Técnico Judiciário - Tecnologia da Informação - 2022
No que se refere à política de governança e gestão da Plataforma Digital do Poder Judiciário Brasileiro (PDPJ-Br), instituída pela Resolução CNJ n.º 335/2020, assinale a opção correta.
De acordo com a Portaria CNJ n.º 252/2020, prestar auxílio técnico aos tribunais, quando necessário, e supervisionar o desenvolvimento e sustentação do sistema, visando a garantir aderência entre as funcionalidades desenvolvidas e os requisitos definidos são atribuições
Segundo a Portaria CNJ n.º 131/2021, o Grupo Revisor de Código-Fonte é responsável pela análise das mudanças no código-fonte que forem sugeridas pela comunidade de desenvolvimento nas soluções disponibilizadas na PDPJ-Br e também no sistema PJe, e seus membros desempenharão as atividades em caráter honorífico. Com relação à composição desse grupo revisor, assinale a opção correta.
Na biblioteca ITIL, o nível de serviço que busca transformar o gerenciamento de serviços em ativos estratégicos para atender aos objetivos da empresa é conhecido como
Gerenciamento de catálogo de serviços, gerenciamento de fornecedor e gerenciamento de capacidade são processos do nível de serviço da ITIL denominado
De acordo com o COBIT 5, o domínio alinhar, planejar e organizar (APO) inclui os processos
Conforme a Resolução CNJ n.º 370/2021, as aquisições de bens e a contratação de serviços de tecnologia da informação e comunicação deverão atender às determinações do
Text 20A12-I
As technology advances, the car industry has developed new ways to improve user experience. One of these ways includes using artificial intelligence to make cars self-driving. A self-driving car (also known as an autonomous car or driverless car) is a vehicle that uses a different number of sensors, radars, cameras, and artificial intelligence to travel to destinations without needing a human driver. Many companies have already started to manufacture self-driving cars, which are put through many tests to ensure they are eligible to be on the road without making any errors. To qualify as fully autonomous, a car must navigate routes to predetermined destinations without any human intervention.
Artificial intelligence powers self-driving vehicle frameworks. Self-driving vehicle engineers utilize a great deal of information from image recognition systems, AI and neural networks to assemble frameworks that can drive self-sufficiently. The neural networks distinguish patterns in the data, which is fed to the AI calculations. That data include images from cameras for self-driving vehicles. The neural networks figure out how to recognize traffic lights, trees, pedestrians, road signs, and different parts of any random driving environment.
As an example, Google has started to develop self-driving cars, which use a mix of sensors, light detectors, and other technology, like GPS and cameras. All the input data are combined and the artificial system predicts what those objects might do next. This whole process happens in a matter of milliseconds. Similar to any human driver, the more experience these systems gain, the better they become at driving. The more data it deals with in its deep learning algorithms, the more choices it will make and the faster those choices will be.
Internet: <www.eescorporation.com> (adapted).
According to text 20A12-I,
Text 20A12-I
As technology advances, the car industry has developed new ways to improve user experience. One of these ways includes using artificial intelligence to make cars self-driving. A self-driving car (also known as an autonomous car or driverless car) is a vehicle that uses a different number of sensors, radars, cameras, and artificial intelligence to travel to destinations without needing a human driver. Many companies have already started to manufacture self-driving cars, which are put through many tests to ensure they are eligible to be on the road without making any errors. To qualify as fully autonomous, a car must navigate routes to predetermined destinations without any human intervention.
Artificial intelligence powers self-driving vehicle frameworks. Self-driving vehicle engineers utilize a great deal of information from image recognition systems, AI and neural networks to assemble frameworks that can drive self-sufficiently. The neural networks distinguish patterns in the data, which is fed to the AI calculations. That data include images from cameras for self-driving vehicles. The neural networks figure out how to recognize traffic lights, trees, pedestrians, road signs, and different parts of any random driving environment.
As an example, Google has started to develop self-driving cars, which use a mix of sensors, light detectors, and other technology, like GPS and cameras. All the input data are combined and the artificial system predicts what those objects might do next. This whole process happens in a matter of milliseconds. Similar to any human driver, the more experience these systems gain, the better they become at driving. The more data it deals with in its deep learning algorithms, the more choices it will make and the faster those choices will be.
Internet: <www.eescorporation.com> (adapted).
The main purpose of the second paragraph of text 20A12-I is to explain
Text 20A12-I
As technology advances, the car industry has developed new ways to improve user experience. One of these ways includes using artificial intelligence to make cars self-driving. A self-driving car (also known as an autonomous car or driverless car) is a vehicle that uses a different number of sensors, radars, cameras, and artificial intelligence to travel to destinations without needing a human driver. Many companies have already started to manufacture self-driving cars, which are put through many tests to ensure they are eligible to be on the road without making any errors. To qualify as fully autonomous, a car must navigate routes to predetermined destinations without any human intervention.
Artificial intelligence powers self-driving vehicle frameworks. Self-driving vehicle engineers utilize a great deal of information from image recognition systems, AI and neural networks to assemble frameworks that can drive self-sufficiently. The neural networks distinguish patterns in the data, which is fed to the AI calculations. That data include images from cameras for self-driving vehicles. The neural networks figure out how to recognize traffic lights, trees, pedestrians, road signs, and different parts of any random driving environment.
As an example, Google has started to develop self-driving cars, which use a mix of sensors, light detectors, and other technology, like GPS and cameras. All the input data are combined and the artificial system predicts what those objects might do next. This whole process happens in a matter of milliseconds. Similar to any human driver, the more experience these systems gain, the better they become at driving. The more data it deals with in its deep learning algorithms, the more choices it will make and the faster those choices will be.
Internet: <www.eescorporation.com> (adapted).
From the excerpt “The more data it deals with in its deep learning algorithms, the more choices it will make and the faster those choices will be” (last paragraph of text 20A12-I), it can be concluded that