Questões de Concurso Público SEFAZ-CE 2021 para Auditor Fiscal de Tecnologia da Informação da Receita Estadual

Foram encontradas 157 questões

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Q1831268
Arquitetura de Software MVC (Model-view-controller) ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831268 Arquitetura de Software

A respeito de sistemas distribuídos, julgue o item a seguir. 

O modelo de arquitetura modelo-visão-controlador (MVC) é responsável por encapsular as funcionalidades e os objetos de conteúdo.  

Alternativas
Q1831269
Arquitetura de Computadores Sistemas Distribuídos ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831269 Arquitetura de Computadores

A respeito de sistemas distribuídos, julgue o item a seguir. 

O atributo confiabilidade da tolerância a falhas refere-se ao fator de disponibilidade do sistema em determinado período de tempo.  

Alternativas
Q1831270
Arquitetura de Computadores Sistemas Distribuídos ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831270 Arquitetura de Computadores

A respeito de sistemas distribuídos, julgue o item a seguir. 

Middleware é um sistema que conecta outros recursos, abstraindo protocolos de comunicação e camadas de infraestrutura. 

Alternativas
Q1831271
Arquitetura de Computadores Sistemas Distribuídos ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831271 Arquitetura de Computadores

A respeito de sistemas distribuídos, julgue o item a seguir. 

A ePING é uma estrutura básica de interoperabilidade entre órgãos do governo federal, restrita e obrigatória ao Poder Executivo. 

Alternativas
Q1831272
Arquitetura de Computadores Sistemas Distribuídos ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831272 Arquitetura de Computadores

A respeito de sistemas distribuídos, julgue o item a seguir. 

A política de segurança da ePING exige que informações classificadas e sensíveis transitem em redes inseguras com a devida criptografia, o que impede o acesso por pessoa não autorizada.

Alternativas
Q1831273
Arquitetura de Computadores Sistemas Distribuídos ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831273 Arquitetura de Computadores

A respeito de sistemas distribuídos, julgue o item a seguir. 

Sistemas com funcionalidade de tuning são capazes de otimizar automaticamente suas próprias características internas de funcionamento, sem influência externa. 

Alternativas
Q1831274
Redes de Computadores Transmissão de Dados ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831274 Redes de Computadores

Acerca de técnicas de comunicação de dados e de comutação, julgue o item a seguir. 

Um esquema de correção de erros embasado em verificações de redundância vertical e longitudinal com o uso de bits de paridade é eficiente e recomendado para ambientes de transmissão muito ruidosos. 

Alternativas
Q1831275
Redes de Computadores Transmissão de Dados ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831275 Redes de Computadores

Acerca de técnicas de comunicação de dados e de comutação, julgue o item a seguir. 

Na comutação de células, um enlace de canal virtual (VCL) é formado pela concatenação de conexões virtuais estabelecidas nos vários enlaces da rede, da origem até o destino, formando um caminho único por meio do qual as células serão encaminhadas. 

Alternativas
Q1831276
Redes de Computadores Topologias ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831276 Redes de Computadores
A respeito de topologias, arquiteturas e protocolos de redes de comunicação, julgue o item que se segue.  Em uma rede em topologia de barramento, as comunicações de mensagens do tipo difusão são facilitadas porque todos os nós conectados ao barramento são capazes de ouvir todas as transmissões realizadas. 
Alternativas
Q1831277
Arquitetura de Software Arquitetura em camadas ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831277 Arquitetura de Software
A respeito de topologias, arquiteturas e protocolos de redes de comunicação, julgue o item que se segue.  Em uma arquitetura em camadas, um protocolo é um conjunto de operações em alto nível de abstração funcional que uma camada oferece à camada situada acima dela e funciona como uma interface entre duas camadas subjacentes. 
Alternativas
Q1831278
Redes de Computadores Redes sem Fio ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831278 Redes de Computadores

Acerca de tecnologias de redes locais e de redes sem fio, julgue o item subsequente. 

Incluída no padrão Gigabit Ethernet, a rajada de quadros propõe o aumento da eficiência das transmissões à medida que permite a um transmissor enviar uma sequência concatenada de vários quadros em uma única transmissão.  

Alternativas
Q1831279
Redes de Computadores Redes sem Fio ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831279 Redes de Computadores

Acerca de tecnologias de redes locais e de redes sem fio, julgue o item subsequente. 

No contexto de qualidade de serviço do padrão 802.11, a técnica EDCA (enhanced distributed channel access) empregada pela subcamada MAC prevê classificação e tratamento prioritário ao tráfego de pacotes de dados sensíveis a atraso como voz e vídeo. 

Alternativas
Q1831280
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831280 Inglês
Researchers can turn a single photo into a video
   Sometimes photos cannot truly capture a scene. How much more epic would that vacation photo of Niagara Falls be if the water were moving? Researchers at the University of Washington have developed a deep learning method that can do just that: if given a single photo of a waterfall, the system creates a video showing that water cascading down. All that’s missing is the roar of the water and the feeling of the spray on your face.
   This method can animate any flowing material, including smoke and clouds. This technique produces a short video that loops seamlessly, giving the impression of endless movement.
   An expert says that a picture captures a moment frozen in time, but a lot of information is lost in a static image. That makes people wonder what led to that moment, and how things are changing. If people think about the last time that they found themselves fixated on something really interesting, chances are, it was’t totally static. 
   What is special about that method is that it doesn’t require any user input or extra information. All that is needed is a picture. And it produces as output a high-resolution, seamlessly looping video that quite often looks like a real video. Developing a method that turns a single photo into a believable video has been a challenge for the field.
   The system consists of two parts: first, it predicts how things were moving when the photo was taken, and then uses that information to create the animation. Then the system uses that information to determine if and how each pixel should move. Finally, the researchers want their animation to loop seamlessly to create a look of continuous movement. The animation network follows a few tricks to keep things clean, including transitioning different parts of the frame at different times and deciding how quickly or slowly to blend each pixel depending on its surroundings. 
   This method works best for objects with predictable fluid motion, like water, fire or smoke. These types of motions obey the same set of physical laws, and there are usually cues in the image that tell us how things should be moving. Currently, the technology struggles to predict how reflections should move or how water distorts the appearance of objects beneath it. In the future, the researchers would like to extend their work to operate on a wider range of objects, like animating a person’s hair blowing in the wind. 
Internet: <www.sciencedaily.com> (adapted).

Based on the text above, judge the item below.  

One example of what this method can do to the photo is add the sound of the water in a waterfall.

Alternativas
Q1831281
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831281 Inglês
Researchers can turn a single photo into a video
   Sometimes photos cannot truly capture a scene. How much more epic would that vacation photo of Niagara Falls be if the water were moving? Researchers at the University of Washington have developed a deep learning method that can do just that: if given a single photo of a waterfall, the system creates a video showing that water cascading down. All that’s missing is the roar of the water and the feeling of the spray on your face.
   This method can animate any flowing material, including smoke and clouds. This technique produces a short video that loops seamlessly, giving the impression of endless movement.
   An expert says that a picture captures a moment frozen in time, but a lot of information is lost in a static image. That makes people wonder what led to that moment, and how things are changing. If people think about the last time that they found themselves fixated on something really interesting, chances are, it was’t totally static. 
   What is special about that method is that it doesn’t require any user input or extra information. All that is needed is a picture. And it produces as output a high-resolution, seamlessly looping video that quite often looks like a real video. Developing a method that turns a single photo into a believable video has been a challenge for the field.
   The system consists of two parts: first, it predicts how things were moving when the photo was taken, and then uses that information to create the animation. Then the system uses that information to determine if and how each pixel should move. Finally, the researchers want their animation to loop seamlessly to create a look of continuous movement. The animation network follows a few tricks to keep things clean, including transitioning different parts of the frame at different times and deciding how quickly or slowly to blend each pixel depending on its surroundings. 
   This method works best for objects with predictable fluid motion, like water, fire or smoke. These types of motions obey the same set of physical laws, and there are usually cues in the image that tell us how things should be moving. Currently, the technology struggles to predict how reflections should move or how water distorts the appearance of objects beneath it. In the future, the researchers would like to extend their work to operate on a wider range of objects, like animating a person’s hair blowing in the wind. 
Internet: <www.sciencedaily.com> (adapted).

Based on the text above, judge the item below.  

When people are fixated on something for a while, there might be a chance of that thing being in movement. 

Alternativas
Q1831282
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831282 Inglês
Researchers can turn a single photo into a video
   Sometimes photos cannot truly capture a scene. How much more epic would that vacation photo of Niagara Falls be if the water were moving? Researchers at the University of Washington have developed a deep learning method that can do just that: if given a single photo of a waterfall, the system creates a video showing that water cascading down. All that’s missing is the roar of the water and the feeling of the spray on your face.
   This method can animate any flowing material, including smoke and clouds. This technique produces a short video that loops seamlessly, giving the impression of endless movement.
   An expert says that a picture captures a moment frozen in time, but a lot of information is lost in a static image. That makes people wonder what led to that moment, and how things are changing. If people think about the last time that they found themselves fixated on something really interesting, chances are, it was’t totally static. 
   What is special about that method is that it doesn’t require any user input or extra information. All that is needed is a picture. And it produces as output a high-resolution, seamlessly looping video that quite often looks like a real video. Developing a method that turns a single photo into a believable video has been a challenge for the field.
   The system consists of two parts: first, it predicts how things were moving when the photo was taken, and then uses that information to create the animation. Then the system uses that information to determine if and how each pixel should move. Finally, the researchers want their animation to loop seamlessly to create a look of continuous movement. The animation network follows a few tricks to keep things clean, including transitioning different parts of the frame at different times and deciding how quickly or slowly to blend each pixel depending on its surroundings. 
   This method works best for objects with predictable fluid motion, like water, fire or smoke. These types of motions obey the same set of physical laws, and there are usually cues in the image that tell us how things should be moving. Currently, the technology struggles to predict how reflections should move or how water distorts the appearance of objects beneath it. In the future, the researchers would like to extend their work to operate on a wider range of objects, like animating a person’s hair blowing in the wind. 
Internet: <www.sciencedaily.com> (adapted).

Based on the text above, judge the item below.  

One of the drawbacks of this method is the amount of user input and information it requires. 

Alternativas
Q1831283
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831283 Inglês
Researchers can turn a single photo into a video
   Sometimes photos cannot truly capture a scene. How much more epic would that vacation photo of Niagara Falls be if the water were moving? Researchers at the University of Washington have developed a deep learning method that can do just that: if given a single photo of a waterfall, the system creates a video showing that water cascading down. All that’s missing is the roar of the water and the feeling of the spray on your face.
   This method can animate any flowing material, including smoke and clouds. This technique produces a short video that loops seamlessly, giving the impression of endless movement.
   An expert says that a picture captures a moment frozen in time, but a lot of information is lost in a static image. That makes people wonder what led to that moment, and how things are changing. If people think about the last time that they found themselves fixated on something really interesting, chances are, it was’t totally static. 
   What is special about that method is that it doesn’t require any user input or extra information. All that is needed is a picture. And it produces as output a high-resolution, seamlessly looping video that quite often looks like a real video. Developing a method that turns a single photo into a believable video has been a challenge for the field.
   The system consists of two parts: first, it predicts how things were moving when the photo was taken, and then uses that information to create the animation. Then the system uses that information to determine if and how each pixel should move. Finally, the researchers want their animation to loop seamlessly to create a look of continuous movement. The animation network follows a few tricks to keep things clean, including transitioning different parts of the frame at different times and deciding how quickly or slowly to blend each pixel depending on its surroundings. 
   This method works best for objects with predictable fluid motion, like water, fire or smoke. These types of motions obey the same set of physical laws, and there are usually cues in the image that tell us how things should be moving. Currently, the technology struggles to predict how reflections should move or how water distorts the appearance of objects beneath it. In the future, the researchers would like to extend their work to operate on a wider range of objects, like animating a person’s hair blowing in the wind. 
Internet: <www.sciencedaily.com> (adapted).

Based on the text above, judge the item below.  

It was not so easy to develop such a method to give motion to a single picture. 

Alternativas
Q1831284
Inglês Interpretação de texto | Reading comprehension ,
Ano: 2021 Banca: CESPE / CEBRASPE Órgão: SEFAZ-CE Prova: CESPE / CEBRASPE - 2021 - SEFAZ-CE - Auditor Fiscal de Tecnologia da Informação da Receita Estadual |
Q1831284 Inglês
Researchers can turn a single photo into a video
   Sometimes photos cannot truly capture a scene. How much more epic would that vacation photo of Niagara Falls be if the water were moving? Researchers at the University of Washington have developed a deep learning method that can do just that: if given a single photo of a waterfall, the system creates a video showing that water cascading down. All that’s missing is the roar of the water and the feeling of the spray on your face.
   This method can animate any flowing material, including smoke and clouds. This technique produces a short video that loops seamlessly, giving the impression of endless movement.
   An expert says that a picture captures a moment frozen in time, but a lot of information is lost in a static image. That makes people wonder what led to that moment, and how things are changing. If people think about the last time that they found themselves fixated on something really interesting, chances are, it was’t totally static. 
   What is special about that method is that it doesn’t require any user input or extra information. All that is needed is a picture. And it produces as output a high-resolution, seamlessly looping video that quite often looks like a real video. Developing a method that turns a single photo into a believable video has been a challenge for the field.
   The system consists of two parts: first, it predicts how things were moving when the photo was taken, and then uses that information to create the animation. Then the system uses that information to determine if and how each pixel should move. Finally, the researchers want their animation to loop seamlessly to create a look of continuous movement. The animation network follows a few tricks to keep things clean, including transitioning different parts of the frame at different times and deciding how quickly or slowly to blend each pixel depending on its surroundings. 
   This method works best for objects with predictable fluid motion, like water, fire or smoke. These types of motions obey the same set of physical laws, and there are usually cues in the image that tell us how things should be moving. Currently, the technology struggles to predict how reflections should move or how water distorts the appearance of objects beneath it. In the future, the researchers would like to extend their work to operate on a wider range of objects, like animating a person’s hair blowing in the wind. 
Internet: <www.sciencedaily.com> (adapted).

Based on the text above, judge the item below.  

It can be inferred from the text that, in the future, the researchers would like this method to animate a photo of a woman on a motorbike without wearing a helmet, for example. 

Alternativas
Respostas
120: C
121: E
122: C
123: E
124: C
125: E
126: E
127: E
128: C
129: E
130: C
131: C
132: E
133: C
134: E
135: C
136: C
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