Técnico Judiciário – Área: Apoio Especializado – Especialidade: Programação de Sistemas - 2024

Julgue o item a seguir, a respeito de técnicas de ingestão de dados, análise de dados e Big Data.

Na ingestão de dados, a arquitetura lambda utiliza o processamento em lote para fornecer visualizações das informações e utiliza a atualização em tempo real para ajudar os gestores a visualizarem dados críticos e urgentes.

Julgue o item a seguir, a respeito de técnicas de ingestão de dados, análise de dados e Big Data.

Na abordagem ETL, os dados são carregados no mesmo estado em que foram extraídos e são transformados no estágio posterior ao carregamento.

“Certainly, here is a possible introduction for your topic...”, began a recent article in Surfaces and Interfaces, a scientific journal. Attentive readers might have wondered who exactly that bizarre opening line was addressing. They might also have wondered whether the article was written by a human or by a machine.      It is a question ever more readers of scientific papers are asking. LLMs (Large Language Models) are now more than good enough to help write a scientific paper. They can breathe life into dense scientific prose and speed up the drafting process, especially for non-native English speakers. Such use also comes with risks: LLMs are particularly susceptible to reproducing biases, for example, and can churn out vast amounts of plausible nonsense.

Internet:<economist.com>(adapted).

According to the information stated in the preceding text and the vocabulary used in it, judge the following item.

The word “biases” (last sentence of the text) is, in its context, an adverb.

“Certainly, here is a possible introduction for your topic...”, began a recent article in Surfaces and Interfaces, a scientific journal. Attentive readers might have wondered who exactly that bizarre opening line was addressing. They might also have wondered whether the article was written by a human or by a machine.      It is a question ever more readers of scientific papers are asking. LLMs (Large Language Models) are now more than good enough to help write a scientific paper. They can breathe life into dense scientific prose and speed up the drafting process, especially for non-native English speakers. Such use also comes with risks: LLMs are particularly susceptible to reproducing biases, for example, and can churn out vast amounts of plausible nonsense.

Internet:<economist.com>(adapted).

According to the information stated in the preceding text and the vocabulary used in it, judge the following item.

Large Language Models are able to produce flawless scientific texts.

“Certainly, here is a possible introduction for your topic...”, began a recent article in Surfaces and Interfaces, a scientific journal. Attentive readers might have wondered who exactly that bizarre opening line was addressing. They might also have wondered whether the article was written by a human or by a machine.      It is a question ever more readers of scientific papers are asking. LLMs (Large Language Models) are now more than good enough to help write a scientific paper. They can breathe life into dense scientific prose and speed up the drafting process, especially for non-native English speakers. Such use also comes with risks: LLMs are particularly susceptible to reproducing biases, for example, and can churn out vast amounts of plausible nonsense.

Internet:<economist.com>(adapted).

According to the information stated in the preceding text and the vocabulary used in it, judge the following item.

The expression “churn out” (last sentence of the text) could be replaced with crank out, without harming the correctness of the sentence or its original meaning.

“Certainly, here is a possible introduction for your topic...”, began a recent article in Surfaces and Interfaces, a scientific journal. Attentive readers might have wondered who exactly that bizarre opening line was addressing. They might also have wondered whether the article was written by a human or by a machine.      It is a question ever more readers of scientific papers are asking. LLMs (Large Language Models) are now more than good enough to help write a scientific paper. They can breathe life into dense scientific prose and speed up the drafting process, especially for non-native English speakers. Such use also comes with risks: LLMs are particularly susceptible to reproducing biases, for example, and can churn out vast amounts of plausible nonsense.

Internet:<economist.com>(adapted).

According to the information stated in the preceding text and the vocabulary used in it, judge the following item.

The article mentioned in the first paragraph of the text was written with the help of LLMs.

“Certainly, here is a possible introduction for your topic...”, began a recent article in Surfaces and Interfaces, a scientific journal. Attentive readers might have wondered who exactly that bizarre opening line was addressing. They might also have wondered whether the article was written by a human or by a machine.      It is a question ever more readers of scientific papers are asking. LLMs (Large Language Models) are now more than good enough to help write a scientific paper. They can breathe life into dense scientific prose and speed up the drafting process, especially for non-native English speakers. Such use also comes with risks: LLMs are particularly susceptible to reproducing biases, for example, and can churn out vast amounts of plausible nonsense.

Internet:<economist.com>(adapted).

According to the information stated in the preceding text and the vocabulary used in it, judge the following item.

The first sentence of the second paragraph could be correctly rewritten as It is a question that readers of scientific papers are increasingly asking.

The Internet, as anyone who works deep in its trenches will tell you, is not a smooth, well-oiled machine. It’s a messy patchwork that has been assembled over decades, and it is held together with the digital equivalent of duct tape and bubble gum. Much of it relies on open-source software that is thanklessly maintained by a small army of volunteer programmers who fix the bugs.

Internet: <www.nytimes.com> (adapted).

Considering the previous text and its linguistic aspects, judge the following item.

The word “it”, in the last sentence of the text, refers to “bubble gum”, mentioned in the prior sentence.

The Internet, as anyone who works deep in its trenches will tell you, is not a smooth, well-oiled machine. It’s a messy patchwork that has been assembled over decades, and it is held together with the digital equivalent of duct tape and bubble gum. Much of it relies on open-source software that is thanklessly maintained by a small army of volunteer programmers who fix the bugs.

Internet: <www.nytimes.com> (adapted).

Considering the previous text and its linguistic aspects, judge the following item.

The author suggests that the Internet is, metaphorically speaking, a malfunctioning machine.

The Internet, as anyone who works deep in its trenches will tell you, is not a smooth, well-oiled machine. It’s a messy patchwork that has been assembled over decades, and it is held together with the digital equivalent of duct tape and bubble gum. Much of it relies on open-source software that is thanklessly maintained by a small army of volunteer programmers who fix the bugs.

Internet: <www.nytimes.com> (adapted).

Considering the previous text and its linguistic aspects, judge the following item.

The Internet depends on software that is poorly maintained by a large team of volunteer programmers.