Questões de Concurso Comentadas sobre inglês para técnico judiciário - tecnologia da informação
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Ano: 2022
Banca:
CESPE / CEBRASPE
Órgão:
TRT - 8ª Região (PA e AP)
Prova:
CESPE / CEBRASPE - 2022 - TRT - 8ª Região (PA e AP) - Técnico Judiciário - Tecnologia da Informação |
Q1990188
Inglês
Texto associado
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
Ano: 2022
Banca:
CESPE / CEBRASPE
Órgão:
TRT - 8ª Região (PA e AP)
Prova:
CESPE / CEBRASPE - 2022 - TRT - 8ª Região (PA e AP) - Técnico Judiciário - Tecnologia da Informação |
Q1990187
Inglês
Texto associado
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
Ano: 2022
Banca:
CESPE / CEBRASPE
Órgão:
TRT - 8ª Região (PA e AP)
Prova:
CESPE / CEBRASPE - 2022 - TRT - 8ª Região (PA e AP) - Técnico Judiciário - Tecnologia da Informação |
Q1990186
Inglês
Texto associado
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,
Q1201894
Inglês
Does an Email Hacking Software really Exist?
With my experience of over 10 years in the field of ethical hacking and information security, all I can tell you is that there exists no such ready-made software program (as shown and advertised on many websites) that can break into the service provider’s database and hack email passwords. This is just a myth! This may seem a bit disappointing for many, but this is the fact. However, it is still possible to easily hack email passwords using some of the alternative programs and ways as discussed below: Working Ways to Hack an Email Password: Even though it is impossible to hack the database and instantly crack the email password, it is still possible to trick the users so that they give away the password by themselves. This can be done using a handful of methods like keylogging, social engineering or phishing. However, the easiest and most effective way is by using keyloggers. A keylogger is a small program that records each and every keystroke a user types on the keyboard of a specific computer. So when you install a keylogger on the computer from where the target person is likely to access his/her email, it is possible to capture the password. Though keyloggers are not designed to hack email passwords, they can still be used to accomplish the job. Here is a list of some of the interesting facts about keyloggers: EASY TO USE: A keylogger does not require any special skills. Anyone with basic computer knowledge should be able to use it. REMAINS UNDETECTED: A keylogger will remain undetected after installation and operates in a total stealth mode. So, you need not worry about being caught or traced back. REMOTE INSTALLATION: In addition to installation on a location computer, keyloggers also support remote installation. That means you can also install it even on those computers for which you do not have physical access.
(Adapted form: http://www.gohacking.com/email-hacking-software/)
A melhor tradução para o trecho from where the target person is likely to access his/her email é
A melhor tradução para o trecho from where the target person is likely to access his/her email é
Q1188043
Inglês
Repeaters and hubs
A repeater is an electronic device that receives a network signal, cleans it of unnecessary noise, and regenerates it. The signal is retransmitted at a higher power level, or to the other side of an obstruction, so that the signal can cover longer distances without degradation. In most twisted pair Ethernet configurations, repeaters are required for cable that runs longer than 100 meters. A repeater with multiple ports is known as a hub. Repeaters work on the physical layer of the OSI model. Repeaters require a small amount of time to regenerate the signal. This can cause a propagation delay which can affect network performance. As a result, many network architectures limit the number of repeaters that can be used in a row, e.g., the Ethernet 5-4-3 rule. Hubs have been mostly obsoleted by modern switches; but repeaters are used for long distance links, notably undersea cabling.
Bridges
A network bridge connects multiple network segments at the data link layer (layer 2) of the OSI model to form a single network. Bridges broadcast to all ports except the port on which the broadcast was received. However, bridges do not promiscuously copy traffic to all ports, as hubs do. Instead, bridges learn which MAC addresses are reachable through specific ports. Once the bridge associates a port with an address, it will send traffic for that address to that port only. Bridges learn the association of ports and addresses by examining the source address of frames that it sees on various ports. Once a frame arrives through a port, the bridge assumes that the MAC address is associated with that port and stores its source address. The first time a bridge sees a previously unknown destination address, the bridge will forward the frame to all ports other than the one on which the frame arrived. Bridges come in three basic types: Local bridges: Directly connect LANs Remote bridges: Can be used to create a wide area network (WAN) link between LANs. Remote bridges, where the connecting link is slower than the end networks, largely have been replaced with routers. Wireless bridges: Can be used to join LANs or connect remote devices to LANs.
According to the text above, judge the following item.
Multiple network segments at the layer 2 of the OSI model can be connected by a network bridge, in order to form a single network.
A repeater is an electronic device that receives a network signal, cleans it of unnecessary noise, and regenerates it. The signal is retransmitted at a higher power level, or to the other side of an obstruction, so that the signal can cover longer distances without degradation. In most twisted pair Ethernet configurations, repeaters are required for cable that runs longer than 100 meters. A repeater with multiple ports is known as a hub. Repeaters work on the physical layer of the OSI model. Repeaters require a small amount of time to regenerate the signal. This can cause a propagation delay which can affect network performance. As a result, many network architectures limit the number of repeaters that can be used in a row, e.g., the Ethernet 5-4-3 rule. Hubs have been mostly obsoleted by modern switches; but repeaters are used for long distance links, notably undersea cabling.
Bridges
A network bridge connects multiple network segments at the data link layer (layer 2) of the OSI model to form a single network. Bridges broadcast to all ports except the port on which the broadcast was received. However, bridges do not promiscuously copy traffic to all ports, as hubs do. Instead, bridges learn which MAC addresses are reachable through specific ports. Once the bridge associates a port with an address, it will send traffic for that address to that port only. Bridges learn the association of ports and addresses by examining the source address of frames that it sees on various ports. Once a frame arrives through a port, the bridge assumes that the MAC address is associated with that port and stores its source address. The first time a bridge sees a previously unknown destination address, the bridge will forward the frame to all ports other than the one on which the frame arrived. Bridges come in three basic types: Local bridges: Directly connect LANs Remote bridges: Can be used to create a wide area network (WAN) link between LANs. Remote bridges, where the connecting link is slower than the end networks, largely have been replaced with routers. Wireless bridges: Can be used to join LANs or connect remote devices to LANs.
According to the text above, judge the following item.
Multiple network segments at the layer 2 of the OSI model can be connected by a network bridge, in order to form a single network.
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