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Ano: 2026
Banca:
FGV
Órgão:
AMAZUL
Provas:
FGV - 2026 - AMAZUL - Advogado
|
FGV - 2026 - AMAZUL - Contador |
FGV - 2026 - AMAZUL - Designer Gráfico |
FGV - 2026 - AMAZUL - Analista de Administração |
FGV - 2026 - AMAZUL - Engenheiro Mecatrônico |
FGV - 2026 - AMAZUL - Analista de Desenvolvimento de Sistemas |
FGV - 2026 - AMAZUL - Engenheiro Naval |
FGV - 2026 - AMAZUL - Engenheiro de Computação |
FGV - 2026 - AMAZUL - Médico do Trabalho |
FGV - 2026 - AMAZUL - Psicólogo |
FGV - 2026 - AMAZUL - Analista de Infraestrutura de Tecnologia da Informação |
FGV - 2026 - AMAZUL - Engenheiro de Controle da Qualidade |
FGV - 2026 - AMAZUL - Analista de Negócios |
FGV - 2026 - AMAZUL - Arquiteto |
FGV - 2026 - AMAZUL - Auditor |
FGV - 2026 - AMAZUL - Analista de Recursos Humanos |
FGV - 2026 - AMAZUL - Engenheiro Ambiental |
FGV - 2026 - AMAZUL - Engenheiro de Materiais |
FGV - 2026 - AMAZUL - Especialista de Radioproteção |
FGV - 2026 - AMAZUL - Engenheiro Nuclear |
FGV - 2026 - AMAZUL - Engenheiro de Produção |
FGV - 2026 - AMAZUL - Engenheiro Civil |
FGV - 2026 - AMAZUL - Engenheiro Controle e Automação |
FGV - 2026 - AMAZUL - Físico |
FGV - 2026 - AMAZUL - Engenheiro Químico |
FGV - 2026 - AMAZUL - Engenheiro de Telecomunicações |
FGV - 2026 - AMAZUL - Engenheiro de Segurança do Trabalho |
FGV - 2026 - AMAZUL - Engenheiro Eletricista |
FGV - 2026 - AMAZUL - Engenheiro Eletrônico |
FGV - 2026 - AMAZUL - Meteorologista |
FGV - 2026 - AMAZUL - Químico |
FGV - 2026 - AMAZUL - Tecnólogo em Fabricação Mecânica |
FGV - 2026 - AMAZUL - Engenheiro de Energia |
FGV - 2026 - AMAZUL - Engenheiro Mecânico |
Q3846040
Inglês
Texto associado
READ THE TEXT AND ANSWER THE FOLLOWING QUESTION
Social Dimensions of Climate Change
Extreme weather events are deeply intertwined with global
patterns of inequality. The poorest and most vulnerable people
bear the brunt of climate change impacts yet contribute the least
to the crisis. As the impacts of climate change mount, millions of
vulnerable people face disproportionate challenges in terms of
loss of jobs; physical harm; disease; mental health effects; food
insecurity; access to water; migration and forced displacement;
loss of shelter, assets, and community ties, and other related risks.
Some people are more vulnerable to climate change than others.
For example, workers in sectors such as agriculture, fishing, and
tourism rely on natural resources that are particularly sensitive to
increasingly unpredictable weather and seasonal patterns.
Female-headed households, children, persons with
disabilities, Indigenous Peoples and ethnic minorities, landless
tenants, migrant workers, displaced persons, older people, and
other socially marginalized groups often have fewer financial and
other resources to cope with and recover from shocks which might
threaten their wellbeing and the wellbeing of their families. The
root causes of their vulnerability lie in a combination of their
geographical locations; their financial, socio-economic, cultural,
and social status; and their access to resources, services, and
decision-making power.
The poor are often not just among the most vulnerable to climate
change, but also disproportionately impacted by measures to
address it. These impacts can include increased costs of living, loss
of livelihoods, and limited access to resources and support
systems, which exacerbate existing inequalities and poverty
trends. In the absence of well-designed and citizen-centered
policies, efforts to tackle climate change can have unintended
consequences for the livelihoods of certain groups, including
placing a higher financial burden on poor households […].
While much progress has been made on the science and the types
of policies needed to support a transition to low carbon, climateresilient development, a challenge facing many countries is
engaging citizens who are concerned that they will be unfairly
impacted by climate policies. Citizen-centered programs play a
vital role in ensuring that resources are used efficiently. Engaging
people in shaping climate action is equally critical for achieving
lasting impact. This means ensuring transparency, access to
information, and active citizen engagement on climate risks and
green growth. Such involvement can help build public support to
reduce climate impacts, overcome behavioral and political barriers
to decarbonization, as well as foster both new ideas and a sense of
ownership over solutions.
Moreover, communities bring unique perspectives, skills, and a
wealth of knowledge to the challenge of strengthening resilience
and addressing climate change. They should be engaged as
partners in resilience-building rather than being regarded merely
as beneficiaries. Research and experience show that community
leaders can successfully set priorities, influence ownership, as well
as design and implement investment programs that are responsive
to their community’s own needs. A 2022 report by the
Intergovernmental Panel on Climate Change (IPCC) recognizes the
value of diverse forms of knowledge — such as scientific,
Indigenous, and local knowledge — in building climate resilience.
Innovations in the architecture of climate finance can connect
communities and marginalized groups to the policy, technical, and
financial assistance that they need for locally relevant and
effective development outcomes.
From: https://www.worldbank.org/en/topic/social-dimensions-of-climate-change
“Yet” in “yet contribute the least” (1st paragraph) introduces an
idea of:
Ano: 2026
Banca:
FGV
Órgão:
AMAZUL
Provas:
FGV - 2026 - AMAZUL - Advogado
|
FGV - 2026 - AMAZUL - Contador |
FGV - 2026 - AMAZUL - Designer Gráfico |
FGV - 2026 - AMAZUL - Analista de Administração |
FGV - 2026 - AMAZUL - Engenheiro Mecatrônico |
FGV - 2026 - AMAZUL - Analista de Desenvolvimento de Sistemas |
FGV - 2026 - AMAZUL - Engenheiro Naval |
FGV - 2026 - AMAZUL - Engenheiro de Computação |
FGV - 2026 - AMAZUL - Médico do Trabalho |
FGV - 2026 - AMAZUL - Psicólogo |
FGV - 2026 - AMAZUL - Analista de Infraestrutura de Tecnologia da Informação |
FGV - 2026 - AMAZUL - Engenheiro de Controle da Qualidade |
FGV - 2026 - AMAZUL - Analista de Negócios |
FGV - 2026 - AMAZUL - Arquiteto |
FGV - 2026 - AMAZUL - Auditor |
FGV - 2026 - AMAZUL - Analista de Recursos Humanos |
FGV - 2026 - AMAZUL - Engenheiro Ambiental |
FGV - 2026 - AMAZUL - Engenheiro de Materiais |
FGV - 2026 - AMAZUL - Especialista de Radioproteção |
FGV - 2026 - AMAZUL - Engenheiro Nuclear |
FGV - 2026 - AMAZUL - Engenheiro de Produção |
FGV - 2026 - AMAZUL - Engenheiro Civil |
FGV - 2026 - AMAZUL - Engenheiro Controle e Automação |
FGV - 2026 - AMAZUL - Físico |
FGV - 2026 - AMAZUL - Engenheiro Químico |
FGV - 2026 - AMAZUL - Engenheiro de Telecomunicações |
FGV - 2026 - AMAZUL - Engenheiro de Segurança do Trabalho |
FGV - 2026 - AMAZUL - Engenheiro Eletricista |
FGV - 2026 - AMAZUL - Engenheiro Eletrônico |
FGV - 2026 - AMAZUL - Meteorologista |
FGV - 2026 - AMAZUL - Químico |
FGV - 2026 - AMAZUL - Tecnólogo em Fabricação Mecânica |
FGV - 2026 - AMAZUL - Engenheiro de Energia |
FGV - 2026 - AMAZUL - Engenheiro Mecânico |
Q3846039
Inglês
Texto associado
READ THE TEXT AND ANSWER THE FOLLOWING QUESTION
Social Dimensions of Climate Change
Extreme weather events are deeply intertwined with global
patterns of inequality. The poorest and most vulnerable people
bear the brunt of climate change impacts yet contribute the least
to the crisis. As the impacts of climate change mount, millions of
vulnerable people face disproportionate challenges in terms of
loss of jobs; physical harm; disease; mental health effects; food
insecurity; access to water; migration and forced displacement;
loss of shelter, assets, and community ties, and other related risks.
Some people are more vulnerable to climate change than others.
For example, workers in sectors such as agriculture, fishing, and
tourism rely on natural resources that are particularly sensitive to
increasingly unpredictable weather and seasonal patterns.
Female-headed households, children, persons with
disabilities, Indigenous Peoples and ethnic minorities, landless
tenants, migrant workers, displaced persons, older people, and
other socially marginalized groups often have fewer financial and
other resources to cope with and recover from shocks which might
threaten their wellbeing and the wellbeing of their families. The
root causes of their vulnerability lie in a combination of their
geographical locations; their financial, socio-economic, cultural,
and social status; and their access to resources, services, and
decision-making power.
The poor are often not just among the most vulnerable to climate
change, but also disproportionately impacted by measures to
address it. These impacts can include increased costs of living, loss
of livelihoods, and limited access to resources and support
systems, which exacerbate existing inequalities and poverty
trends. In the absence of well-designed and citizen-centered
policies, efforts to tackle climate change can have unintended
consequences for the livelihoods of certain groups, including
placing a higher financial burden on poor households […].
While much progress has been made on the science and the types
of policies needed to support a transition to low carbon, climateresilient development, a challenge facing many countries is
engaging citizens who are concerned that they will be unfairly
impacted by climate policies. Citizen-centered programs play a
vital role in ensuring that resources are used efficiently. Engaging
people in shaping climate action is equally critical for achieving
lasting impact. This means ensuring transparency, access to
information, and active citizen engagement on climate risks and
green growth. Such involvement can help build public support to
reduce climate impacts, overcome behavioral and political barriers
to decarbonization, as well as foster both new ideas and a sense of
ownership over solutions.
Moreover, communities bring unique perspectives, skills, and a
wealth of knowledge to the challenge of strengthening resilience
and addressing climate change. They should be engaged as
partners in resilience-building rather than being regarded merely
as beneficiaries. Research and experience show that community
leaders can successfully set priorities, influence ownership, as well
as design and implement investment programs that are responsive
to their community’s own needs. A 2022 report by the
Intergovernmental Panel on Climate Change (IPCC) recognizes the
value of diverse forms of knowledge — such as scientific,
Indigenous, and local knowledge — in building climate resilience.
Innovations in the architecture of climate finance can connect
communities and marginalized groups to the policy, technical, and
financial assistance that they need for locally relevant and
effective development outcomes.
From: https://www.worldbank.org/en/topic/social-dimensions-of-climate-change
The idiom in “bear the brunt of climate change impacts” (1st
paragraph) means to:
Ano: 2026
Banca:
FGV
Órgão:
AMAZUL
Provas:
FGV - 2026 - AMAZUL - Advogado
|
FGV - 2026 - AMAZUL - Contador |
FGV - 2026 - AMAZUL - Designer Gráfico |
FGV - 2026 - AMAZUL - Analista de Administração |
FGV - 2026 - AMAZUL - Engenheiro Mecatrônico |
FGV - 2026 - AMAZUL - Analista de Desenvolvimento de Sistemas |
FGV - 2026 - AMAZUL - Engenheiro Naval |
FGV - 2026 - AMAZUL - Engenheiro de Computação |
FGV - 2026 - AMAZUL - Médico do Trabalho |
FGV - 2026 - AMAZUL - Psicólogo |
FGV - 2026 - AMAZUL - Analista de Infraestrutura de Tecnologia da Informação |
FGV - 2026 - AMAZUL - Engenheiro de Controle da Qualidade |
FGV - 2026 - AMAZUL - Analista de Negócios |
FGV - 2026 - AMAZUL - Arquiteto |
FGV - 2026 - AMAZUL - Auditor |
FGV - 2026 - AMAZUL - Analista de Recursos Humanos |
FGV - 2026 - AMAZUL - Engenheiro Ambiental |
FGV - 2026 - AMAZUL - Engenheiro de Materiais |
FGV - 2026 - AMAZUL - Especialista de Radioproteção |
FGV - 2026 - AMAZUL - Engenheiro Nuclear |
FGV - 2026 - AMAZUL - Engenheiro de Produção |
FGV - 2026 - AMAZUL - Engenheiro Civil |
FGV - 2026 - AMAZUL - Engenheiro Controle e Automação |
FGV - 2026 - AMAZUL - Físico |
FGV - 2026 - AMAZUL - Engenheiro Químico |
FGV - 2026 - AMAZUL - Engenheiro de Telecomunicações |
FGV - 2026 - AMAZUL - Engenheiro de Segurança do Trabalho |
FGV - 2026 - AMAZUL - Engenheiro Eletricista |
FGV - 2026 - AMAZUL - Engenheiro Eletrônico |
FGV - 2026 - AMAZUL - Meteorologista |
FGV - 2026 - AMAZUL - Químico |
FGV - 2026 - AMAZUL - Tecnólogo em Fabricação Mecânica |
FGV - 2026 - AMAZUL - Engenheiro de Energia |
FGV - 2026 - AMAZUL - Engenheiro Mecânico |
Q3846038
Inglês
Texto associado
READ THE TEXT AND ANSWER THE FOLLOWING QUESTION
Social Dimensions of Climate Change
Extreme weather events are deeply intertwined with global
patterns of inequality. The poorest and most vulnerable people
bear the brunt of climate change impacts yet contribute the least
to the crisis. As the impacts of climate change mount, millions of
vulnerable people face disproportionate challenges in terms of
loss of jobs; physical harm; disease; mental health effects; food
insecurity; access to water; migration and forced displacement;
loss of shelter, assets, and community ties, and other related risks.
Some people are more vulnerable to climate change than others.
For example, workers in sectors such as agriculture, fishing, and
tourism rely on natural resources that are particularly sensitive to
increasingly unpredictable weather and seasonal patterns.
Female-headed households, children, persons with
disabilities, Indigenous Peoples and ethnic minorities, landless
tenants, migrant workers, displaced persons, older people, and
other socially marginalized groups often have fewer financial and
other resources to cope with and recover from shocks which might
threaten their wellbeing and the wellbeing of their families. The
root causes of their vulnerability lie in a combination of their
geographical locations; their financial, socio-economic, cultural,
and social status; and their access to resources, services, and
decision-making power.
The poor are often not just among the most vulnerable to climate
change, but also disproportionately impacted by measures to
address it. These impacts can include increased costs of living, loss
of livelihoods, and limited access to resources and support
systems, which exacerbate existing inequalities and poverty
trends. In the absence of well-designed and citizen-centered
policies, efforts to tackle climate change can have unintended
consequences for the livelihoods of certain groups, including
placing a higher financial burden on poor households […].
While much progress has been made on the science and the types
of policies needed to support a transition to low carbon, climateresilient development, a challenge facing many countries is
engaging citizens who are concerned that they will be unfairly
impacted by climate policies. Citizen-centered programs play a
vital role in ensuring that resources are used efficiently. Engaging
people in shaping climate action is equally critical for achieving
lasting impact. This means ensuring transparency, access to
information, and active citizen engagement on climate risks and
green growth. Such involvement can help build public support to
reduce climate impacts, overcome behavioral and political barriers
to decarbonization, as well as foster both new ideas and a sense of
ownership over solutions.
Moreover, communities bring unique perspectives, skills, and a
wealth of knowledge to the challenge of strengthening resilience
and addressing climate change. They should be engaged as
partners in resilience-building rather than being regarded merely
as beneficiaries. Research and experience show that community
leaders can successfully set priorities, influence ownership, as well
as design and implement investment programs that are responsive
to their community’s own needs. A 2022 report by the
Intergovernmental Panel on Climate Change (IPCC) recognizes the
value of diverse forms of knowledge — such as scientific,
Indigenous, and local knowledge — in building climate resilience.
Innovations in the architecture of climate finance can connect
communities and marginalized groups to the policy, technical, and
financial assistance that they need for locally relevant and
effective development outcomes.
From: https://www.worldbank.org/en/topic/social-dimensions-of-climate-change
Based on the text, mark the statements below as TRUE (T) or FALSE
(F).
( ) Harsh climate conditions exert a uniform impact across populations.
( ) Supporting citizen involvement is key to building commitment.
( ) At this stage, the challenges have been wholly addressed and handled.
The statements are, respectively:
( ) Harsh climate conditions exert a uniform impact across populations.
( ) Supporting citizen involvement is key to building commitment.
( ) At this stage, the challenges have been wholly addressed and handled.
The statements are, respectively:
Q4064613
Inglês
Texto associado
Text II
From: https://www.cartoonmovement.com/cartoon/facial-recognition-0
The speech “Don’t try to sneak a water bottle past security this
time” implies that the character in the cartoon
Q4064612
Inglês
Texto associado
Text II
From: https://www.cartoonmovement.com/cartoon/facial-recognition-0
The cartoon criticizes the fact that face recognition can be
Q4064611
Inglês
Texto associado
Text I
Understanding bias in facial recognition technologies
Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point. As developers have continued to swiftly expand the scope of these kinds of technologies into an almost unbounded range of applications, an increasingly strident chorus of critical voices has sounded concerns about the injurious effects of the proliferation of such systems on impacted individuals and communities. Critics argue that the irresponsible design and use of facial detection and recognition technologies (FDRTs) threaten to violate civil liberties, infringe on basic human rights and further entrench structural racism and systemic marginalisation. In addition, they argue that the gradual creep of face surveillance infrastructures into every domain of lived experience may eventually eradicate the modern democratic forms of life that have long provided cherished means to individual flourishing, social solidarity and human self-creation.
Defenders, by contrast, emphasise the gains in public safety, security and efficiency that digitally streamlined capacities for facial identification, identity verification and trait characterisation may bring. These proponents point to potential real-world benefits like the added security of facial recognition enhanced border control, the increased efficacy of missing children or criminal suspect searches that are driven by the application of brute force facial analysis to largescale databases and the many added conveniences of facial verification in the business of everyday life.
Whatever side of the debate on which one lands, it would appear that FDRTs are here to stay.
Adapted from: understanding_bias_in_facial_recognition_technology.pdf
The word “like” in “like the added security of facial recognition”
(2nd paragraph) introduces a(n)
Q4064610
Inglês
Texto associado
Text I
Understanding bias in facial recognition technologies
Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point. As developers have continued to swiftly expand the scope of these kinds of technologies into an almost unbounded range of applications, an increasingly strident chorus of critical voices has sounded concerns about the injurious effects of the proliferation of such systems on impacted individuals and communities. Critics argue that the irresponsible design and use of facial detection and recognition technologies (FDRTs) threaten to violate civil liberties, infringe on basic human rights and further entrench structural racism and systemic marginalisation. In addition, they argue that the gradual creep of face surveillance infrastructures into every domain of lived experience may eventually eradicate the modern democratic forms of life that have long provided cherished means to individual flourishing, social solidarity and human self-creation.
Defenders, by contrast, emphasise the gains in public safety, security and efficiency that digitally streamlined capacities for facial identification, identity verification and trait characterisation may bring. These proponents point to potential real-world benefits like the added security of facial recognition enhanced border control, the increased efficacy of missing children or criminal suspect searches that are driven by the application of brute force facial analysis to largescale databases and the many added conveniences of facial verification in the business of everyday life.
Whatever side of the debate on which one lands, it would appear that FDRTs are here to stay.
Adapted from: understanding_bias_in_facial_recognition_technology.pdf
In the first sentence, when the author says that the debate
“has reached a boiling point”, he means that the debate is
Q4064609
Inglês
Texto associado
Text I
Understanding bias in facial recognition technologies
Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point. As developers have continued to swiftly expand the scope of these kinds of technologies into an almost unbounded range of applications, an increasingly strident chorus of critical voices has sounded concerns about the injurious effects of the proliferation of such systems on impacted individuals and communities. Critics argue that the irresponsible design and use of facial detection and recognition technologies (FDRTs) threaten to violate civil liberties, infringe on basic human rights and further entrench structural racism and systemic marginalisation. In addition, they argue that the gradual creep of face surveillance infrastructures into every domain of lived experience may eventually eradicate the modern democratic forms of life that have long provided cherished means to individual flourishing, social solidarity and human self-creation.
Defenders, by contrast, emphasise the gains in public safety, security and efficiency that digitally streamlined capacities for facial identification, identity verification and trait characterisation may bring. These proponents point to potential real-world benefits like the added security of facial recognition enhanced border control, the increased efficacy of missing children or criminal suspect searches that are driven by the application of brute force facial analysis to largescale databases and the many added conveniences of facial verification in the business of everyday life.
Whatever side of the debate on which one lands, it would appear that FDRTs are here to stay.
Adapted from: understanding_bias_in_facial_recognition_technology.pdf
In the last sentence, the author states that facial detection and
recognition technologies
Q4064608
Inglês
Texto associado
Text I
Understanding bias in facial recognition technologies
Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point. As developers have continued to swiftly expand the scope of these kinds of technologies into an almost unbounded range of applications, an increasingly strident chorus of critical voices has sounded concerns about the injurious effects of the proliferation of such systems on impacted individuals and communities. Critics argue that the irresponsible design and use of facial detection and recognition technologies (FDRTs) threaten to violate civil liberties, infringe on basic human rights and further entrench structural racism and systemic marginalisation. In addition, they argue that the gradual creep of face surveillance infrastructures into every domain of lived experience may eventually eradicate the modern democratic forms of life that have long provided cherished means to individual flourishing, social solidarity and human self-creation.
Defenders, by contrast, emphasise the gains in public safety, security and efficiency that digitally streamlined capacities for facial identification, identity verification and trait characterisation may bring. These proponents point to potential real-world benefits like the added security of facial recognition enhanced border control, the increased efficacy of missing children or criminal suspect searches that are driven by the application of brute force facial analysis to largescale databases and the many added conveniences of facial verification in the business of everyday life.
Whatever side of the debate on which one lands, it would appear that FDRTs are here to stay.
Adapted from: understanding_bias_in_facial_recognition_technology.pdf
Based on Text I, analyze the assertions below:
I. Critics are concerned about the pervasiveness of facial recognition technology.
II. Facial recognition systems may reduce the efficiency and security of border control.
III. Facial recognition systems may reduce the efficiency and security of border control.
Choose the correct answer:
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,
Ano: 2025
Banca:
FGV
Órgão:
CPRM
Provas:
FGV - 2025 - CPRM - Pesquisador em Geociências - Engenharia Ambiental
|
FGV - 2025 - CPRM - Pesquisador em Geociências - Geologia - Levantamento Geológico e de Recursos Minerais |
FGV - 2025 - CPRM - Pesquisador em Geociências - Geologia - Ordenamento Territorial - Área de Risco |
FGV - 2025 - CPRM - Pesquisador em Geociências - Geologia - Sedimentologia, Estratigrafia e Análise tectônica de Bacias Sedimentares |
FGV - 2025 - CPRM - Pesquisador em Geociências - Hidrologia |
FGV - 2025 - CPRM - Pesquisador em Geociências - Paleontologia |
FGV - 2025 - CPRM - Pesquisador em Geociências - Hidrogeologia |
Q3758068
Inglês
Texto associado
Jadarite, described as ‘Earth's kryptonite twin,’ has potential to
replace fossil fuels
A plain-white mineral found in western Serbia has a name straight
out of the comics and a chemical profile that battery makers crave.
Called jadarite, also known as sodium-lithium- boron silicate
hydroxide, was first pulled from drill cores in 2004 and officially
recognized as a new mineral two years later.
Geologists soon noticed that the formula on the sample label
matched the faux “kryptonite” shown in a 2006 Superman film,
minus the fluorine and the green glow. That pop-culture twist
helped the discovery grab headlines, yet the real excitement lies in
what the mineral could do for electric vehicles and renewable
power storage.
Jadarite occurs as dull, chalky nodules tucked inside fine-grained
shale in the Jadar Valley. The host rocks formed in an ancient lake
basin rich in volcanic ash, allowing lithium and boron to build up in
the pore waters until the mineral crystallized. Those conditions
have been found only in Serbia so far, making the deposit both
unique and strategically valuable.
Michael Page, a process chemist at Australia’s Nuclear Science and
Technology Organisation (ANSTO), points out that the valley “is
considered one of the largest lithium deposits in the world, making
it a potential game-changer for the global green energy
transition.” […]
Serbian communities are not unanimous in welcoming the mine.
Environmental groups warn that alkali-rich tailings could leak into
the Jadar River and harm local agriculture. Independent studies
have found elevated boron and lithium downstream of exploratory
boreholes, fueling weekly protests in Belgrade.
Supporters counter that rigorous water-management plans and
sealed tailings cells can limit impacts, and that the economic gains,
including thousands of skilled jobs, are hard to ignore. European
automakers also see the project as a chance to shorten supply
chains now dominated by South American brines and Chinese
refiners.
Whether or not the Jadar project reaches full production, the
mineral has already altered the critical-minerals map. Its existence
proves that lithium can concentrate outside traditional pegmatites
and brines, broadening the hunt to basins once dismissed as
uneconomic clay.
Researchers are now experimenting with synthetic pathways,
seeding gels of silica, borate, and lithium under lake-like conditions
to see if jadarite can be grown on demand. Success could pave the
way for engineered deposits that bypass mining altogether. For
now, though, nature’s one known batch in western Serbia remains
the focus of intense scientific, industrial, and public scrutiny.
Adapted from https://www.earth.com/news/jadarite-described-as-earthskryptonite-twin-has-potential-to-replace-fossil-fuels/
Based on the last paragraph, analyse the assertions below:
I. Scientific experimentation might do away with the need for mining.
II. Currently, interest in jadarite deposits seems to be waning.
III. It is highly unlikely that the deposits found in Serbia will reshape the future of energy.
Choose the correct answer.
I. Scientific experimentation might do away with the need for mining.
II. Currently, interest in jadarite deposits seems to be waning.
III. It is highly unlikely that the deposits found in Serbia will reshape the future of energy.
Choose the correct answer.
Ano: 2025
Banca:
FGV
Órgão:
CPRM
Provas:
FGV - 2025 - CPRM - Pesquisador em Geociências - Engenharia Ambiental
|
FGV - 2025 - CPRM - Pesquisador em Geociências - Geologia - Levantamento Geológico e de Recursos Minerais |
FGV - 2025 - CPRM - Pesquisador em Geociências - Geologia - Ordenamento Territorial - Área de Risco |
FGV - 2025 - CPRM - Pesquisador em Geociências - Geologia - Sedimentologia, Estratigrafia e Análise tectônica de Bacias Sedimentares |
FGV - 2025 - CPRM - Pesquisador em Geociências - Hidrologia |
FGV - 2025 - CPRM - Pesquisador em Geociências - Paleontologia |
FGV - 2025 - CPRM - Pesquisador em Geociências - Hidrogeologia |
Q3758067
Inglês
Texto associado
Jadarite, described as ‘Earth's kryptonite twin,’ has potential to
replace fossil fuels
A plain-white mineral found in western Serbia has a name straight
out of the comics and a chemical profile that battery makers crave.
Called jadarite, also known as sodium-lithium- boron silicate
hydroxide, was first pulled from drill cores in 2004 and officially
recognized as a new mineral two years later.
Geologists soon noticed that the formula on the sample label
matched the faux “kryptonite” shown in a 2006 Superman film,
minus the fluorine and the green glow. That pop-culture twist
helped the discovery grab headlines, yet the real excitement lies in
what the mineral could do for electric vehicles and renewable
power storage.
Jadarite occurs as dull, chalky nodules tucked inside fine-grained
shale in the Jadar Valley. The host rocks formed in an ancient lake
basin rich in volcanic ash, allowing lithium and boron to build up in
the pore waters until the mineral crystallized. Those conditions
have been found only in Serbia so far, making the deposit both
unique and strategically valuable.
Michael Page, a process chemist at Australia’s Nuclear Science and
Technology Organisation (ANSTO), points out that the valley “is
considered one of the largest lithium deposits in the world, making
it a potential game-changer for the global green energy
transition.” […]
Serbian communities are not unanimous in welcoming the mine.
Environmental groups warn that alkali-rich tailings could leak into
the Jadar River and harm local agriculture. Independent studies
have found elevated boron and lithium downstream of exploratory
boreholes, fueling weekly protests in Belgrade.
Supporters counter that rigorous water-management plans and
sealed tailings cells can limit impacts, and that the economic gains,
including thousands of skilled jobs, are hard to ignore. European
automakers also see the project as a chance to shorten supply
chains now dominated by South American brines and Chinese
refiners.
Whether or not the Jadar project reaches full production, the
mineral has already altered the critical-minerals map. Its existence
proves that lithium can concentrate outside traditional pegmatites
and brines, broadening the hunt to basins once dismissed as
uneconomic clay.
Researchers are now experimenting with synthetic pathways,
seeding gels of silica, borate, and lithium under lake-like conditions
to see if jadarite can be grown on demand. Success could pave the
way for engineered deposits that bypass mining altogether. For
now, though, nature’s one known batch in western Serbia remains
the focus of intense scientific, industrial, and public scrutiny.
Adapted from https://www.earth.com/news/jadarite-described-as-earthskryptonite-twin-has-potential-to-replace-fossil-fuels/
In the fourth paragraph, the process chemist’s opinion about the
Jadar Valley is that it is
Ano: 2025
Banca:
FGV
Órgão:
CPRM
Provas:
FGV - 2025 - CPRM - Pesquisador em Geociências - Engenharia Ambiental
|
FGV - 2025 - CPRM - Pesquisador em Geociências - Geologia - Levantamento Geológico e de Recursos Minerais |
FGV - 2025 - CPRM - Pesquisador em Geociências - Geologia - Ordenamento Territorial - Área de Risco |
FGV - 2025 - CPRM - Pesquisador em Geociências - Geologia - Sedimentologia, Estratigrafia e Análise tectônica de Bacias Sedimentares |
FGV - 2025 - CPRM - Pesquisador em Geociências - Hidrologia |
FGV - 2025 - CPRM - Pesquisador em Geociências - Paleontologia |
FGV - 2025 - CPRM - Pesquisador em Geociências - Hidrogeologia |
Q3758066
Inglês
Texto associado
Jadarite, described as ‘Earth's kryptonite twin,’ has potential to
replace fossil fuels
A plain-white mineral found in western Serbia has a name straight
out of the comics and a chemical profile that battery makers crave.
Called jadarite, also known as sodium-lithium- boron silicate
hydroxide, was first pulled from drill cores in 2004 and officially
recognized as a new mineral two years later.
Geologists soon noticed that the formula on the sample label
matched the faux “kryptonite” shown in a 2006 Superman film,
minus the fluorine and the green glow. That pop-culture twist
helped the discovery grab headlines, yet the real excitement lies in
what the mineral could do for electric vehicles and renewable
power storage.
Jadarite occurs as dull, chalky nodules tucked inside fine-grained
shale in the Jadar Valley. The host rocks formed in an ancient lake
basin rich in volcanic ash, allowing lithium and boron to build up in
the pore waters until the mineral crystallized. Those conditions
have been found only in Serbia so far, making the deposit both
unique and strategically valuable.
Michael Page, a process chemist at Australia’s Nuclear Science and
Technology Organisation (ANSTO), points out that the valley “is
considered one of the largest lithium deposits in the world, making
it a potential game-changer for the global green energy
transition.” […]
Serbian communities are not unanimous in welcoming the mine.
Environmental groups warn that alkali-rich tailings could leak into
the Jadar River and harm local agriculture. Independent studies
have found elevated boron and lithium downstream of exploratory
boreholes, fueling weekly protests in Belgrade.
Supporters counter that rigorous water-management plans and
sealed tailings cells can limit impacts, and that the economic gains,
including thousands of skilled jobs, are hard to ignore. European
automakers also see the project as a chance to shorten supply
chains now dominated by South American brines and Chinese
refiners.
Whether or not the Jadar project reaches full production, the
mineral has already altered the critical-minerals map. Its existence
proves that lithium can concentrate outside traditional pegmatites
and brines, broadening the hunt to basins once dismissed as
uneconomic clay.
Researchers are now experimenting with synthetic pathways,
seeding gels of silica, borate, and lithium under lake-like conditions
to see if jadarite can be grown on demand. Success could pave the
way for engineered deposits that bypass mining altogether. For
now, though, nature’s one known batch in western Serbia remains
the focus of intense scientific, industrial, and public scrutiny.
Adapted from https://www.earth.com/news/jadarite-described-as-earthskryptonite-twin-has-potential-to-replace-fossil-fuels/
In the fragment “a chemical profile that battery makers crave” (1st
paragraph), the verb is close in meaning to
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