O Hospital Campo Grande do Norte deseja gerenciar a participação de seus médicos em congressos médicos. Para isso, um banco de dados relacional foi projetado com as tabelas a seguir.
CREATE TABLE Medicos (
id_medico INT PRIMARY KEY,
nome VARCHAR(100) NOT NULL,
especialidade VARCHAR(50) NOT NULL
);
CREATE TABLE Congressos (
id_congresso INT PRIMARY KEY,
nome VARCHAR(100) NOT NULL,
local VARCHAR(100) NOT NULL,
data_inicio DATE NOT NULL,
data_fim DATE NOT NULL CHECK (data_fim >= data_inicio)
);
CREATE TABLE Participacoes (
id_medico INT,
id_congresso INT,
papel VARCHAR(50) CHECK (papel IN (‘Palestrante’, ‘Ouvinte’, ‘Organizador)),
data_inscricao TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (id_medico, id_congresso),
FOREIGN KEY (id_medico) REFERENCES Medicos(id_medico) ON DELETE CASCADE,
FOREIGN KEY (id_congresso) REFERENCES Congressos(id_congresso) ON DELETE CASCADE
);
Além disso, para facilitar consultas, foi criada a seguinte view.
CREATE VIEW V_Medicos_Congressos AS
SELECT M.nome AS medico, C.nome AS congresso, C.local, P.papel
FROM Medicos M
JOIN Participacoes P ON M.id_medico = P.id_medico
JOIN Congressos C ON P.id_congresso = C.id_congresso;
Com base nessa situação hipotética, julgue o item a seguir.
Caso um médico seja removido da tabela Medicos, o sistema lançará um erro de violação de integridade referencial, pois a tabela Participacoes possui uma chave estrangeira que impede a remoção de médicos que tenham participado de congressos.
Acerca da elaboração de políticas de segurança de rede e da implementação de diretrizes de segurança de rede, julgue o item seguinte
Nas organizações, devem‑se configurar redes wi‑fi corporativas utilizando apenas WPA2‑Personal, pois esse protocolo é suficiente para impedir acessos não autorizados em qualquer ambiente empresarial
Com relação aos conceitos da governança de dados e à Lei Geral de Proteção de Dados Pessoais (LGPD), julgue o item a seguir.
A qualidade dos dados é um dos pilares da governança de dados, garantindo que as informações sejam precisas, consistentes e confiáveis para a tomada de decisão
In the early 2000s, when Amazon introduced its Kiva robots to automate warehouse operations, employees feared for their jobs as machines began taking over tasks previously performed by humans. Today, advances in gen AI and natural language processing, such as ChatGPT, are transforming many industries and raising similar concerns. However, unlike past automation technologies, gen AI has the unique potential to impact all job sectors, particularly given its fundamental ability to improve its capabilities over time – which promises to affect the workforce in ways that go beyond simple job replacement.
In new research, forthcoming in Management Science, we explore the impact gen AI has already had on the labor market by examining trends in demand for online freelancers. Our findings show significant short‑term job replacement after these tools were introduced, and that jobs prone to automation, like writing and coding, were the most affected by ChatGPT.Our research also examines how competition, job requirements, and employer willingness‑to‑pay have changed to better understand how the online job market is evolving with the rise of gen AI. Although still in its early stages, gen AI’s impact on online labor markets is already becoming discernible, suggesting potential shifts in long‑term labor market dynamics that could bring both challenges and opportunities.
To conduct our study, we analyzed 1.388.711 job posts from a leading global online freelancing platform from July 2021 to July 2023. Online freelancing platforms provide a good setting for examining emerging trends due to the digital, task‑oriented, and flexible nature of work on these platforms. We focus our analysis on the introduction of two types of gen AI tools: ChatGPT and image‑generating AI. Specifically, we wanted to understand whether the introduction and diffusion of these tools decreased demand for jobs on this platform and, if so, which types of jobs and skills are affected most and by how much.
Using a machine learning algorithm, we first grouped job posts into different categories based on their detailed job descriptions. These categories were then classified into three types: manual‑intensive jobs (e.g., data and office management, video services, and audio services), automation‑prone jobs (e.g., writing; software, app, and web development; engineering), and image‑generating jobs (e.g., graphic design and 3D modeling). We then examined the impact that the introduction of Gen AI tools had on demand across these different types of jobs.
We find that the introduction of ChatGPT and image‑generating tools led to nearly immediate decreases in posts for online gig workers across job types, but particularly for automation‑prone jobs. After the introduction of ChatGPT, there was a 21% decrease in the weekly number of posts in automation‑prone jobs compared to manual‑intensive jobs. Writing jobs were affected the most (30.37% decrease), followed by software, app, and web development (20.62%) and engineering (10.42%).
Internet: www.hbr.org (adapted).
According to the text, Artificial Intelligence is capable of huge impacts in the labor market, due to its ability of improvement.
In the early 2000s, when Amazon introduced its Kiva robots to automate warehouse operations, employees feared for their jobs as machines began taking over tasks previously performed by humans. Today, advances in gen AI and natural language processing, such as ChatGPT, are transforming many industries and raising similar concerns. However, unlike past automation technologies, gen AI has the unique potential to impact all job sectors, particularly given its fundamental ability to improve its capabilities over time – which promises to affect the workforce in ways that go beyond simple job replacement.
In new research, forthcoming in Management Science, we explore the impact gen AI has already had on the labor market by examining trends in demand for online freelancers. Our findings show significant short‑term job replacement after these tools were introduced, and that jobs prone to automation, like writing and coding, were the most affected by ChatGPT.Our research also examines how competition, job requirements, and employer willingness‑to‑pay have changed to better understand how the online job market is evolving with the rise of gen AI. Although still in its early stages, gen AI’s impact on online labor markets is already becoming discernible, suggesting potential shifts in long‑term labor market dynamics that could bring both challenges and opportunities.
To conduct our study, we analyzed 1.388.711 job posts from a leading global online freelancing platform from July 2021 to July 2023. Online freelancing platforms provide a good setting for examining emerging trends due to the digital, task‑oriented, and flexible nature of work on these platforms. We focus our analysis on the introduction of two types of gen AI tools: ChatGPT and image‑generating AI. Specifically, we wanted to understand whether the introduction and diffusion of these tools decreased demand for jobs on this platform and, if so, which types of jobs and skills are affected most and by how much.
Using a machine learning algorithm, we first grouped job posts into different categories based on their detailed job descriptions. These categories were then classified into three types: manual‑intensive jobs (e.g., data and office management, video services, and audio services), automation‑prone jobs (e.g., writing; software, app, and web development; engineering), and image‑generating jobs (e.g., graphic design and 3D modeling). We then examined the impact that the introduction of Gen AI tools had on demand across these different types of jobs.
We find that the introduction of ChatGPT and image‑generating tools led to nearly immediate decreases in posts for online gig workers across job types, but particularly for automation‑prone jobs. After the introduction of ChatGPT, there was a 21% decrease in the weekly number of posts in automation‑prone jobs compared to manual‑intensive jobs. Writing jobs were affected the most (30.37% decrease), followed by software, app, and web development (20.62%) and engineering (10.42%).
Internet: www.hbr.org (adapted).
The conjunction “whether” (third paragraph) in the extract “Specifically, we wanted to understand whether the introduction” can be correctly replaced by although.
In the early 2000s, when Amazon introduced its Kiva robots to automate warehouse operations, employees feared for their jobs as machines began taking over tasks previously performed by humans. Today, advances in gen AI and natural language processing, such as ChatGPT, are transforming many industries and raising similar concerns. However, unlike past automation technologies, gen AI has the unique potential to impact all job sectors, particularly given its fundamental ability to improve its capabilities over time – which promises to affect the workforce in ways that go beyond simple job replacement.
In new research, forthcoming in Management Science, we explore the impact gen AI has already had on the labor market by examining trends in demand for online freelancers. Our findings show significant short‑term job replacement after these tools were introduced, and that jobs prone to automation, like writing and coding, were the most affected by ChatGPT.Our research also examines how competition, job requirements, and employer willingness‑to‑pay have changed to better understand how the online job market is evolving with the rise of gen AI. Although still in its early stages, gen AI’s impact on online labor markets is already becoming discernible, suggesting potential shifts in long‑term labor market dynamics that could bring both challenges and opportunities.
To conduct our study, we analyzed 1.388.711 job posts from a leading global online freelancing platform from July 2021 to July 2023. Online freelancing platforms provide a good setting for examining emerging trends due to the digital, task‑oriented, and flexible nature of work on these platforms. We focus our analysis on the introduction of two types of gen AI tools: ChatGPT and image‑generating AI. Specifically, we wanted to understand whether the introduction and diffusion of these tools decreased demand for jobs on this platform and, if so, which types of jobs and skills are affected most and by how much.
Using a machine learning algorithm, we first grouped job posts into different categories based on their detailed job descriptions. These categories were then classified into three types: manual‑intensive jobs (e.g., data and office management, video services, and audio services), automation‑prone jobs (e.g., writing; software, app, and web development; engineering), and image‑generating jobs (e.g., graphic design and 3D modeling). We then examined the impact that the introduction of Gen AI tools had on demand across these different types of jobs.
We find that the introduction of ChatGPT and image‑generating tools led to nearly immediate decreases in posts for online gig workers across job types, but particularly for automation‑prone jobs. After the introduction of ChatGPT, there was a 21% decrease in the weekly number of posts in automation‑prone jobs compared to manual‑intensive jobs. Writing jobs were affected the most (30.37% decrease), followed by software, app, and web development (20.62%) and engineering (10.42%).
Internet: www.hbr.org (adapted).
The word “willingness” (second paragraph) can be replaced correctly by goodwill without any changes in meaning.
O Hospital Campo Grande está implementando um novo sistema de banco de dados para gerenciar informações sobre médicos, pacientes e atendimentos. O banco de dados apresenta as tabelas a seguir.
CREATE TABLE Medicos (
id_medico INT PRIMARY KEY,
nome VARCHAR(100),
especialidade VARCHAR(50),
salario DECIMAL(10,2)
);
CREATE TABLE Pacientes (
id_paciente INT PRIMARY KEY,
nome VARCHAR(100),
data_nascimento DATE
);
CREATE TABLE Atendimentos (
id_atendimento INT PRIMARY KEY,
id_medico INT,
id_paciente INT,
data_atendimento DATE,
descricao VARCHAR(100),
FOREIGN KEY (id_medico) REFERENCES Medicos(id_medico),
FOREIGN KEY (id_paciente) REFERENCES Pacientes(id_paciente)
);
Com relação à situação hipotética apresentada e aos comandos SQL, julgue o item seguinte.
Para registrar um atendimento em que o médico de id_medico = 10 atendeu o paciente de id_paciente = 50 no momento atual, o comando correto é:
INSERT INTO Atendimentos (id_medico, id_paciente, descricao)
VALUES (10, 50, ‘Consulta de rotina’);
O dr. João é um médico que atua em uma clínica de telemedicina e utiliza um sistema com base em Linux para registrar e acessar prontuários eletrônicos. Durante suas consultas, ele precisa realizar operações avançadas no console do Linux para manipular, pesquisar e proteger arquivos médicos. Com base nessa situação hipotética e nos comandos utilizados em Linux a ela relacionados, julgue o item seguinte.
O dr. João deseja encontrar todas as ocorrências do termo “diabetes tipo 2” em arquivos de prontuários no diretório “/home/pacientes/”. Ele pode utilizar o comando grep -irl “diabetes tipo 2” /home/pacientes/ para listar os arquivos que contêm essa expressão.
Em relação ao gerenciamento de serviços de TI com ITIL e ao gerenciamento de projetos de TI com PMBOK, julgue o item a seguir
O gerenciamento da comunicação é um dos pilares do PMBOK, especialmente em projetos de TI, nos quais a complexidade técnica exige clareza e alinhamento entre as partes interessadas.
Acerca dos monitores de vídeo, das unidades de disco rígido e das partições Linux, julgue o item a seguir
Os discos rígidos armazenam dados em discos magnéticos giratórios e utilizam um braço mecânico com uma cabeça de leitura/gravação para acessar as informações.
Quanto ao DHCP, aos equipamentos de rede e às VLANs, julgue o item a seguir.
O DHCP é um protocolo de serviço TCP/IP que oferece alocação dinâmica de endereços IP e configurações de rede, como máscaras de sub-rede e gateways padrão.
No que diz respeito aos conceitos de segurança da informação e aos tipos de ameaças, julgue o item seguinte
APT (Advanced Persistent Threats) são ataques sofisticados e prolongados que visam às infraestruturas críticas.
Quanto ao DHCP, aos equipamentos de rede e às VLANs, julgue o item a seguir.
Um firewall opera, especificamente, na camada de sessão (camada 5) do modelo OSI e controla o acesso com base em endereços IP e em portas de rede.
In the early 2000s, when Amazon introduced its Kiva robots to automate warehouse operations, employees feared for their jobs as machines began taking over tasks previously performed by humans. Today, advances in gen AI and natural language processing, such as ChatGPT, are transforming many industries and raising similar concerns. However, unlike past automation technologies, gen AI has the unique potential to impact all job sectors, particularly given its fundamental ability to improve its capabilities over time – which promises to affect the workforce in ways that go beyond simple job replacement.
In new research, forthcoming in Management Science, we explore the impact gen AI has already had on the labor market by examining trends in demand for online freelancers. Our findings show significant short‑term job replacement after these tools were introduced, and that jobs prone to automation, like writing and coding, were the most affected by ChatGPT.Our research also examines how competition, job requirements, and employer willingness‑to‑pay have changed to better understand how the online job market is evolving with the rise of gen AI. Although still in its early stages, gen AI’s impact on online labor markets is already becoming discernible, suggesting potential shifts in long‑term labor market dynamics that could bring both challenges and opportunities.
To conduct our study, we analyzed 1.388.711 job posts from a leading global online freelancing platform from July 2021 to July 2023. Online freelancing platforms provide a good setting for examining emerging trends due to the digital, task‑oriented, and flexible nature of work on these platforms. We focus our analysis on the introduction of two types of gen AI tools: ChatGPT and image‑generating AI. Specifically, we wanted to understand whether the introduction and diffusion of these tools decreased demand for jobs on this platform and, if so, which types of jobs and skills are affected most and by how much.
Using a machine learning algorithm, we first grouped job posts into different categories based on their detailed job descriptions. These categories were then classified into three types: manual‑intensive jobs (e.g., data and office management, video services, and audio services), automation‑prone jobs (e.g., writing; software, app, and web development; engineering), and image‑generating jobs (e.g., graphic design and 3D modeling). We then examined the impact that the introduction of Gen AI tools had on demand across these different types of jobs.
We find that the introduction of ChatGPT and image‑generating tools led to nearly immediate decreases in posts for online gig workers across job types, but particularly for automation‑prone jobs. After the introduction of ChatGPT, there was a 21% decrease in the weekly number of posts in automation‑prone jobs compared to manual‑intensive jobs. Writing jobs were affected the most (30.37% decrease), followed by software, app, and web development (20.62%) and engineering (10.42%).
Internet: www.hbr.org (adapted).
The researchers discovered that Artificial Intelligence has increased automation-prone jobs, especially compared to manual-intensive jobs.
O Hospital Campo Grande do Norte deseja gerenciar a participação de seus médicos em congressos médicos. Para isso, um banco de dados relacional foi projetado com as tabelas a seguir.
CREATE TABLE Medicos (
id_medico INT PRIMARY KEY,
nome VARCHAR(100) NOT NULL,
especialidade VARCHAR(50) NOT NULL
);
CREATE TABLE Congressos (
id_congresso INT PRIMARY KEY,
nome VARCHAR(100) NOT NULL,
local VARCHAR(100) NOT NULL,
data_inicio DATE NOT NULL,
data_fim DATE NOT NULL CHECK (data_fim >= data_inicio)
);
CREATE TABLE Participacoes (
id_medico INT,
id_congresso INT,
papel VARCHAR(50) CHECK (papel IN (‘Palestrante’, ‘Ouvinte’, ‘Organizador)),
data_inscricao TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (id_medico, id_congresso),
FOREIGN KEY (id_medico) REFERENCES Medicos(id_medico) ON DELETE CASCADE,
FOREIGN KEY (id_congresso) REFERENCES Congressos(id_congresso) ON DELETE CASCADE
);
Além disso, para facilitar consultas, foi criada a seguinte view.
CREATE VIEW V_Medicos_Congressos AS
SELECT M.nome AS medico, C.nome AS congresso, C.local, P.papel
FROM Medicos M
JOIN Participacoes P ON M.id_medico = P.id_medico
JOIN Congressos C ON P.id_congresso = C.id_congresso;
Com base nessa situação hipotética, julgue o item a seguir.
O comando a seguir encontra todos os médicos que participaram de congressos em 2023, mas não participaram de nenhum congresso em 2024.
SELECT DISTINCT M.nome
FROM Medicos M
JOIN Participacoes P ON M.id_medico = P.id_medico
JOIN Congressos C ON P.id_congresso = C.id_congresso
WHERE EXTRACT(YEAR FROM C.data_inicio) = 2023
AND M.id_medico NOT IN (
SELECT P2.id_medico
FROM Participacoes P2
JOIN Congressos C2 ON P2.id_congresso = C2.id_congresso
WHERE EXTRACT(YEAR FROM C2.data_inicio) = 2024
);
