine life to historical texts.
        With the sheer volume, variety and speed of data today, as well as developing technologies, programs are more than a repackaging of existing courses. “Data science is emerging as an academic discipline, defined not by a mere amalgamation of interdisciplinary fields but as a body of knowledge, a set of professional practices, a professional organization and a set of ethical responsibilities,” said Christopher Starr, chairman of the computer science department at the College of Charleston, one of a few institutions offering data science at the undergraduate level.
         Most master’s degree programs in data science require basic programming skills. They start with what Ms. Schutt describes as the “boring” part — scraping and cleaning raw data and “getting it into a nice table where you can actually analyze it.” Many use data sets provided by businesses or government, and pass back their results. Some host competitions to see which student can come up with the best solution to a company’s problem.
         Studying a Web user’s data has privacy implications. Using data to decide someone’s eligibility for a line of credit or health insurance, or even recommending who they friend on Facebook, can affect their lives. “We’re building these models that have impact on human life,” Ms. Schutt said. “How can we do that carefully?” Ethics classes address these questions.
           Finally, students have to learn to communicate their findings, visually and orally, and they need business know-how, perhaps to develop new products.

    From: www.nytimes.com
    According to the text, besides being referred to as a sexy job in our century, data science
    Escolha uma alternativa para a questão 4e2f0772-af
  • 9FFBCAC7-AF

    Biologia

    A química da vida    UECE · 2013DifícilEntre para guardar nos favoritos
    O corpo humano produz milhares de proteínas diferentes que, no organismo, possuem funções também diferentes, como por exemplo: estruturais, catalíticas, contrácteis, transportadoras, de defesa imunológica, etc, estando o formato de cada proteína diretamente relacionado com a sua função. Quanto à estrutura e à função das proteínas, indique a afirmação correta.
    Escolha uma alternativa para a questão 9ffbcac7-af
  • 1AB85904-AF

    Inglês

    Interpretação de texto | Reading comprehension    UECE · 2013DifícilEntre para guardar nos favoritos

    TEXT


          Hundreds of studies have assessed leadership styles, mainly by having employees report on how their managers typically behave. Researchers have also collected information on how effective managers are. After large numbers of such studies became available, reviewers aggregated them quantitatively to discover what kinds of leadership are effective.

          One conclusion that has emerged based on the research of the past 30 years is that a hybrid style known as transformational leadership is highly effective in most contemporary organizational contexts.

          A transformational leader acts as an inspirational role model, motivates others to go beyond the confines of their job descriptions, encourages creativity and innovation, fosters good human relationships, and develops the skills of followers. This type of leadership is effective because it fosters strong interpersonal bonds based on a leader’s charisma and consideration of others. These bonds enable leaders to promote high-quality performance by encouraging workers rather than threatening them, thus motivating them to exceed basic expectations.

          By bringing out the best in others, transformational leaders enhance the performance of groups and organizations.

          Transformational leadership is androgynous because it incorporates culturally masculine and feminine behaviors. This androgynous mixing of the masculine and feminine means that skill in this contemporary way of leading does not necessarily come naturally. It may require some effort and thought.

          Men often have to work on their social skills and women on being assertive enough to inspire others. It is nonetheless clear that both women and men can adapt to the demands of leadership in the transformational mode.

          One of the surprises of research on transformational leadership is that female managers are somewhat more transformational than male managers. In particular, they exceed men in their attention to human relationships. Also, in delivering incentives, women lean toward a more positive, reward-based approach and men toward a more negative and less effective, threat-based approach. In these respects, women appear to be better leaders than men, despite the double standard that can close women out of these roles.

          Why are women leaders more transformational when they are less likely to become leaders in the first place? One reason is that the double standard that slows women’s rise would work against mediocre women while allowing mediocre men to rise. As a consequence, the women who attain leadership roles really are better than the men on average.

          It is also true women generally avoid more domineering, “command and control” behavior because of the backlash they receive if they lead in this way. Men can often get away with autocratic behavior that is roundly disliked in women. Ironically, this backlash against domineering women may foster good leadership because the androgynous middle ground is more likely to bring success. Leaders gain less from ordering others about than from forming teams of smart, motivated collaborators who together figure out how to solve problems and get work done.

    From: http://www.nytimes.com/ 2013/03/20  

    Women usually refuse to behave in a domineering way due to the fact that they
    Escolha uma alternativa para a questão 1ab85904-af
  • 1AB18C60-AF

    Inglês

    Interpretação de texto | Reading comprehension    UECE · 2013DifícilEntre para guardar nos favoritos

    TEXT


          Hundreds of studies have assessed leadership styles, mainly by having employees report on how their managers typically behave. Researchers have also collected information on how effective managers are. After large numbers of such studies became available, reviewers aggregated them quantitatively to discover what kinds of leadership are effective.

          One conclusion that has emerged based on the research of the past 30 years is that a hybrid style known as transformational leadership is highly effective in most contemporary organizational contexts.

          A transformational leader acts as an inspirational role model, motivates others to go beyond the confines of their job descriptions, encourages creativity and innovation, fosters good human relationships, and develops the skills of followers. This type of leadership is effective because it fosters strong interpersonal bonds based on a leader’s charisma and consideration of others. These bonds enable leaders to promote high-quality performance by encouraging workers rather than threatening them, thus motivating them to exceed basic expectations.

          By bringing out the best in others, transformational leaders enhance the performance of groups and organizations.

          Transformational leadership is androgynous because it incorporates culturally masculine and feminine behaviors. This androgynous mixing of the masculine and feminine means that skill in this contemporary way of leading does not necessarily come naturally. It may require some effort and thought.

          Men often have to work on their social skills and women on being assertive enough to inspire others. It is nonetheless clear that both women and men can adapt to the demands of leadership in the transformational mode.

          One of the surprises of research on transformational leadership is that female managers are somewhat more transformational than male managers. In particular, they exceed men in their attention to human relationships. Also, in delivering incentives, women lean toward a more positive, reward-based approach and men toward a more negative and less effective, threat-based approach. In these respects, women appear to be better leaders than men, despite the double standard that can close women out of these roles.

          Why are women leaders more transformational when they are less likely to become leaders in the first place? One reason is that the double standard that slows women’s rise would work against mediocre women while allowing mediocre men to rise. As a consequence, the women who attain leadership roles really are better than the men on average.

          It is also true women generally avoid more domineering, “command and control” behavior because of the backlash they receive if they lead in this way. Men can often get away with autocratic behavior that is roundly disliked in women. Ironically, this backlash against domineering women may foster good leadership because the androgynous middle ground is more likely to bring success. Leaders gain less from ordering others about than from forming teams of smart, motivated collaborators who together figure out how to solve problems and get work done.

    From: http://www.nytimes.com/ 2013/03/20  

    Among the factors that make transformational leadership effective, the text mentions
    Escolha uma alternativa para a questão 1ab18c60-af
  • 1AA96CF3-AF

    Inglês

    Interpretação de texto | Reading comprehension    UECE · 2013DifícilEntre para guardar nos favoritos

    TEXT


          Hundreds of studies have assessed leadership styles, mainly by having employees report on how their managers typically behave. Researchers have also collected information on how effective managers are. After large numbers of such studies became available, reviewers aggregated them quantitatively to discover what kinds of leadership are effective.

          One conclusion that has emerged based on the research of the past 30 years is that a hybrid style known as transformational leadership is highly effective in most contemporary organizational contexts.

          A transformational leader acts as an inspirational role model, motivates others to go beyond the confines of their job descriptions, encourages creativity and innovation, fosters good human relationships, and develops the skills of followers. This type of leadership is effective because it fosters strong interpersonal bonds based on a leader’s charisma and consideration of others. These bonds enable leaders to promote high-quality performance by encouraging workers rather than threatening them, thus motivating them to exceed basic expectations.

          By bringing out the best in others, transformational leaders enhance the performance of groups and organizations.

          Transformational leadership is androgynous because it incorporates culturally masculine and feminine behaviors. This androgynous mixing of the masculine and feminine means that skill in this contemporary way of leading does not necessarily come naturally. It may require some effort and thought.

          Men often have to work on their social skills and women on being assertive enough to inspire others. It is nonetheless clear that both women and men can adapt to the demands of leadership in the transformational mode.

          One of the surprises of research on transformational leadership is that female managers are somewhat more transformational than male managers. In particular, they exceed men in their attention to human relationships. Also, in delivering incentives, women lean toward a more positive, reward-based approach and men toward a more negative and less effective, threat-based approach. In these respects, women appear to be better leaders than men, despite the double standard that can close women out of these roles.

          Why are women leaders more transformational when they are less likely to become leaders in the first place? One reason is that the double standard that slows women’s rise would work against mediocre women while allowing mediocre men to rise. As a consequence, the women who attain leadership roles really are better than the men on average.

          It is also true women generally avoid more domineering, “command and control” behavior because of the backlash they receive if they lead in this way. Men can often get away with autocratic behavior that is roundly disliked in women. Ironically, this backlash against domineering women may foster good leadership because the androgynous middle ground is more likely to bring success. Leaders gain less from ordering others about than from forming teams of smart, motivated collaborators who together figure out how to solve problems and get work done.

    From: http://www.nytimes.com/ 2013/03/20  

    According to the research results, women tend to do better in terms of the application of the transformational type of leadership because of their
    Escolha uma alternativa para a questão 1aa96cf3-af
  • 1A6259C9-AF

    Conhecimentos Gerais

    Conhecimentos Gerais Sobre a América Latina    UECE · 2013DifícilEntre para guardar nos favoritos
    O Presidente venezuelano Hugo Chávez, que estava no poder desde 1999, mesmo tendo adotado medidas discutíveis e duramente criticadas internacionalmente, conseguiu estabelecer-se como um dos líderes da América Latina. Com sua morte, o cenário político nesta parte do mundo já demonstra sinais de mudança através
    Escolha uma alternativa para a questão 1a6259c9-af
  • 1A4E9CBD-AF

    História

    A estruturação do Estado norte-americano : território, cidadania e política    UECE · 2013DifícilEntre para guardar nos favoritos
    A Guerra da Secessão (1861-1865), conhecida entre os historiadores como a segunda revolução norte-americana, foi palco de um dos mais letais conflitos bélicos dos Estados Unidos, dividindo o país entre norte e sul. Durante a guerra, mais de 600 mil norte-americanos morreram; as explicações para essa guerra são várias.

    Dentre as opções a seguir, assinale a que NÃO constitui uma razão para a referida guerra.
    Escolha uma alternativa para a questão 1a4e9cbd-af
  • 1A3D9512-AF

    História

    História do Brasil    UECE · 2013DifícilEntre para guardar nos favoritos

    Em dezembro de 1815, Dom João elevou o Brasil à condição de Reino Unido de Portugal e Algarves. Atente para o que é dito sobre esse assunto.


    I. Tal medida deveu-se à transferência da família real portuguesa para o Brasil, posto que acabara de desembarcar e precisava oficializar a nova sede do governo português.

    II. O Brasil deixou de ser colônia e, tornando-se a sede da monarquia portuguesa, equiparava-se politicamente à metrópole.

    III. A medida facilitou as relações comerciais, e possibilitou maior autonomia à antiga colônia.


    Está correto o que se afirma em

    Escolha uma alternativa para a questão 1a3d9512-af
  • 1A18370A-AF

    Português

    Interpretação de Textos    UECE · 2013DifícilEntre para guardar nos favoritos

    TEXTO II


    O texto II desta prova foi extraído do segundo capítulo da novela A indesejada aposentadoria, do escritor maranhense Josué Montello (*1917 — †2006). Contemporâneo dos escritores que fizeram o romance de 30, Montello enveredou por outro caminho: explorou a narrativa urbana. Escreveu uma das obras-primas da literatura brasileira: Os tambores de São Luís (1975). A novela A indesejada aposentadoria, de 1972, conta a história de Guihermino Pereira, um funcionário público, já nas vésperas de se aposentar. 


    Imagem da questão de Português, UECE 2013, Interpretação de Textos

    Josué Montello. A indesejada aposentadoria.
    Capítulo II, p. 11-14. Texto adaptado. 

    Atente aos pares de palavras opostas tendo em vista a estrutura em que se assenta a superficialidade do texto.


    I. Atividade vs. inércia.

    II. Fortaleza vs. tibieza ou debilidade.

    III. Inteireza vs. incompletude.


    O texto trabalha com as oposições contidas em

    Escolha uma alternativa para a questão 1a18370a-af
  • 19FF5BFB-AF

    Português

    Interpretação de Textos    UECE · 2013DifícilEntre para guardar nos favoritos

    TEXTO II


    O texto II desta prova foi extraído do segundo capítulo da novela A indesejada aposentadoria, do escritor maranhense Josué Montello (*1917 — †2006). Contemporâneo dos escritores que fizeram o romance de 30, Montello enveredou por outro caminho: explorou a narrativa urbana. Escreveu uma das obras-primas da literatura brasileira: Os tambores de São Luís (1975). A novela A indesejada aposentadoria, de 1972, conta a história de Guihermino Pereira, um funcionário público, já nas vésperas de se aposentar. 


    Imagem da questão de Português, UECE 2013, Interpretação de Textos

    Josué Montello. A indesejada aposentadoria.
    Capítulo II, p. 11-14. Texto adaptado. 

    Assinale a perspectiva da qual fala o enunciador.
    Escolha uma alternativa para a questão 19ff5bfb-af
  • 19F52520-AF

    Português

    Interpretação de Textos    UECE · 2013DifícilEntre para guardar nos favoritos

    TEXTO I

    O texto I desta prova é um excerto da parte 2, capítulo III, da obra Bandeirantes e pioneiros: paralelo entre duas culturas, de Vianna Moog — gaúcho de São Leopoldo (*1906 — †1988). Nesse capítulo, Moog faz um estudo comparativo entre a colonização dos EUA e a do Brasil, um paralelo entre as fundações da América inglesa e da América portuguesa. 


    Imagem da questão de Português, UECE 2013, Interpretação de Textos

    Vianna Moog. Bandeirantes e pioneiros: Paralelo entre duas culturas. Capítulo III: Conquista e colonização. p. 103-104. Texto adaptado.

    O vocábulo Mayflower significa “flor de maio”. Observe com atenção o contexto em que a palavra aparece (linhas 8-22) e marque a alternativa que completa corretamente o seguinte enunciado: Com “Mayflower” se denominou
    Escolha uma alternativa para a questão 19f52520-af
  • 92889565-AF

    Biologia

    Hereditariedade e diversidade da vida    UNESP · 2013DifícilEntre para guardar nos favoritos

    Leia a placa informativa presente em uma churrascaria.

    Imagem da questão de Biologia, UNESP 2013, Hereditariedade e diversidade da vida


    Porcos e javalis são subespécies de uma mesma espécie, Sus scrofa. A referência ao número de cromossomos justifica-se pelo fato de que são considerados javalis puros apenas os indivíduos com 36 cromossomos. Os porcos domésticos possuem 38 cromossomos e podem cruzar com javalis.

    Desse modo, é correto afirmar que:

    Escolha uma alternativa para a questão 92889565-af
  • 9258A204-AF

    História

    História do Brasil    UNESP · 2013DifícilEntre para guardar nos favoritos
    Em 1977, o Regime Militar, por meio da Agência Nacional de Comunicação, lançou uma propaganda que ensinava a população a fazer um cata-vento verde-amarelo e convocava-a a sair às ruas com esses brinquedos para comemorar a Semana da Pátria. Por meio de uma charge, o cartunista Henfil ironizou essa iniciativa do governo, sublinhando um outro problema enfrentado pelo país nessa época.

    Imagem da questão de História, UNESP 2013, História do Brasil

    (IstoÉ,19.10.1977. Adaptado.)


    Considerando o contexto histórico no qual a charge se insere, é correto afirmar que o cartunista chamava a atenção para

    Escolha uma alternativa para a questão 9258a204-af
  • 92459E54-AF

    Atualidades

    Guerras, Conflitos e Terrorismo na Atualidade    UNESP · 2013DifícilEntre para guardar nos favoritos
    Ocorrida entre 2011 e 2012, a série de manifestações e protestos, que recebeu o nome de “Primavera Árabe”, aconteceu principalmente em países situados
    Escolha uma alternativa para a questão 92459e54-af
  • 9202BDC5-AF

    Literatura

    Gênero Épico ou Narrativo    UNESP · 2013DifícilEntre para guardar nos favoritos
    Podemos afirmar que as obras A divina comédia, escrita por Dante Alighieri no início do século XIV, e Dom Quixote, escrita por Miguel de Cervantes no início do século XVII,
    Escolha uma alternativa para a questão 9202bdc5-af
  • 91C7187C-AF

    Português

    Interpretação de Textos    UNESP · 2013DifícilEntre para guardar nos favoritos
    Instrução: A questão toma por base uma passagem de um livro de José Ribeiro sobre o folclore nacional.



    Curupira


           Na teogonia* tupi, o anhangá, gênio andante, espírito andejo ou vagabundo, destinava-se a proteger a caça do campo. Era imaginado, segundo a tradição colhida pelo Dr. Couto de Magalhães, sob a figura de um veado branco, com olhos de fogo.
          Todo aquele que perseguisse um animal que estivesse amamentando corria o risco de ver Anhangá e a visão determinava logo a febre e, às vezes, a loucura. O caapora é o mesmo tipo mítico encontrado nas regiões central e meridional e aí representado por um homem enorme coberto de pelos negros por todo o rosto e por todo o corpo, ao qual se confiou a proteção da caça do mato. Tristonho e taciturno, anda sempre montado em um porco de grandes dimensões, dando de quando em vez um grito para impelir a vara. Quem o encontra adquire logo a certeza de ficar infeliz e de ser mal sucedido em tudo que intentar. Dele se originaram as expressões portuguesas caipora e caiporismo, como sinônimo de má sorte, infelicidade, desdita nos negócios. Bilac assim o descreve: “Companheiro do curupira, ou sua duplicata, é o Caapora, ora gigante, ora anão, montado num caititu, e cavalgando à frente de varas de porcos do mato, fumando cachimbo ou cigarro, pedindo fogo aos viajores; à frente dele voam os vaga-lumes, seus batedores, alumiando o caminho”.
         Ambos representam um só mito com diferente configuração e a mesma identidade com o curupira e o jurupari, numes que guardam a floresta. Todos convergem mais ou menos para o mesmo fim, sendo que o curupira é representado na região setentrional por um “pequeno tapuio” com os pés voltados para trás e sem os orifícios necessários para as secreções indispensáveis à vida, pelo que a gente do Pará diz que ele é músico. O Curupira ou Currupira, como é chamado no sul, aliás erroneamente, figura em uma infinidade de lendas tanto no norte como no sul do Brasil. No Pará, quando se viaja pelos rios e se ouve alguma pancada longínqua no meio dos bosques, “os romeiros dizem que é o Curupira que está batendo nas sapupemas, a ver se as árvores estão suficientemente fortes para sofrerem a ação de alguma tempestade que está próxima. A função do Curupira é proteger as florestas. Todo aquele que derriba, ou por qualquer modo estraga inutilmente as árvores, é punido por ele com a pena de errar tempos imensos pelos bosques, sem poder atinar com o caminho de casa, ou meio algum de chegar até os seus”. Como se vê, qualquer desses tipos é a manifestação de um só mito em regiões e circunstâncias diferentes.

    (O Brasil no folclore, 1970.)



    (*) Teogonia, s.f.: 1. Filos. Doutrina mística relativa ao nascimento dos deuses, e que frequentemente se relaciona com a formação do mundo. 2. Conjunto de divindades cujo culto forma o sistema religioso dum povo politeísta. (Dicionário Aurélio Eletrônico – Século XXI.)
    Anhangá e Caapora se identificam, segundo o texto, pelo fato de caracterizarem
    Escolha uma alternativa para a questão 91c7187c-af