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Ano: 2025
Banca:
VUNESP
Órgão:
Prefeitura de Sorocaba - SP
Prova:
VUNESP - 2025 - Prefeitura de Sorocaba - SP - Professor de Educação Básica II - Inglês |
Q3781909
Inglês
Texto associado
Leia o texto a seguir para responder a questão:
The learning principles that good games incorporate
are by no means unknown to researchers in the learning
sciences. In fact current research on learning supports the
sorts of learning principles that good games use, though
these principles are often exemplified in games in particularly
striking ways (for a survey and citations of the literature, see
Gee 2003). However, many of these principles are much
better reflected in good games than they are in today’s
schools, where we also ask young people to learn complex
and challenging things. With the current return in our schools
to skill-and-drill and curricula driven by standardized tests,
good learning principles have, more and more, been left on
the cognitive scientist’s laboratory bench and, I will argue,
inside good computer and video games.
Game design involves modeling human interactions
with and within complex virtual worlds, including learning
processes as part and parcel of these interactions. This is,
in fact, not unlike design research in educational psychology
where researchers model new forms of interaction connected
to learning in classrooms (complex worlds, indeed), study
such interactions to better understand how and why they lead
to deep learning, and then ultimately disseminate them across
a great many classrooms (see, for example, the chapters in
Kelly 2003).
(James Paulo Gee. Situated Language and Learning:
a critique of traditional schooling)
According to the author, “good learning principles” are
neglected and relegated because of
Ano: 2025
Banca:
VUNESP
Órgão:
Prefeitura de Sorocaba - SP
Prova:
VUNESP - 2025 - Prefeitura de Sorocaba - SP - Professor de Educação Básica II - Inglês |
Q3781908
Inglês
Texto associado
Leia o texto a seguir para responder a questão:
Language monitor
A new topic area will quickly generate the need to acquire
new language in the form of vocabulary, structures, and
pronunciation. It is a good idea to have ready a way of coping
with this demand.
If students can feel that they have the time and opportunity
to master the use of language that either you or they have
identified as being necessary for a certain stage in a project,
this will go a long way to increasing their confidence and
language competence.
One way to do this is to produce a language monitor
which focuses on vocabulary and structures that have been
identified as being useful.
This allows other students to read it and absorb the word
or phrase, the meaning, pronunciation, associated words or
collocations, and how to use it in a sentence. They can also
add their own cards. The vocabulary monitor remains on the
noticeboard throughout the project, constantly available for
reinforcement and consolidation. It can also be used as a
source of vocabulary games.
In addition to this or as an alternative, if you have
suitable computer facilities available, electronic lists could be
created. Students can add to the lists in the same way as the
noticeboard. The updated list can be printed out at regular
intervals and put on the noticeboard and handouts given to
the students.
(Diana L. Fried-Booth. Project Work. Adaptado)
Considering the concept of the “Language Monitor,” an
English as a Foreign Language teacher best integrates
this tool to foster learner autonomy and confidence in a
project-based learning environment by
Ano: 2025
Banca:
VUNESP
Órgão:
Prefeitura de Sorocaba - SP
Prova:
VUNESP - 2025 - Prefeitura de Sorocaba - SP - Professor de Educação Básica II - Inglês |
Q3781907
Inglês
Texto associado
Leia o texto a seguir para responder a questão:
Language monitor
A new topic area will quickly generate the need to acquire
new language in the form of vocabulary, structures, and
pronunciation. It is a good idea to have ready a way of coping
with this demand.
If students can feel that they have the time and opportunity
to master the use of language that either you or they have
identified as being necessary for a certain stage in a project,
this will go a long way to increasing their confidence and
language competence.
One way to do this is to produce a language monitor
which focuses on vocabulary and structures that have been
identified as being useful.
This allows other students to read it and absorb the word
or phrase, the meaning, pronunciation, associated words or
collocations, and how to use it in a sentence. They can also
add their own cards. The vocabulary monitor remains on the
noticeboard throughout the project, constantly available for
reinforcement and consolidation. It can also be used as a
source of vocabulary games.
In addition to this or as an alternative, if you have
suitable computer facilities available, electronic lists could be
created. Students can add to the lists in the same way as the
noticeboard. The updated list can be printed out at regular
intervals and put on the noticeboard and handouts given to
the students.
(Diana L. Fried-Booth. Project Work. Adaptado)
According to the text, the main purpose of the “Language
Monitor” is to
Resumos relacionados
Resumo relacionado
Advérbios e conjunções em inglês para concursos públicos
O estudo de advérbios e conjunções na língua inglesa é fundamental para quem deseja se destacar em provas de concursos públicos. Esses elementos desempenham papéis essenciais na construção de frases, influenciando diretamente o sentido e a coesão textual, habilidades bastante exigidas nas questões de interpretação e compreensão de textos em inglês.
Resumo relacionado
Artigos (Articles) em inglês: uso em concursos públicos
Artigos (Articles) são palavras essenciais na gramática da língua inglesa, usadas para indicar se um substantivo está sendo mencionado de forma específica ou geral. Eles desempenham papel fundamental em provas de concursos, pois ajudam na compreensão e interpretação dos textos, além de serem frequentemente cobrados em questões envolvendo uso correto de estruturas gramaticais.
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 |
Q3781905
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)
The text mentions that understanding a tool’s specific
affordances involves recognizing “the constraints and
possibilities of the modes available for meaning making
and communication”.
For a language teacher, a significant implication of focusing on a tool’s constraints in task design is
For a language teacher, a significant implication of focusing on a tool’s constraints in task design is
Ano: 2025
Banca:
VUNESP
Órgão:
Prefeitura de Sorocaba - SP
Prova:
VUNESP - 2025 - Prefeitura de Sorocaba - SP - Professor de Educação Básica II - Inglês |
Q3781900
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.)
Choose the statement that most accurately conveys the
authors’ critique and suggestions for improving the Time
to Share series as presented in the text.
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
Q3781821
Inglês
Texto associado
Space power: The dream of beaming solar energy from orbit
(Available at: www.bbc.com/future/article/20251029-the-beam-dream-should-we-build-solar-farms-in-space–
text specially adapted for this test).
Based on the text as a whole, which statement best summarizes the author’s
viewpoint?
Q3781820
Inglês
Texto associado
Space power: The dream of beaming solar energy from orbit
(Available at: www.bbc.com/future/article/20251029-the-beam-dream-should-we-build-solar-farms-in-space–
text specially adapted for this test).
Analyse the following statements, according to the grammatical structures and their
meanings in the text:
I. The clause “have made it more feasible” (l. 27-28) expresses an action that began in the past and continues to have effects in the present.
II. In the sentence “It would require enormous satellite structures” (l. 21), the verb form “would require” indicates a hypothetical situation rather than a real one.
III. In the sentence “making it work is no small task” (l. 21), the structure “making it work” functions as the subject of the sentence.
IV.The structure “it was dismissed as too costly” (l. 26) refers to a past passive construction in the simple past.
Which ones are correct?
I. The clause “have made it more feasible” (l. 27-28) expresses an action that began in the past and continues to have effects in the present.
II. In the sentence “It would require enormous satellite structures” (l. 21), the verb form “would require” indicates a hypothetical situation rather than a real one.
III. In the sentence “making it work is no small task” (l. 21), the structure “making it work” functions as the subject of the sentence.
IV.The structure “it was dismissed as too costly” (l. 26) refers to a past passive construction in the simple past.
Which ones are correct?
Q3781819
Inglês
Texto associado
Space power: The dream of beaming solar energy from orbit
(Available at: www.bbc.com/future/article/20251029-the-beam-dream-should-we-build-solar-farms-in-space–
text specially adapted for this test).
Analyse the statements below according to the vocabulary used in the text, and
mark T, if true, or F, if false.
( )The word “feasible” (l. 28) could be replaced by “achievable” without changing the meaning.
( ) The prefix un– in “uncertain” (l. 38) and “unrealistic” (l. 17) indicates reversal of action, similar to the verb “undo”.
( ) The word “viable” (l. 32) refers to something that can function successfully.
( ) The term “renewable” (l. 14) is formed by the addition of the prefix re- and the suffix -able, which mean, respectively, “not” and “capability/possibility”.
The correct order of filling in the parentheses, from top to bottom, is:
( )The word “feasible” (l. 28) could be replaced by “achievable” without changing the meaning.
( ) The prefix un– in “uncertain” (l. 38) and “unrealistic” (l. 17) indicates reversal of action, similar to the verb “undo”.
( ) The word “viable” (l. 32) refers to something that can function successfully.
( ) The term “renewable” (l. 14) is formed by the addition of the prefix re- and the suffix -able, which mean, respectively, “not” and “capability/possibility”.
The correct order of filling in the parentheses, from top to bottom, 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 |
Q3780403
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 word tricked (1st paragraph) means that LLMs can be
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,
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