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Q3780425
Direito Constitucional Processo Legislativo ,
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 |
Q3780425 Direito Constitucional
O Chefe do Poder Executivo do Estado Sigma encaminhou projeto de lei à Assembleia Legislativa no qual criava novos cargos em comissão de simbologia XX, já existente na referida estrutura, e aumentava sua remuneração em 10% (dez por cento).
No âmbito do Poder Legislativo, foram votadas e aprovadas três emendas parlamentares. A primeira emenda instituiu uma gratificação de produtividade para os ocupantes dos referidos cargos em comissão. A segunda, aumentou a remuneração em apenas 9% (nove por cento). Por fim, a terceira criou um órgão de controle interno, no âmbito do Poder Executivo, para aferir a produtividade dos ocupantes de cargos em comissão. Ao fim do processo legislativo, foi editada a Lei nº Y, cuja constitucionalidade foi contestada por um partido político de oposição ao governo.
Em relação às emendas aprovadas
Alternativas
Q3780423
Direito Constitucional Organização dos Poderes ,
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 |
Q3780423 Direito Constitucional
Entre os servidores públicos do Estado Sigma, iniciou-se um movimento com o objetivo de construir um referencial de isonomia remuneratória, de modo a estimular a atuação funcional e a aumentar a produtividade.
Sensível aos objetivos do movimento, um grupo de Deputados Estaduais apresentou proposição legislativa para uniformizar o teto remuneratório constitucional no âmbito das estruturas estatais de poder, ressalvadas apenas as exceções da Constituição da República.
Sobre a uniformização pretendida, à luz da sistemática constitucional vigente, assinale a afirmativa correta. 
Alternativas
Q3780422
Direito Constitucional Organização dos Poderes ,
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 |
Q3780422 Direito Constitucional
Em matéria de controle interno, no contexto da fiscalização contábil, financeira e orçamentária, de acordo com a Constituição Federal, assinale a afirmativa correta.
Alternativas
Q3780421
Direito Constitucional Constituições Estaduais , Teoria da Constituição ,
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 |
Q3780421 Direito Constitucional
A Constituição do Estado Alfa foi alterada e o seu Art. X passou a dispor que, no âmbito da Assembleia Legislativa, será criada uma comissão permanente de articulação e controle interinstitucional, que será responsável por manter diálogo permanente com as estruturas estatais de poder de nível estadual, aglutinar as informações decorrentes do controle realizado pelo Poder Legislativo, e contribuir para o aperfeiçoamento de funções e serviços públicos.
A partir da interpretação do Art. X, é obtida norma de eficácia
Alternativas
Q3780420
Direito Constitucional Organização Político-Administrativa do Estado ,
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 |
Q3780420 Direito Constitucional
O Estado Alfa editou a Lei Complementar nº X, dispondo sobre determinados aspectos afetos ao controle da execução orçamentária, que ainda não tinham sido disciplinados em norma infraconstitucional.
Os órgãos de controle interno vinham aplicando regularmente a Lei Complementar nº X, quando sobreveio a Lei Complementar Federal nº Y, editada pela União, que tinha conteúdo diverso.
Após amplos debates a respeito da lei a ser observada, os órgãos de controle interno concluíram corretamente que
Alternativas
Q3780418
Administração Pública Governabilidade, Governança e Accountability ,
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 |
Q3780418 Administração Pública
A obrigação dos agentes e das organizações que gerenciam recursos públicos de assumir integralmente as responsabilidades por suas decisões e pela prestação de contas de sua atuação de forma voluntária, inclusive sobre as consequências de seus atos e omissões, é denominada
Alternativas
Q3780412
Ética na Administração Pública Código de Ética Profissional do Servidor Público Civil do Poder Executivo Federal - Decreto nº 1.171 de 1994 , Introdução, Ética e Moral e Orientações Gerais ,
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 |
Q3780412 Ética na Administração Pública
Os requisitos éticos representam valores aceitáveis e esperados em relação à conduta dos auditores internos governamentais. Considerando as disposições do referencial técnico da atividade de auditoria interna governamental, relacione os requisitos éticos listados a seguir aos seus respectivos comportamentos.
I. Autonomia Técnica. II. Integridade e comportamento . III. Objetividade.
( ) Os auditores internos governamentais devem ser capazes de lidar de forma adequada com pressões ou situações que possam resultar em ganhos pessoais ou organizacionais inadequados.
( ) Caso o Responsável pela UAIG tenha atribuições de gestão externas à atividade de auditoria interna, o trabalho de avaliação sobre os processos pelos quais foi responsável deve ser supervisionado por uma unidade externa à auditoria interna.
( ) Os auditores devem declarar impedimento nas situações que possam afetar o desempenho das suas atribuições.

Assinale a opção que apresenta a relação correta, na ordem apresentada.
Alternativas
Q3780409
Ética na Administração Pública Introdução, Ética e Moral e Orientações Gerais ,
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 |
Q3780409 Ética na Administração Pública
Os auditores internos governamentais devem se comportar com cortesia e respeito no trato com pessoas, mesmo em situações de divergência de opinião, abstendo-se de emitir juízo ou adotar práticas que indiquem qualquer tipo de discriminação ou preconceito.
Assinale a opção que indica o princípio fundamental para a prática da atividade de Auditoria Interna Governamental que exige esse comportamento dos auditores internos.
Alternativas
Q3780407
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: FGV Órgão: CGE-SP Provas: FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Auditoria | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Tecnologia da Informação | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Correição e Combate à Corrupção | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Obras e Concessões | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Contabilidade Pública e Finanças |
Q3780407 Inglês
Read the following text and answer the questions.


Artificial Intelligence: The “lethal trifecta”

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

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

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

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

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

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

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

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

Adapted from The Economist, September 27th, 2025, p. 10
The text concludes that the Victorian engineers’ decision 
Alternativas
Q3780406
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: FGV Órgão: CGE-SP Provas: FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Auditoria | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Tecnologia da Informação | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Correição e Combate à Corrupção | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Obras e Concessões | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Contabilidade Pública e Finanças |
Q3780406 Inglês
Read the following text and answer the questions.


Artificial Intelligence: The “lethal trifecta”

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

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

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

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

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

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

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

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

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


Artificial Intelligence: The “lethal trifecta”

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

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

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

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

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

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

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

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

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


Artificial Intelligence: The “lethal trifecta”

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

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

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

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

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

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

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

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

Adapted from The Economist, September 27th, 2025, p. 10
The phrase shoddy work costs lives (3rd paragraph) refers to work that is 
Alternativas
Q3780403
Inglês Vocabulário | Vocabulary , Sinônimos | Synonyms ,
Ano: 2025 Banca: FGV Órgão: CGE-SP Provas: FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Auditoria | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Tecnologia da Informação | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Correição e Combate à Corrupção | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Obras e Concessões | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Contabilidade Pública e Finanças |
Q3780403 Inglês
Read the following text and answer the questions.


Artificial Intelligence: The “lethal trifecta”

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

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

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

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

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

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

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

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

Adapted from The Economist, September 27th, 2025, p. 10
The word tricked (1st paragraph) means that LLMs can be
Alternativas
Q3780402
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2025 Banca: FGV Órgão: CGE-SP Provas: FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Auditoria | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Tecnologia da Informação | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Correição e Combate à Corrupção | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Obras e Concessões | FGV - 2025 - CGE-SP - Auditor Estadual de Controle - Contabilidade Pública e Finanças |
Q3780402 Inglês
Read the following text and answer the questions.


Artificial Intelligence: The “lethal trifecta”

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

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

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

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

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

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

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

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

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


Artificial Intelligence: The “lethal trifecta”

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

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

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

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

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

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

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

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

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


Artificial Intelligence: The “lethal trifecta”

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

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

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

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

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

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

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

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

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

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


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

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

III. A deterministic model does not deal with randomness.


The statements are, respectively,

Alternativas
Q3780399
Português Interpretação de Textos ,
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 |
Q3780399 Português
Assinale a opção que apresenta o texto que deve ser incluído entre os narrativos.
Alternativas
Q3780397
Português Sintaxe ,
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 |
Q3780397 Português
As opções a seguir mostram orações adjetivas sublinhadas. Assinale a opção que apresenta a substituição adequada de uma oração por um adjetivo.
Alternativas
Q3780396
Português Interpretação de Textos ,
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 |
Q3780396 Português
Nas opções a seguir há termos destacados que são referidos de forma diferente na continuidade do texto.
Assinale a opção em que essa referência é feita por um termo geral.
Alternativas
Q3780390
Português Interpretação de Textos ,
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 |
Q3780390 Português
Leia o texto a seguir.
Os pés do homem se afundaram na areia, deixando uma marca informe, como se fosse a pegada de algum animal. Treparam sobre as pedras, usando as unhas ao sentirem a inclinação da subida, logo caminharam para cima, buscando o horizonte.
‘Pés chatos’ – disse o que o seguia. E um dedo de menos. Falta-lhe o dedo central no pé esquerdo. Não abundam indivíduos com essa marca. Assim será fácil.
A estranheza presente nesse texto, que é o início de um romance, decorre
Alternativas
Respostas
381: C
382: A
383: D
384: B
385: C
386: B
387: B
388: C
389: B
390: A
391: C
392: E
393: D
394: E
395: A
396: C
397: E
398: E
399: A
400: B
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