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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:
Instituto Fênix
Órgão:
Prefeitura de São Domingos - SC
Prova:
Instituto Fênix - 2025 - Prefeitura de São Domingos - SC - Professor de Língua Estrangeira - Inglês |
Q3778307
Inglês
Texto associado
Are Some Sugars ‘Less Bad’ Than Others?
Q: I’m trying to limit sugar, but I love sweets. Are “natural” sweeteners like honey and agave syrup healthier alternatives to table sugar?
You probably know that the sugars in fruits, vegetables and other plants are far better for you than the added sugars often found in processed foods like sodas, candy bars and many baked goods.
But in that category of added sugars, there’s an array of sweeteners that are often seen as more “natural” or healthier than others. Honey, maple syrup and agave nectar, for instance, are commonly touted as “better for you” swaps for regular sugar, such as in many health-focused baking recipes and on social media.
Is that right? We asked three nutrition experts to help us sort it out.
Source:
https://www.nytimes.com/2025/10/28/well/eat/health-
effects-honey-maple-syrup-agave.html
Which option correctly rewrites the sentence “We asked
three nutrition experts to help us sort it out” in the present
perfect?
Ano: 2025
Banca:
Instituto Fênix
Órgão:
Prefeitura de São Domingos - SC
Prova:
Instituto Fênix - 2025 - Prefeitura de São Domingos - SC - Professor de Língua Estrangeira - Inglês |
Q3778306
Inglês
Texto associado
Are Some Sugars ‘Less Bad’ Than Others?
Q: I’m trying to limit sugar, but I love sweets. Are “natural” sweeteners like honey and agave syrup healthier alternatives to table sugar?
You probably know that the sugars in fruits, vegetables and other plants are far better for you than the added sugars often found in processed foods like sodas, candy bars and many baked goods.
But in that category of added sugars, there’s an array of sweeteners that are often seen as more “natural” or healthier than others. Honey, maple syrup and agave nectar, for instance, are commonly touted as “better for you” swaps for regular sugar, such as in many health-focused baking recipes and on social media.
Is that right? We asked three nutrition experts to help us sort it out.
Source:
https://www.nytimes.com/2025/10/28/well/eat/health-
effects-honey-maple-syrup-agave.html
In the question “Is that right?” the word that refers to:
Ano: 2025
Banca:
Instituto Fênix
Órgão:
Prefeitura de São Domingos - SC
Prova:
Instituto Fênix - 2025 - Prefeitura de São Domingos - SC - Professor de Língua Estrangeira - Inglês |
Q3778305
Inglês
Texto associado
Are Some Sugars ‘Less Bad’ Than Others?
Q: I’m trying to limit sugar, but I love sweets. Are “natural” sweeteners like honey and agave syrup healthier alternatives to table sugar?
You probably know that the sugars in fruits, vegetables and other plants are far better for you than the added sugars often found in processed foods like sodas, candy bars and many baked goods.
But in that category of added sugars, there’s an array of sweeteners that are often seen as more “natural” or healthier than others. Honey, maple syrup and agave nectar, for instance, are commonly touted as “better for you” swaps for regular sugar, such as in many health-focused baking recipes and on social media.
Is that right? We asked three nutrition experts to help us sort it out.
Source:
https://www.nytimes.com/2025/10/28/well/eat/health-
effects-honey-maple-syrup-agave.html
In the sentence “I’m trying to limit sugar, but I love sweets,”
the word limit is closest in meaning to:
Ano: 2025
Banca:
Instituto Fênix
Órgão:
Prefeitura de São Domingos - SC
Prova:
Instituto Fênix - 2025 - Prefeitura de São Domingos - SC - Professor de Língua Estrangeira - Inglês |
Q3778304
Inglês
Texto associado
Are Some Sugars ‘Less Bad’ Than Others?
Q: I’m trying to limit sugar, but I love sweets. Are “natural” sweeteners like honey and agave syrup healthier alternatives to table sugar?
You probably know that the sugars in fruits, vegetables and other plants are far better for you than the added sugars often found in processed foods like sodas, candy bars and many baked goods.
But in that category of added sugars, there’s an array of sweeteners that are often seen as more “natural” or healthier than others. Honey, maple syrup and agave nectar, for instance, are commonly touted as “better for you” swaps for regular sugar, such as in many health-focused baking recipes and on social media.
Is that right? We asked three nutrition experts to help us sort it out.
Source:
https://www.nytimes.com/2025/10/28/well/eat/health-
effects-honey-maple-syrup-agave.html
Segundo o texto, alguns adoçantes como mel e xarope de
agave são frequentemente divulgados em redes sociais como
substitutos “melhores” do que o açúcar comum. Assinale a
alternativa incorreta sobre a forma como o texto apresenta
essa percepção.
Ano: 2025
Banca:
Instituto Fênix
Órgão:
Prefeitura de São Domingos - SC
Prova:
Instituto Fênix - 2025 - Prefeitura de São Domingos - SC - Professor de Língua Estrangeira - Inglês |
Q3778303
Inglês
Texto associado
Are Some Sugars ‘Less Bad’ Than Others?
Q: I’m trying to limit sugar, but I love sweets. Are “natural” sweeteners like honey and agave syrup healthier alternatives to table sugar?
You probably know that the sugars in fruits, vegetables and other plants are far better for you than the added sugars often found in processed foods like sodas, candy bars and many baked goods.
But in that category of added sugars, there’s an array of sweeteners that are often seen as more “natural” or healthier than others. Honey, maple syrup and agave nectar, for instance, are commonly touted as “better for you” swaps for regular sugar, such as in many health-focused baking recipes and on social media.
Is that right? We asked three nutrition experts to help us sort it out.
Source:
https://www.nytimes.com/2025/10/28/well/eat/health-
effects-honey-maple-syrup-agave.html
No trecho inicial, o autor afirma que os açúcares presentes
em frutas e vegetais são “far better for you” do que os açúcares
adicionados. Essa informação permite concluir que:
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774235
Inglês
Reading comprehension involves understanding both
explicit information and implicit meanings within a text.
Select the alternative that correctly identifies the main
idea of a text stating: "While many believe technology
isolates people, studies show it can actually enhance
connectivity across long distances."
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774234
Inglês
In the context of Communicative Language Teaching
(CLT), the integration of the four language skills
(listening, speaking, reading, and writing) is fundamental for developing communicative competence. Select the
alternative that correctly describes an integrated skills
activity.
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774233
Inglês
Conditional sentences describe a condition and its result.
The First Conditional is used for real or possible
situations in the future. Select the correct structure for the
First Conditional.
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774231
Inglês
Pronouns are used to replace nouns, and their form
depends on their function in the sentence. In the sentence "Sarah gave the book to Peter," identify the
correct pronouns to replace "Sarah" and "Peter".
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774230
Inglês
Phrasal verbs consist of a verb plus a particle
(preposition or adverb) that creates a new meaning. Mark
T, (True), or F, (False) for the definitions of the phrasal
verbs in bold.
(__)"To look for" means to try to find something or someone (search).
(__)"To give up" means to stop doing something or to surrender.
(__)"To get up" means to leave a bus or a train.
(__)"To turn on" means to start a machine or light by pressing a switch.
Select the alternative that presents the correct sequence, from top to bottom.
(__)"To look for" means to try to find something or someone (search).
(__)"To give up" means to stop doing something or to surrender.
(__)"To get up" means to leave a bus or a train.
(__)"To turn on" means to start a machine or light by pressing a switch.
Select the alternative that presents the correct sequence, from top to bottom.
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774229
Inglês
The Noun Phrase (NP) is a syntactic unit built around a
noun, which functions as the head, and may include
determiners and modifiers. Analyze the statements below
regarding the structure of the noun phrase in the
sentence: "The three expensive cars."
I."Cars" is the head noun of the phrase, determining the number and gender of the phrase.
II."The" acts as a determiner (definite article), identifying the specific group of cars being referred to.
III."Three" and "expensive" function as pre-modifiers (numeral and adjective) that describe the head noun.
Choose the alternative that indicates the correct statement(s).
I."Cars" is the head noun of the phrase, determining the number and gender of the phrase.
II."The" acts as a determiner (definite article), identifying the specific group of cars being referred to.
III."Three" and "expensive" function as pre-modifiers (numeral and adjective) that describe the head noun.
Choose the alternative that indicates the correct statement(s).
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774228
Inglês
Verb tenses and moods allow speakers to locate actions
in time and express attitudes. Analyze the statements
about the use of verb tenses in English.
I.The Present Perfect tense describes an action that happened at an unspecified time in the past or has a connection to the present.
II.The Simple Past tense is used for actions that were completed at a definite time in the past.
III.The Present Continuous tense is used to describe permanent states and facts that are always true.
Choose the alternative that indicates the correct statement(s).
I.The Present Perfect tense describes an action that happened at an unspecified time in the past or has a connection to the present.
II.The Simple Past tense is used for actions that were completed at a definite time in the past.
III.The Present Continuous tense is used to describe permanent states and facts that are always true.
Choose the alternative that indicates the correct statement(s).
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774227
Inglês
Connectives (or linking words) are crucial for joining
clauses and expressing relationships between ideas. In
the sentence "She studied hard for the exam;
nevertheless, she didn't get a good grade," the
connective "nevertheless" expresses:
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774226
Inglês
The Passive Voice is used to shift the focus from the
doer of the action to the action itself or the object.
Choose the alternative that correctly transforms the
active sentence "The chef prepared a delicious meal" into
the passive voice.
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774225
Inglês
Prepositions of place describe the position of an object
relative to another. Select the alternative that correctly
completes the sentence: "The picture is hanging _____
the wall."
Ano: 2025
Banca:
AMAUC
Órgão:
Prefeitura de Itá - SC
Prova:
AMAUC - 2025 - Prefeitura de Itá - SC - Professor de Inglês |
Q3774224
Inglês
Discourse markers are words or phrases used to
organize speech and writing, signaling the speaker's
attitude or the relationship between ideas. Identify the
function of the discourse marker "actually" in the
sentence: "People think he is British, but actually, he is
Australian."
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