Questões de Concurso Sobre interpretação de texto | reading comprehension em inglês

Foram encontradas 12.921 questões

www.qconcursos.com
Q3780406
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: FGV Órgão: CGE-SP Provas: FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Auditoria | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Tecnologia da Informação | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Correição e Combate à Corrupção | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Obras e Concessões | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Contabilidade Pública e Finanças |
Q3780406 Inglês
Read the following text and answer the questions.


Artificial Intelligence: The “lethal trifecta”

    LARGE LANGUAGE MODELS (LLMs), a trendy way of building artificial intelligence, have an inherent security problem: they cannot separate code from data. As a result, they are at risk of a type of attack called a prompt injection, in which they are tricked into following commands they should not. Sometimes the result is merely embarrassing, as when a customer-help agent is persuaded to talk like a pirate. On other occasions, it is far more damaging.

    The worst effects of this flaw are reserved for those who create what is known as the “lethal trifecta”. If a company, eager to offer a powerful AI assistant to its employees, gives an LLM access to untrusted data, the ability to read valuable secrets and the ability to communicate with the outside world at the same time, then trouble is sure to follow. And avoiding this is not just a matter for AI engineers. Ordinary users, too, need to learn how to use AI safely, because installing the wrong combination of apps can generate the trifecta accidentally. 

   Better AI engineering is, though, the first line of defence. And that means AI engineers need to start thinking like engineers, who build things like bridges and therefore know that shoddy work costs lives.

  The great works of Victorian England were erected by engineers who could not be sure of the properties of the materials they were using. In particular, whether by incompetence or malfeasance, the iron of the period was often not up to snuff. As a consequence, engineers erred on the side of caution, overbuilding to incorporate redundancy into their creations. The result was a series of centuries-spanning masterpieces.

   AI-security providers do not think like this. Conventional coding is a deterministic practice. Security vulnerabilities are seen as errors to be fixed, and when fixed, they go away. AI engineers, inculcated in this way of thinking from their schooldays, therefore often act as if problems can be solved just with more training data and more astute system prompts.

   These do, indeed, reduce risk. The cleverest frontier models are better at spotting and refusing malicious requests than their older or smaller cousins. But they cannot eliminate risk altogether. Unlike most software, LLMs are probabilistic. Their output is driven by random selection from likely responses. A deterministic approach to safety is thus inadequate. A better way forward is to copy engineers in the physical world and learn to work with, rather than against, capricious systems that can never be guaranteed to function as they should. That means becoming happier dealing with unpredictability by introducing safety margins, risk tolerance and error rates.

   Overbuilding in the AI age might, for instance, mean using a more powerful model than is needed for the task at hand, to reduce the risk it will be tricked into doing something inappropriate. It might mean imposing limits on the number of queries LLMs can take from external sources, calibrated to the risk of damage from a malicious query. And mechanical engineering emphasises failing safely. If an AI system must have access to secrets, then avoid handing it the keys to the kingdom.

   In the physical world, bridges have weight limits – even if they are not always stated clearly to drivers. And, importantly, these are well within the actual tolerances that calculations suggest a bridge will bear. The time has now come for the virtual world of AI systems to be similarly equipped.

Adapted from The Economist, September 27th, 2025, p. 10
The metaphor used in avoid handing it the keys to the kingdom (7th paragraph) means avoid giving the system
Alternativas
Q3780405
Inglês Interpretação de texto | Reading comprehension , Sinônimos | Synonyms ,
Ano: 2025 Banca: FGV Órgão: CGE-SP Provas: FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Auditoria | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Tecnologia da Informação | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Correição e Combate à Corrupção | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Obras e Concessões | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Contabilidade Pública e Finanças |
Q3780405 Inglês
Read the following text and answer the questions.


Artificial Intelligence: The “lethal trifecta”

    LARGE LANGUAGE MODELS (LLMs), a trendy way of building artificial intelligence, have an inherent security problem: they cannot separate code from data. As a result, they are at risk of a type of attack called a prompt injection, in which they are tricked into following commands they should not. Sometimes the result is merely embarrassing, as when a customer-help agent is persuaded to talk like a pirate. On other occasions, it is far more damaging.

    The worst effects of this flaw are reserved for those who create what is known as the “lethal trifecta”. If a company, eager to offer a powerful AI assistant to its employees, gives an LLM access to untrusted data, the ability to read valuable secrets and the ability to communicate with the outside world at the same time, then trouble is sure to follow. And avoiding this is not just a matter for AI engineers. Ordinary users, too, need to learn how to use AI safely, because installing the wrong combination of apps can generate the trifecta accidentally. 

   Better AI engineering is, though, the first line of defence. And that means AI engineers need to start thinking like engineers, who build things like bridges and therefore know that shoddy work costs lives.

  The great works of Victorian England were erected by engineers who could not be sure of the properties of the materials they were using. In particular, whether by incompetence or malfeasance, the iron of the period was often not up to snuff. As a consequence, engineers erred on the side of caution, overbuilding to incorporate redundancy into their creations. The result was a series of centuries-spanning masterpieces.

   AI-security providers do not think like this. Conventional coding is a deterministic practice. Security vulnerabilities are seen as errors to be fixed, and when fixed, they go away. AI engineers, inculcated in this way of thinking from their schooldays, therefore often act as if problems can be solved just with more training data and more astute system prompts.

   These do, indeed, reduce risk. The cleverest frontier models are better at spotting and refusing malicious requests than their older or smaller cousins. But they cannot eliminate risk altogether. Unlike most software, LLMs are probabilistic. Their output is driven by random selection from likely responses. A deterministic approach to safety is thus inadequate. A better way forward is to copy engineers in the physical world and learn to work with, rather than against, capricious systems that can never be guaranteed to function as they should. That means becoming happier dealing with unpredictability by introducing safety margins, risk tolerance and error rates.

   Overbuilding in the AI age might, for instance, mean using a more powerful model than is needed for the task at hand, to reduce the risk it will be tricked into doing something inappropriate. It might mean imposing limits on the number of queries LLMs can take from external sources, calibrated to the risk of damage from a malicious query. And mechanical engineering emphasises failing safely. If an AI system must have access to secrets, then avoid handing it the keys to the kingdom.

   In the physical world, bridges have weight limits – even if they are not always stated clearly to drivers. And, importantly, these are well within the actual tolerances that calculations suggest a bridge will bear. The time has now come for the virtual world of AI systems to be similarly equipped.

Adapted from The Economist, September 27th, 2025, p. 10
Introducing in by introducing safety margins (6th paragraph) is similar in meaning to 
Alternativas
Q3780404
Inglês Interpretação de texto | Reading comprehension , Vocabulário | Vocabulary ,
Ano: 2025 Banca: FGV Órgão: CGE-SP Provas: FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Auditoria | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Tecnologia da Informação | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Correição e Combate à Corrupção | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Obras e Concessões | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Contabilidade Pública e Finanças |
Q3780404 Inglês
Read the following text and answer the questions.


Artificial Intelligence: The “lethal trifecta”

    LARGE LANGUAGE MODELS (LLMs), a trendy way of building artificial intelligence, have an inherent security problem: they cannot separate code from data. As a result, they are at risk of a type of attack called a prompt injection, in which they are tricked into following commands they should not. Sometimes the result is merely embarrassing, as when a customer-help agent is persuaded to talk like a pirate. On other occasions, it is far more damaging.

    The worst effects of this flaw are reserved for those who create what is known as the “lethal trifecta”. If a company, eager to offer a powerful AI assistant to its employees, gives an LLM access to untrusted data, the ability to read valuable secrets and the ability to communicate with the outside world at the same time, then trouble is sure to follow. And avoiding this is not just a matter for AI engineers. Ordinary users, too, need to learn how to use AI safely, because installing the wrong combination of apps can generate the trifecta accidentally. 

   Better AI engineering is, though, the first line of defence. And that means AI engineers need to start thinking like engineers, who build things like bridges and therefore know that shoddy work costs lives.

  The great works of Victorian England were erected by engineers who could not be sure of the properties of the materials they were using. In particular, whether by incompetence or malfeasance, the iron of the period was often not up to snuff. As a consequence, engineers erred on the side of caution, overbuilding to incorporate redundancy into their creations. The result was a series of centuries-spanning masterpieces.

   AI-security providers do not think like this. Conventional coding is a deterministic practice. Security vulnerabilities are seen as errors to be fixed, and when fixed, they go away. AI engineers, inculcated in this way of thinking from their schooldays, therefore often act as if problems can be solved just with more training data and more astute system prompts.

   These do, indeed, reduce risk. The cleverest frontier models are better at spotting and refusing malicious requests than their older or smaller cousins. But they cannot eliminate risk altogether. Unlike most software, LLMs are probabilistic. Their output is driven by random selection from likely responses. A deterministic approach to safety is thus inadequate. A better way forward is to copy engineers in the physical world and learn to work with, rather than against, capricious systems that can never be guaranteed to function as they should. That means becoming happier dealing with unpredictability by introducing safety margins, risk tolerance and error rates.

   Overbuilding in the AI age might, for instance, mean using a more powerful model than is needed for the task at hand, to reduce the risk it will be tricked into doing something inappropriate. It might mean imposing limits on the number of queries LLMs can take from external sources, calibrated to the risk of damage from a malicious query. And mechanical engineering emphasises failing safely. If an AI system must have access to secrets, then avoid handing it the keys to the kingdom.

   In the physical world, bridges have weight limits – even if they are not always stated clearly to drivers. And, importantly, these are well within the actual tolerances that calculations suggest a bridge will bear. The time has now come for the virtual world of AI systems to be similarly equipped.

Adapted from The Economist, September 27th, 2025, p. 10
The phrase shoddy work costs lives (3rd paragraph) refers to work that is 
Alternativas
Q3780402
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: FGV Órgão: CGE-SP Provas: FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Auditoria | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Tecnologia da Informação | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Correição e Combate à Corrupção | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Obras e Concessões | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Contabilidade Pública e Finanças |
Q3780402 Inglês
Read the following text and answer the questions.


Artificial Intelligence: The “lethal trifecta”

    LARGE LANGUAGE MODELS (LLMs), a trendy way of building artificial intelligence, have an inherent security problem: they cannot separate code from data. As a result, they are at risk of a type of attack called a prompt injection, in which they are tricked into following commands they should not. Sometimes the result is merely embarrassing, as when a customer-help agent is persuaded to talk like a pirate. On other occasions, it is far more damaging.

    The worst effects of this flaw are reserved for those who create what is known as the “lethal trifecta”. If a company, eager to offer a powerful AI assistant to its employees, gives an LLM access to untrusted data, the ability to read valuable secrets and the ability to communicate with the outside world at the same time, then trouble is sure to follow. And avoiding this is not just a matter for AI engineers. Ordinary users, too, need to learn how to use AI safely, because installing the wrong combination of apps can generate the trifecta accidentally. 

   Better AI engineering is, though, the first line of defence. And that means AI engineers need to start thinking like engineers, who build things like bridges and therefore know that shoddy work costs lives.

  The great works of Victorian England were erected by engineers who could not be sure of the properties of the materials they were using. In particular, whether by incompetence or malfeasance, the iron of the period was often not up to snuff. As a consequence, engineers erred on the side of caution, overbuilding to incorporate redundancy into their creations. The result was a series of centuries-spanning masterpieces.

   AI-security providers do not think like this. Conventional coding is a deterministic practice. Security vulnerabilities are seen as errors to be fixed, and when fixed, they go away. AI engineers, inculcated in this way of thinking from their schooldays, therefore often act as if problems can be solved just with more training data and more astute system prompts.

   These do, indeed, reduce risk. The cleverest frontier models are better at spotting and refusing malicious requests than their older or smaller cousins. But they cannot eliminate risk altogether. Unlike most software, LLMs are probabilistic. Their output is driven by random selection from likely responses. A deterministic approach to safety is thus inadequate. A better way forward is to copy engineers in the physical world and learn to work with, rather than against, capricious systems that can never be guaranteed to function as they should. That means becoming happier dealing with unpredictability by introducing safety margins, risk tolerance and error rates.

   Overbuilding in the AI age might, for instance, mean using a more powerful model than is needed for the task at hand, to reduce the risk it will be tricked into doing something inappropriate. It might mean imposing limits on the number of queries LLMs can take from external sources, calibrated to the risk of damage from a malicious query. And mechanical engineering emphasises failing safely. If an AI system must have access to secrets, then avoid handing it the keys to the kingdom.

   In the physical world, bridges have weight limits – even if they are not always stated clearly to drivers. And, importantly, these are well within the actual tolerances that calculations suggest a bridge will bear. The time has now come for the virtual world of AI systems to be similarly equipped.

Adapted from The Economist, September 27th, 2025, p. 10
By referring to LLMs as a trendy way of building artificial intelligence (1st paragraph), the author implies they are 
Alternativas
Q3780401
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: FGV Órgão: CGE-SP Provas: FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Auditoria | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Tecnologia da Informação | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Correição e Combate à Corrupção | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Obras e Concessões | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Contabilidade Pública e Finanças |
Q3780401 Inglês
Read the following text and answer the questions.


Artificial Intelligence: The “lethal trifecta”

    LARGE LANGUAGE MODELS (LLMs), a trendy way of building artificial intelligence, have an inherent security problem: they cannot separate code from data. As a result, they are at risk of a type of attack called a prompt injection, in which they are tricked into following commands they should not. Sometimes the result is merely embarrassing, as when a customer-help agent is persuaded to talk like a pirate. On other occasions, it is far more damaging.

    The worst effects of this flaw are reserved for those who create what is known as the “lethal trifecta”. If a company, eager to offer a powerful AI assistant to its employees, gives an LLM access to untrusted data, the ability to read valuable secrets and the ability to communicate with the outside world at the same time, then trouble is sure to follow. And avoiding this is not just a matter for AI engineers. Ordinary users, too, need to learn how to use AI safely, because installing the wrong combination of apps can generate the trifecta accidentally. 

   Better AI engineering is, though, the first line of defence. And that means AI engineers need to start thinking like engineers, who build things like bridges and therefore know that shoddy work costs lives.

  The great works of Victorian England were erected by engineers who could not be sure of the properties of the materials they were using. In particular, whether by incompetence or malfeasance, the iron of the period was often not up to snuff. As a consequence, engineers erred on the side of caution, overbuilding to incorporate redundancy into their creations. The result was a series of centuries-spanning masterpieces.

   AI-security providers do not think like this. Conventional coding is a deterministic practice. Security vulnerabilities are seen as errors to be fixed, and when fixed, they go away. AI engineers, inculcated in this way of thinking from their schooldays, therefore often act as if problems can be solved just with more training data and more astute system prompts.

   These do, indeed, reduce risk. The cleverest frontier models are better at spotting and refusing malicious requests than their older or smaller cousins. But they cannot eliminate risk altogether. Unlike most software, LLMs are probabilistic. Their output is driven by random selection from likely responses. A deterministic approach to safety is thus inadequate. A better way forward is to copy engineers in the physical world and learn to work with, rather than against, capricious systems that can never be guaranteed to function as they should. That means becoming happier dealing with unpredictability by introducing safety margins, risk tolerance and error rates.

   Overbuilding in the AI age might, for instance, mean using a more powerful model than is needed for the task at hand, to reduce the risk it will be tricked into doing something inappropriate. It might mean imposing limits on the number of queries LLMs can take from external sources, calibrated to the risk of damage from a malicious query. And mechanical engineering emphasises failing safely. If an AI system must have access to secrets, then avoid handing it the keys to the kingdom.

   In the physical world, bridges have weight limits – even if they are not always stated clearly to drivers. And, importantly, these are well within the actual tolerances that calculations suggest a bridge will bear. The time has now come for the virtual world of AI systems to be similarly equipped.

Adapted from The Economist, September 27th, 2025, p. 10
The author compares AI and 19th century engineers to argue that the latter were
Alternativas
Q3780400
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: FGV Órgão: CGE-SP Provas: FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Auditoria | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Tecnologia da Informação | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Correição e Combate à Corrupção | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Obras e Concessões | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Contabilidade Pública e Finanças |
Q3780400 Inglês
Read the following text and answer the questions.


Artificial Intelligence: The “lethal trifecta”

    LARGE LANGUAGE MODELS (LLMs), a trendy way of building artificial intelligence, have an inherent security problem: they cannot separate code from data. As a result, they are at risk of a type of attack called a prompt injection, in which they are tricked into following commands they should not. Sometimes the result is merely embarrassing, as when a customer-help agent is persuaded to talk like a pirate. On other occasions, it is far more damaging.

    The worst effects of this flaw are reserved for those who create what is known as the “lethal trifecta”. If a company, eager to offer a powerful AI assistant to its employees, gives an LLM access to untrusted data, the ability to read valuable secrets and the ability to communicate with the outside world at the same time, then trouble is sure to follow. And avoiding this is not just a matter for AI engineers. Ordinary users, too, need to learn how to use AI safely, because installing the wrong combination of apps can generate the trifecta accidentally. 

   Better AI engineering is, though, the first line of defence. And that means AI engineers need to start thinking like engineers, who build things like bridges and therefore know that shoddy work costs lives.

  The great works of Victorian England were erected by engineers who could not be sure of the properties of the materials they were using. In particular, whether by incompetence or malfeasance, the iron of the period was often not up to snuff. As a consequence, engineers erred on the side of caution, overbuilding to incorporate redundancy into their creations. The result was a series of centuries-spanning masterpieces.

   AI-security providers do not think like this. Conventional coding is a deterministic practice. Security vulnerabilities are seen as errors to be fixed, and when fixed, they go away. AI engineers, inculcated in this way of thinking from their schooldays, therefore often act as if problems can be solved just with more training data and more astute system prompts.

   These do, indeed, reduce risk. The cleverest frontier models are better at spotting and refusing malicious requests than their older or smaller cousins. But they cannot eliminate risk altogether. Unlike most software, LLMs are probabilistic. Their output is driven by random selection from likely responses. A deterministic approach to safety is thus inadequate. A better way forward is to copy engineers in the physical world and learn to work with, rather than against, capricious systems that can never be guaranteed to function as they should. That means becoming happier dealing with unpredictability by introducing safety margins, risk tolerance and error rates.

   Overbuilding in the AI age might, for instance, mean using a more powerful model than is needed for the task at hand, to reduce the risk it will be tricked into doing something inappropriate. It might mean imposing limits on the number of queries LLMs can take from external sources, calibrated to the risk of damage from a malicious query. And mechanical engineering emphasises failing safely. If an AI system must have access to secrets, then avoid handing it the keys to the kingdom.

   In the physical world, bridges have weight limits – even if they are not always stated clearly to drivers. And, importantly, these are well within the actual tolerances that calculations suggest a bridge will bear. The time has now come for the virtual world of AI systems to be similarly equipped.

Adapted from The Economist, September 27th, 2025, p. 10

Based on the text, mark the statements below as true (T) or false (F).


I. AI models are watertight when it comes to safety risks.

II Bridges built in the Victorian Age were proven to be quite fragile.

III. A deterministic model does not deal with randomness.


The statements are, respectively,

Alternativas
Q3778306
Inglês Pronomes | Pronouns , Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: Instituto Fênix Órgão: Prefeitura de São Domingos - SC Prova: Instituto Fênix - 2025 - Prefeitura de São Domingos - SC - Professor de Língua Estrangeira - Inglês |
Q3778306 Inglês

Are Some Sugars ‘Less Bad’ Than Others?


        Q: I’m trying to limit sugar, but I love sweets. Are “natural” sweeteners like honey and agave syrup healthier alternatives to table sugar?


         You probably know that the sugars in fruits, vegetables and other plants are far better for you than the added sugars often found in processed foods like sodas, candy bars and many baked goods.


         But in that category of added sugars, there’s an array of sweeteners that are often seen as more “natural” or healthier than others. Honey, maple syrup and agave nectar, for instance, are commonly touted as “better for you” swaps for regular sugar, such as in many health-focused baking recipes and on social media.


        Is that right? We asked three nutrition experts to help us sort it out. 


Source: 

https://www.nytimes.com/2025/10/28/well/eat/health-

effects-honey-maple-syrup-agave.html 

In the question “Is that right?” the word that refers to: 
Alternativas
Q3778305
Inglês Interpretação de texto | Reading comprehension , Sinônimos | Synonyms ,
Ano: 2025 Banca: Instituto Fênix Órgão: Prefeitura de São Domingos - SC Prova: Instituto Fênix - 2025 - Prefeitura de São Domingos - SC - Professor de Língua Estrangeira - Inglês |
Q3778305 Inglês

Are Some Sugars ‘Less Bad’ Than Others?


        Q: I’m trying to limit sugar, but I love sweets. Are “natural” sweeteners like honey and agave syrup healthier alternatives to table sugar?


         You probably know that the sugars in fruits, vegetables and other plants are far better for you than the added sugars often found in processed foods like sodas, candy bars and many baked goods.


         But in that category of added sugars, there’s an array of sweeteners that are often seen as more “natural” or healthier than others. Honey, maple syrup and agave nectar, for instance, are commonly touted as “better for you” swaps for regular sugar, such as in many health-focused baking recipes and on social media.


        Is that right? We asked three nutrition experts to help us sort it out. 


Source: 

https://www.nytimes.com/2025/10/28/well/eat/health-

effects-honey-maple-syrup-agave.html 

In the sentence “I’m trying to limit sugar, but I love sweets,” the word limit is closest in meaning to:
Alternativas
Q3778304
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: Instituto Fênix Órgão: Prefeitura de São Domingos - SC Prova: Instituto Fênix - 2025 - Prefeitura de São Domingos - SC - Professor de Língua Estrangeira - Inglês |
Q3778304 Inglês

Are Some Sugars ‘Less Bad’ Than Others?


        Q: I’m trying to limit sugar, but I love sweets. Are “natural” sweeteners like honey and agave syrup healthier alternatives to table sugar?


         You probably know that the sugars in fruits, vegetables and other plants are far better for you than the added sugars often found in processed foods like sodas, candy bars and many baked goods.


         But in that category of added sugars, there’s an array of sweeteners that are often seen as more “natural” or healthier than others. Honey, maple syrup and agave nectar, for instance, are commonly touted as “better for you” swaps for regular sugar, such as in many health-focused baking recipes and on social media.


        Is that right? We asked three nutrition experts to help us sort it out. 


Source: 

https://www.nytimes.com/2025/10/28/well/eat/health-

effects-honey-maple-syrup-agave.html 

Segundo o texto, alguns adoçantes como mel e xarope de agave são frequentemente divulgados em redes sociais como substitutos “melhores” do que o açúcar comum. Assinale a alternativa incorreta sobre a forma como o texto apresenta essa percepção. 
Alternativas
Q3778303
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: Instituto Fênix Órgão: Prefeitura de São Domingos - SC Prova: Instituto Fênix - 2025 - Prefeitura de São Domingos - SC - Professor de Língua Estrangeira - Inglês |
Q3778303 Inglês

Are Some Sugars ‘Less Bad’ Than Others?


        Q: I’m trying to limit sugar, but I love sweets. Are “natural” sweeteners like honey and agave syrup healthier alternatives to table sugar?


         You probably know that the sugars in fruits, vegetables and other plants are far better for you than the added sugars often found in processed foods like sodas, candy bars and many baked goods.


         But in that category of added sugars, there’s an array of sweeteners that are often seen as more “natural” or healthier than others. Honey, maple syrup and agave nectar, for instance, are commonly touted as “better for you” swaps for regular sugar, such as in many health-focused baking recipes and on social media.


        Is that right? We asked three nutrition experts to help us sort it out. 


Source: 

https://www.nytimes.com/2025/10/28/well/eat/health-

effects-honey-maple-syrup-agave.html 

No trecho inicial, o autor afirma que os açúcares presentes em frutas e vegetais são “far better for you” do que os açúcares adicionados. Essa informação permite concluir que: 
Alternativas
Q3774235
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: AMAUC Órgão: Prefeitura de Itá - SC Prova: AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774235 Inglês
Reading comprehension involves understanding both explicit information and implicit meanings within a text. Select the alternative that correctly identifies the main idea of a text stating: "While many believe technology isolates people, studies show it can actually enhance connectivity across long distances."
Alternativas
Q3773719
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: IBAM Órgão: Prefeitura de Arapiraca - AL Prova: IBAM - 2025 - Prefeitura de Arapiraca - AL - Professor de Inglês |
Q3773719 Inglês

Read the text below and answer question


Plan to test Liberian schoolchildren for drugs blocked

October 17th, 2025

By Moses Kollie Garzeawu

Monrovia, Liberia, Africa


Liberia's Education Ministry has blocked controversial plans to introduce mandatory drug testing in all of the country's schools.


Speaking to local media, the interim head of the Liberia Drug Enforcement Agency (LDEA), Fitzgerald Biago, said school testing would help address the growing problem of drug abuse.


The announcement sparked a mixed response. Some thought it would help tackle the scourge of drugs, while others saw it as an invasion of privacy, or feared it would cost too much.


Last year, President Joseph Boakai declared drug and substance abuse a national emergency and a recent EU-backed report estimated that one in five young Liberians take drugs.


However, the Education Ministry said it was not aware of any plans to test students and added that such a decision needed to be based on concrete evidence and properly thought through.


Assistant minister in charge of students Sona Toure-Sesay told the BBC that this kind of plan required proper research. "Let's assume we are made aware of the proposed initiatives by the LDEA, it will require us to conduct research and review case studies from other countries where this has been successful," she said.  


Toure-Sesay also noted that testing could affect students. "What happens to students who test positive? What are the social services in place for them? Some of them might be bullied even after returning, and it may affect their overall educational performances."


She added that a multi-sectoral committee on drug and substance abuse had been set up, headed by the Health Ministry. Along with strengthening health clubs in schools, she said that this would help to reduce the prevalence of drugs among students.


President Boakai dismissed the leadership of the LDEA in August this year, and recently appointed Biago, a former senior police officer, as interim head of the agency.



Taken from:

https://www.bbc.com/news/articles/c0mxz3x1lr7o  

The sentence “The announcement sparked a mixed response” (paragraph 3) means that:
Alternativas
Q3773717
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: IBAM Órgão: Prefeitura de Arapiraca - AL Prova: IBAM - 2025 - Prefeitura de Arapiraca - AL - Professor de Inglês |
Q3773717 Inglês

Read the text below and answer question


Plan to test Liberian schoolchildren for drugs blocked

October 17th, 2025

By Moses Kollie Garzeawu

Monrovia, Liberia, Africa


Liberia's Education Ministry has blocked controversial plans to introduce mandatory drug testing in all of the country's schools.


Speaking to local media, the interim head of the Liberia Drug Enforcement Agency (LDEA), Fitzgerald Biago, said school testing would help address the growing problem of drug abuse.


The announcement sparked a mixed response. Some thought it would help tackle the scourge of drugs, while others saw it as an invasion of privacy, or feared it would cost too much.


Last year, President Joseph Boakai declared drug and substance abuse a national emergency and a recent EU-backed report estimated that one in five young Liberians take drugs.


However, the Education Ministry said it was not aware of any plans to test students and added that such a decision needed to be based on concrete evidence and properly thought through.


Assistant minister in charge of students Sona Toure-Sesay told the BBC that this kind of plan required proper research. "Let's assume we are made aware of the proposed initiatives by the LDEA, it will require us to conduct research and review case studies from other countries where this has been successful," she said.  


Toure-Sesay also noted that testing could affect students. "What happens to students who test positive? What are the social services in place for them? Some of them might be bullied even after returning, and it may affect their overall educational performances."


She added that a multi-sectoral committee on drug and substance abuse had been set up, headed by the Health Ministry. Along with strengthening health clubs in schools, she said that this would help to reduce the prevalence of drugs among students.


President Boakai dismissed the leadership of the LDEA in August this year, and recently appointed Biago, a former senior police officer, as interim head of the agency.



Taken from:

https://www.bbc.com/news/articles/c0mxz3x1lr7o  

Assistant Minister in charge of students Sona Toure-Sesay is concerned about the drug testing plan because: 
Alternativas
Q3773716
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: IBAM Órgão: Prefeitura de Arapiraca - AL Prova: IBAM - 2025 - Prefeitura de Arapiraca - AL - Professor de Inglês |
Q3773716 Inglês

Read the text below and answer question


Plan to test Liberian schoolchildren for drugs blocked

October 17th, 2025

By Moses Kollie Garzeawu

Monrovia, Liberia, Africa


Liberia's Education Ministry has blocked controversial plans to introduce mandatory drug testing in all of the country's schools.


Speaking to local media, the interim head of the Liberia Drug Enforcement Agency (LDEA), Fitzgerald Biago, said school testing would help address the growing problem of drug abuse.


The announcement sparked a mixed response. Some thought it would help tackle the scourge of drugs, while others saw it as an invasion of privacy, or feared it would cost too much.


Last year, President Joseph Boakai declared drug and substance abuse a national emergency and a recent EU-backed report estimated that one in five young Liberians take drugs.


However, the Education Ministry said it was not aware of any plans to test students and added that such a decision needed to be based on concrete evidence and properly thought through.


Assistant minister in charge of students Sona Toure-Sesay told the BBC that this kind of plan required proper research. "Let's assume we are made aware of the proposed initiatives by the LDEA, it will require us to conduct research and review case studies from other countries where this has been successful," she said.  


Toure-Sesay also noted that testing could affect students. "What happens to students who test positive? What are the social services in place for them? Some of them might be bullied even after returning, and it may affect their overall educational performances."


She added that a multi-sectoral committee on drug and substance abuse had been set up, headed by the Health Ministry. Along with strengthening health clubs in schools, she said that this would help to reduce the prevalence of drugs among students.


President Boakai dismissed the leadership of the LDEA in August this year, and recently appointed Biago, a former senior police officer, as interim head of the agency.



Taken from:

https://www.bbc.com/news/articles/c0mxz3x1lr7o  

The LDEA interim head Fitzgerald Biago suggested a mandatory drug testing in all of the country’s schools because:  
Alternativas
Q3773452
Inglês Interpretação de texto | Reading comprehension , Ensino da Língua Estrangeira Inglesa ,
Ano: 2025 Banca: AMEOSC Órgão: Prefeitura de Palma Sola - SC Prova: AMEOSC - 2025 - Prefeitura de Palma Sola - SC - Professor de Inglês - Edital nº 12 |
Q3773452 Inglês
The integration of digital technologies in language teaching offers new possibilities for engagement. Mark T (True) or F (False) for the statements regarding the use of technology.


(__)Gamification involves using game design elements in non-game contexts to motivate students and enhance learning.


(__)The BNCC includes digital culture as a key competency, encouraging the critical and ethical use of technology.


(__)Technology should be used to replace the teacher entirely, as apps can explain grammar better than humans


(__)Digital resources allow for multimodal practices, combining text, audio, and video to support different learning styles.


Select the alternative that presents the correct sequence, from top to bottom.  
Alternativas
Q3773451
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: AMEOSC Órgão: Prefeitura de Palma Sola - SC Prova: AMEOSC - 2025 - Prefeitura de Palma Sola - SC - Professor de Inglês - Edital nº 12 |
Q3773451 Inglês
Reading strategies are essential for efficient comprehension. Select the alternative that correctly defines the strategy known as "Scanning". 
Alternativas
Q3771573
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: IDESG Órgão: Prefeitura de Alto Rio Novo - ES Prova: IDESG - 2025 - Prefeitura de Alto Rio Novo - ES - PEB-II - Professor de Língua Estrangeira Moderna - Inglês |
Q3771573 Inglês
Read the text and answer the question.
Schools often adopt digital platforms quickly because purchase cycles reward novelty and visibility. Login rates and dashboard activity are paraded as evidence of impact, even when teachers report that the dashboards narrow what counts as learning. A teacher who resists a mandated platform is frequently labeled a laggard. Yet hesitation can be prudent: when pacing is dictated by a metric, attention may shift from understanding to what the metric can capture. The argument here is not to reject technology. It is to renegotiate control—who sets the goals, which data matter, and what should remain off the record. Only then can professional judgment act as a productive constraint, preventing means from swallowing ends.
Which statement best captures the author’s central claim?
Alternativas
Q3771447
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: Unesc Órgão: Prefeitura de Morro da Fumaça - SC Prova: Unesc - 2025 - Prefeitura de Morro da Fumaça - SC - Professor de Língua Inglesa |
Q3771447 Inglês
When reading a text, the "main idea" is the central point or message the author wants to communicate. In a well-structured paragraph, where is the main idea often found? 
Alternativas
Q3771445
Inglês Interpretação de texto | Reading comprehension , Ensino da Língua Estrangeira Inglesa ,
Ano: 2025 Banca: Unesc Órgão: Prefeitura de Morro da Fumaça - SC Prova: Unesc - 2025 - Prefeitura de Morro da Fumaça - SC - Professor de Língua Inglesa |
Q3771445 Inglês
When teaching reading, a teacher asks students to look at the title, pictures, and headings of a text before they start reading, in order to guess what the text is about. What is this pre-reading strategy called?
Alternativas
Q3771337
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: AMEOSC Órgão: Prefeitura de Itapiranga - SC Prova: AMEOSC - 2025 - Prefeitura de Itapiranga - SC - Professor de Língua Estrangeira (Inglês) |
Q3771337 Inglês
Reading strategies are cognitive tools that help the reader construct the meaning of a text. Which alternative correctly describes the skimming strategy?
Alternativas
Respostas
1041: A
1042: C
1043: E
1044: E
1045: A
1046: C
1047: B
1048: A
1049: D
1050: D
1051: A
1052: D
1053: B
1054: C
1055: D
1056: D
1057: C
1058: D
1059: B
1060: B
www.qconcursos.com