Questões de Concurso
Comentadas sobre interpretação de texto | reading comprehension em inglês
Foram encontradas 8.692 questões
Ano: 2025
Banca:
VUNESP
Órgão:
Prefeitura de Sorocaba - SP
Prova:
VUNESP - 2025 - Prefeitura de Sorocaba - SP - Professor de Educação Básica II - Inglês |
Q3781906
Inglês
Texto associado
Leia o texto a seguir para responder a questão:
[…] The action research cycle results show that task
design should follow a certain sequence: First, tasks should
focus on gaining an understanding of the e-literacy skills
required when working with tools such as forums, wikis, and
social bookmarking sites for language learning and teaching
purposes. Ideally, this understanding should enable teachers
to provide a rationale for using bespoke tools. Next, tasks
should raise their awareness of a tool’s specific affordances,
i.e. the constraints and possibilities of the modes available
for meaning making and communication (Hampel & Hauck,
2006). This will allow the teachers to move to the next level
of Hampel and Stickler’s (2005) skills pyramid by fostering
their multimodal communicative competence and thus
their professional literacy (Willis, 2001). These steps are
a prerequisite for the subsequent phase in which teachers
themselves design tasks with the goal of fostering, in turn,
their learners’ multimodal competence and autonomy since
merely equipping learners with creative and democratic
representational online resources will not necessarily result
in higher student control over the learning process or the
development of autonomy (Hampel & Hauck, 2006).
(Carolin Fuchs, Andreas Müller-Hartmann, Mirjam Hauck.
Promoting learner autonomy through multiliteracy
skills development in cross-institutional exchanges. Adaptado)
Based on the final phase described in the text, where
teachers design tasks to foster learner autonomy and
multimodal competence, a teacher planning a task using
a discussion forum should ensure the task
Ano: 2025
Banca:
VUNESP
Órgão:
Prefeitura de Sorocaba - SP
Prova:
VUNESP - 2025 - Prefeitura de Sorocaba - SP - Professor de Educação Básica II - Inglês |
Q3781899
Inglês
Texto associado
Leia o texto a seguir para responder a questão:
Our analysis of the Time to Share series revealed that it
follows the principles of recent theories in the development
of the learning activities although there is an excessive use
of the Portuguese language. Students are somewhat well
encouraged to actively participate but more could be done
in this sense. Further responsibility could be put upon the
students in terms of interactions and research on the web.
They are digital natives and their familiarity with this new
world can make a difference in their involvement with learning
English online for today and for the future. Their success
depends on them, and they must be encouraged to learn by
themselves.
(Reinildes Dias & Ana Emília Fajardo Turbin. The two “multis” and the
multiliteracies pedagogy: “shaking hands” in the Brazilian English
public education for teens.)
Based on the authors’ analysis, what is the most
emphatically suggested pedagogical shift needed to
enhance student involvement and leverage their status
as “digital natives” in the learning process?
Ano: 2025
Banca:
VUNESP
Órgão:
Prefeitura de Sorocaba - SP
Prova:
VUNESP - 2025 - Prefeitura de Sorocaba - SP - Professor de Educação Básica II - Inglês |
Q3781898
Inglês
Texto associado
Leia o texto a seguir para responder a questão:
The more traditional methods and approaches to teaching
culture, such as movies and video, can be enhanced through
the integration of digital media. Feature films have become
readily available and have been included in numerous
textbooks and designed to actively involve the learner (Aparisi,
Blanco, & Rinka, 2007; Blanco & Tocaimaza-Hatch, 2007).
Foreign language instructors are beginning to incorporate
more movies in the foreign language classroom as “an
accessible window” (Bueno, 2009, p. 319) to the target culture
through “combined effects of images, sounds, camera, plots
and dialogue” (Stephens, 2001, p. 2). According to Bueno
(2009), media literacy promotes cross-cultural competence
and comprehension focused on meaning rather than on
form, as well as repeated exposure to L2 cultural products,
practices, and perspectives, and the target language itself.
(Oxana Dema & Aleidine Kramer Moeller. Teaching culture
in the 21st century language classroom. Adaptado.)
Considering the author’s point of view on the use of digital
media for cultural and intercultural purposes, which of the
following activities best exemplifies an enhanced use of
films and digital media in the foreign language classroom?
Ano: 2025
Banca:
VUNESP
Órgão:
Prefeitura de Sorocaba - SP
Prova:
VUNESP - 2025 - Prefeitura de Sorocaba - SP - Professor de Educação Básica II - Inglês |
Q3781897
Inglês
Texto associado
Leia o texto a seguir para responder a questão:
The more traditional methods and approaches to teaching
culture, such as movies and video, can be enhanced through
the integration of digital media. Feature films have become
readily available and have been included in numerous
textbooks and designed to actively involve the learner (Aparisi,
Blanco, & Rinka, 2007; Blanco & Tocaimaza-Hatch, 2007).
Foreign language instructors are beginning to incorporate
more movies in the foreign language classroom as “an
accessible window” (Bueno, 2009, p. 319) to the target culture
through “combined effects of images, sounds, camera, plots
and dialogue” (Stephens, 2001, p. 2). According to Bueno
(2009), media literacy promotes cross-cultural competence
and comprehension focused on meaning rather than on
form, as well as repeated exposure to L2 cultural products,
practices, and perspectives, and the target language itself.
(Oxana Dema & Aleidine Kramer Moeller. Teaching culture
in the 21st century language classroom. Adaptado.)
According to the author, the pedagogical value of
incorporating films and digital media into foreign language
instruction is
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 |
Q3780407
Inglês
Texto associado
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 text concludes that the Victorian engineers’ decision
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
Texto associado
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
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
Texto associado
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
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
Texto associado
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
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
Texto associado
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
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
Texto associado
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
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
Texto associado
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,
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
Texto associado
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:
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
Texto associado
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:
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
Texto associado
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:
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."
Ano: 2025
Banca:
IBAM
Órgão:
Prefeitura de Arapiraca - AL
Prova:
IBAM - 2025 - Prefeitura de Arapiraca - AL - Professor de Inglês |
Q3773719
Inglês
Texto associado
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:
Ano: 2025
Banca:
IBAM
Órgão:
Prefeitura de Arapiraca - AL
Prova:
IBAM - 2025 - Prefeitura de Arapiraca - AL - Professor de Inglês |
Q3773717
Inglês
Texto associado
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:
Ano: 2025
Banca:
IBAM
Órgão:
Prefeitura de Arapiraca - AL
Prova:
IBAM - 2025 - Prefeitura de Arapiraca - AL - Professor de Inglês |
Q3773716
Inglês
Texto associado
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:
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.
(__)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.
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".
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