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Concurso: USP

Concurso: USP
Cargo: Técnico de Laboratório (Especialidade: Mecânica)
Ano: 2026

Ano:

2026

Banca:

FUVEST

Cargo:

Técnico de Laboratório (Especialidade: Mecânica)

Disciplina: Inglês

FC933728

Dificuldade em análise

notificar erro

Notificar erro

Motivo:

Há um erro no enunciado e/ou alternativas

A resposta no gabarito oficial é outra

A disciplina ou assunto estão incorretos

A questão foi anulada

A questão está desatualizada

Informe mais detalhes

Erro de digitação ou português

As alternativas não aparecem

As imagens não aparecem

As imagens estão ilegíveis

Outro (especifique)

Alternativa A

Alternativa B

Alternativa C

Alternativa D

Alternativa E

Descrição

ENVIAR

Acertou

Errou

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Building Trustworthy AI in Government: Enablers, Guardrails, and Engagement 

    Governments are starting to use AI in areas like public services, tax work, and disaster response. When it works well, AI can help people get answers faster, spot problems earlier, and support better decisions. As a result, AI can improve productivity, responsiveness, and accountability in government.

    However, many public AI projects stay in small pilots. This happens because governments often lack skills, good data, modern digital systems, and clear ways to measure impact. These gaps can also increase risk aversion, so teams avoid innovation even when the potential benefits are high.

    The OECD proposes a simple way to understand “trustworthy AI in government”: a framework with three connected pillars. In the figure, the goal is in the centre. Around it, the three pillars explain what governments must build and do, so they can reach the public value goals shown on the outer ring (productivity, responsiveness and accountability).

     Enablers are the foundations. They include strong governance, quality data, and digital infrastructure, as well as skills and talent in the civil service. They also require purposeful investment, smart public procurement, and partnerships with non-government actors, so that AI systems can be built and used reliably.

    Guardrails are the safety systems that guide AI use. They include ethics and risk management, transparency duties, and monitoring and oversight bodies that can check results over time. They can also be non-binding guidance or binding laws and policies, along with enforcement measures. Tools like impact assessment and auditing help keep these guardrails practical. Still, guardrails should be proportionate: not every rule fits every use case, or progress may stop.

    Engagement means involving the people who are affected. This includes working across levels of government, across policy areas, and with the broader ecosystem (civil society, businesses and researchers). It also includes citizens and civil servants, and sometimes collaboration across borders. Engagement pushes governments to design user-centred systems, listen to concerns, and make necessary adjustments.

     The main message is that trust is “unlocked” by the right mix. If enablers are weak, AI cannot scale. If guardrails are missing, harms grow. If engagement is shallow, solutions may look efficient but feel unfair, and trust can fall.

(Adapted from oecd.org on February 22, 2026)

Considere o trecho “Guardrails are the safety systems that guide AI use.” (5º parágrafo). Sem alterar o sentido original do texto, a palavra “guide” pode ser substituída por

(a)

restrict.

(b)

prohibit.

(c)

direct.

(d)

ignore.

(e)

eliminate.

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Concurso: USP

Concurso: USP
Cargo: Técnico de Laboratório (Especialidade: Mecânica)
Ano: 2026

Ano:

2026

Banca:

FUVEST

Cargo:

Técnico de Laboratório (Especialidade: Mecânica)

Disciplina: Inglês

FC933727

Dificuldade em análise

notificar erro

Notificar erro

Motivo:

Há um erro no enunciado e/ou alternativas

A resposta no gabarito oficial é outra

A disciplina ou assunto estão incorretos

A questão foi anulada

A questão está desatualizada

Informe mais detalhes

Erro de digitação ou português

As alternativas não aparecem

As imagens não aparecem

As imagens estão ilegíveis

Outro (especifique)

Alternativa A

Alternativa B

Alternativa C

Alternativa D

Alternativa E

Descrição

ENVIAR

Acertou

Errou

adicionar questão na lista de favoritas

remover questão da lista de favoritas

mostrar texto associado esconder texto associado

Building Trustworthy AI in Government: Enablers, Guardrails, and Engagement 

    Governments are starting to use AI in areas like public services, tax work, and disaster response. When it works well, AI can help people get answers faster, spot problems earlier, and support better decisions. As a result, AI can improve productivity, responsiveness, and accountability in government.

    However, many public AI projects stay in small pilots. This happens because governments often lack skills, good data, modern digital systems, and clear ways to measure impact. These gaps can also increase risk aversion, so teams avoid innovation even when the potential benefits are high.

    The OECD proposes a simple way to understand “trustworthy AI in government”: a framework with three connected pillars. In the figure, the goal is in the centre. Around it, the three pillars explain what governments must build and do, so they can reach the public value goals shown on the outer ring (productivity, responsiveness and accountability).

     Enablers are the foundations. They include strong governance, quality data, and digital infrastructure, as well as skills and talent in the civil service. They also require purposeful investment, smart public procurement, and partnerships with non-government actors, so that AI systems can be built and used reliably.

    Guardrails are the safety systems that guide AI use. They include ethics and risk management, transparency duties, and monitoring and oversight bodies that can check results over time. They can also be non-binding guidance or binding laws and policies, along with enforcement measures. Tools like impact assessment and auditing help keep these guardrails practical. Still, guardrails should be proportionate: not every rule fits every use case, or progress may stop.

    Engagement means involving the people who are affected. This includes working across levels of government, across policy areas, and with the broader ecosystem (civil society, businesses and researchers). It also includes citizens and civil servants, and sometimes collaboration across borders. Engagement pushes governments to design user-centred systems, listen to concerns, and make necessary adjustments.

     The main message is that trust is “unlocked” by the right mix. If enablers are weak, AI cannot scale. If guardrails are missing, harms grow. If engagement is shallow, solutions may look efficient but feel unfair, and trust can fall.

(Adapted from oecd.org on February 22, 2026)

Considere o trecho “These gaps can also increase risk aversion, so teams avoid innovation even when the potential benefits are high.” (2º parágrafo)
A expressão “risk aversion” pode ser corretamente compreendida como:¬

(a)

Disposição para assumir desafios financeiros.

(b)

Tendência para evitar situações arriscadas.

(c)

 Prática de calcular riscos com precisão.

(d)

 Incentivo institucional à experimentação.

(e)

 Redução de custos administrativos.

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Concurso: USP

Concurso: USP
Cargo: Técnico de Laboratório (Especialidade: Mecânica)
Ano: 2026

Ano:

2026

Banca:

FUVEST

Cargo:

Técnico de Laboratório (Especialidade: Mecânica)

Disciplina: Inglês

FC933726

Dificuldade em análise

notificar erro

Notificar erro

Motivo:

Há um erro no enunciado e/ou alternativas

A resposta no gabarito oficial é outra

A disciplina ou assunto estão incorretos

A questão foi anulada

A questão está desatualizada

Informe mais detalhes

Erro de digitação ou português

As alternativas não aparecem

As imagens não aparecem

As imagens estão ilegíveis

Outro (especifique)

Alternativa A

Alternativa B

Alternativa C

Alternativa D

Alternativa E

Descrição

ENVIAR

Acertou

Errou

adicionar questão na lista de favoritas

remover questão da lista de favoritas

mostrar texto associado esconder texto associado

Building Trustworthy AI in Government: Enablers, Guardrails, and Engagement 

    Governments are starting to use AI in areas like public services, tax work, and disaster response. When it works well, AI can help people get answers faster, spot problems earlier, and support better decisions. As a result, AI can improve productivity, responsiveness, and accountability in government.

    However, many public AI projects stay in small pilots. This happens because governments often lack skills, good data, modern digital systems, and clear ways to measure impact. These gaps can also increase risk aversion, so teams avoid innovation even when the potential benefits are high.

    The OECD proposes a simple way to understand “trustworthy AI in government”: a framework with three connected pillars. In the figure, the goal is in the centre. Around it, the three pillars explain what governments must build and do, so they can reach the public value goals shown on the outer ring (productivity, responsiveness and accountability).

     Enablers are the foundations. They include strong governance, quality data, and digital infrastructure, as well as skills and talent in the civil service. They also require purposeful investment, smart public procurement, and partnerships with non-government actors, so that AI systems can be built and used reliably.

    Guardrails are the safety systems that guide AI use. They include ethics and risk management, transparency duties, and monitoring and oversight bodies that can check results over time. They can also be non-binding guidance or binding laws and policies, along with enforcement measures. Tools like impact assessment and auditing help keep these guardrails practical. Still, guardrails should be proportionate: not every rule fits every use case, or progress may stop.

    Engagement means involving the people who are affected. This includes working across levels of government, across policy areas, and with the broader ecosystem (civil society, businesses and researchers). It also includes citizens and civil servants, and sometimes collaboration across borders. Engagement pushes governments to design user-centred systems, listen to concerns, and make necessary adjustments.

     The main message is that trust is “unlocked” by the right mix. If enablers are weak, AI cannot scale. If guardrails are missing, harms grow. If engagement is shallow, solutions may look efficient but feel unfair, and trust can fall.

(Adapted from oecd.org on February 22, 2026)

No 5º parágrafo, ao afirmar que “Still, guardrails should be proportionate: not every rule fits every use case, or progress may stop. ”, o texto defende que as regras para o uso da IA devem

(a)

ser iguais para todos os sistemas, pois isso aumenta a clareza e acelera a adoção.

(b)

evitar monitoramento contínuo, já que ele aumenta a aversão ao risco.

(c)

focar apenas em auditorias, pois leis e políticas costumam atrasar projetos.

(d)

priorizar parcerias e compras públicas, substituindo a necessidade de regras.

(e)

variar conforme o caso, equilibrando proteção e avanço, para não impedir a inovação. #

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Concurso: USP

Concurso: USP
Cargo: Técnico de Laboratório (Especialidade: Mecânica)
Ano: 2026

Ano:

2026

Banca:

FUVEST

Cargo:

Técnico de Laboratório (Especialidade: Mecânica)

Disciplina: Inglês

FC933725

Dificuldade em análise

notificar erro

Notificar erro

Motivo:

Há um erro no enunciado e/ou alternativas

A resposta no gabarito oficial é outra

A disciplina ou assunto estão incorretos

A questão foi anulada

A questão está desatualizada

Informe mais detalhes

Erro de digitação ou português

As alternativas não aparecem

As imagens não aparecem

As imagens estão ilegíveis

Outro (especifique)

Alternativa A

Alternativa B

Alternativa C

Alternativa D

Alternativa E

Descrição

ENVIAR

Acertou

Errou

adicionar questão na lista de favoritas

remover questão da lista de favoritas

mostrar texto associado esconder texto associado

Building Trustworthy AI in Government: Enablers, Guardrails, and Engagement 

    Governments are starting to use AI in areas like public services, tax work, and disaster response. When it works well, AI can help people get answers faster, spot problems earlier, and support better decisions. As a result, AI can improve productivity, responsiveness, and accountability in government.

    However, many public AI projects stay in small pilots. This happens because governments often lack skills, good data, modern digital systems, and clear ways to measure impact. These gaps can also increase risk aversion, so teams avoid innovation even when the potential benefits are high.

    The OECD proposes a simple way to understand “trustworthy AI in government”: a framework with three connected pillars. In the figure, the goal is in the centre. Around it, the three pillars explain what governments must build and do, so they can reach the public value goals shown on the outer ring (productivity, responsiveness and accountability).

     Enablers are the foundations. They include strong governance, quality data, and digital infrastructure, as well as skills and talent in the civil service. They also require purposeful investment, smart public procurement, and partnerships with non-government actors, so that AI systems can be built and used reliably.

    Guardrails are the safety systems that guide AI use. They include ethics and risk management, transparency duties, and monitoring and oversight bodies that can check results over time. They can also be non-binding guidance or binding laws and policies, along with enforcement measures. Tools like impact assessment and auditing help keep these guardrails practical. Still, guardrails should be proportionate: not every rule fits every use case, or progress may stop.

    Engagement means involving the people who are affected. This includes working across levels of government, across policy areas, and with the broader ecosystem (civil society, businesses and researchers). It also includes citizens and civil servants, and sometimes collaboration across borders. Engagement pushes governments to design user-centred systems, listen to concerns, and make necessary adjustments.

     The main message is that trust is “unlocked” by the right mix. If enablers are weak, AI cannot scale. If guardrails are missing, harms grow. If engagement is shallow, solutions may look efficient but feel unfair, and trust can fall.

(Adapted from oecd.org on February 22, 2026)

No 5º parágrafo do texto, a palavra “guardrails” é usada em sentido figurado. Ela se refere, mais diretamente, a: 

(a)

Barreiras físicas que impedem o acesso de cidadãos a sistemas digitais.

(b)

Mecanismos e regras que orientam e limitam o uso da IA para reduzir riscos e manter o controle.

(c)

Ferramentas de produtividade que aceleram decisões sem necessidade de supervisão.

(d)

 Investimentos em infraestrutura para tornar a IA mais rápida e barata.

(e)

Campanhas de engajamento para convencer o público de que a IA é sempre confiável.

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Concurso: USP

Concurso: USP
Cargo: Técnico de Laboratório (Especialidade: Mecânica)
Ano: 2026

Ano:

2026

Banca:

FUVEST

Cargo:

Técnico de Laboratório (Especialidade: Mecânica)

Disciplina: Inglês

FC933724

Dificuldade em análise

notificar erro

Notificar erro

Motivo:

Há um erro no enunciado e/ou alternativas

A resposta no gabarito oficial é outra

A disciplina ou assunto estão incorretos

A questão foi anulada

A questão está desatualizada

Informe mais detalhes

Erro de digitação ou português

As alternativas não aparecem

As imagens não aparecem

As imagens estão ilegíveis

Outro (especifique)

Alternativa A

Alternativa B

Alternativa C

Alternativa D

Alternativa E

Descrição

ENVIAR

Acertou

Errou

adicionar questão na lista de favoritas

remover questão da lista de favoritas

mostrar texto associado esconder texto associado

Building Trustworthy AI in Government: Enablers, Guardrails, and Engagement 

    Governments are starting to use AI in areas like public services, tax work, and disaster response. When it works well, AI can help people get answers faster, spot problems earlier, and support better decisions. As a result, AI can improve productivity, responsiveness, and accountability in government.

    However, many public AI projects stay in small pilots. This happens because governments often lack skills, good data, modern digital systems, and clear ways to measure impact. These gaps can also increase risk aversion, so teams avoid innovation even when the potential benefits are high.

    The OECD proposes a simple way to understand “trustworthy AI in government”: a framework with three connected pillars. In the figure, the goal is in the centre. Around it, the three pillars explain what governments must build and do, so they can reach the public value goals shown on the outer ring (productivity, responsiveness and accountability).

     Enablers are the foundations. They include strong governance, quality data, and digital infrastructure, as well as skills and talent in the civil service. They also require purposeful investment, smart public procurement, and partnerships with non-government actors, so that AI systems can be built and used reliably.

    Guardrails are the safety systems that guide AI use. They include ethics and risk management, transparency duties, and monitoring and oversight bodies that can check results over time. They can also be non-binding guidance or binding laws and policies, along with enforcement measures. Tools like impact assessment and auditing help keep these guardrails practical. Still, guardrails should be proportionate: not every rule fits every use case, or progress may stop.

    Engagement means involving the people who are affected. This includes working across levels of government, across policy areas, and with the broader ecosystem (civil society, businesses and researchers). It also includes citizens and civil servants, and sometimes collaboration across borders. Engagement pushes governments to design user-centred systems, listen to concerns, and make necessary adjustments.

     The main message is that trust is “unlocked” by the right mix. If enablers are weak, AI cannot scale. If guardrails are missing, harms grow. If engagement is shallow, solutions may look efficient but feel unfair, and trust can fall.

(Adapted from oecd.org on February 22, 2026)

No trecho “These gaps can also increase risk aversion”, presente no segundo parágrafo, a expressão “these gaps” refere-se, principalmente, 

(a)

 a falta de habilidades, dados de qualidade, sistemas digitais modernos e formas claras de medir resultados.

(b)

a ausência de leis e políticas obrigatórias sobre o uso de IA no setor público.

(c)

a pouca colaboração entre países para o uso de IA em serviços públicos.

(d)

o excesso de parcerias com instituições não governamentais na criação de sistemas.

(e)

a baixa produtividade e a falta de eficiência como metas externas do governo.¬¬

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