Questões de Língua Inglesa do ano 2025

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Listagem de Questões de Língua Inglesa do ano 2025

#Questão 1077264 - Língua Inglesa, Interpretação de texto | Reading comprehension, CESPE / CEBRASPE, 2025, MPE/CE, Analista Ministerial - Especialidade: Ciências da Computação

        The business intelligence (BI) industry has been challenged with poor user adoption for years. Yet, many CIOs continue to push BI as a core initiative. Billions of dollars have been spent on traditional BI, but adoption rates are below 30%. Why? Successfully levering the full capabilities of business intelligence is still difficult to achieve, and product managers are searching for more. These individuals are looking for ways to expand the impact and value of their BI tools but are lost about where to start.

        The overall goal of BI is to provide business teams with the proper data and information at the right time to create insightful, data-driven decision-making. However, these solutions fall short and continually fail the industry through inefficiency, hefty costs, and an overall lack of value and insightful data production.

        Currently, traditional BI solutions force users to exit their current workflow to even attempt and secure any valuable data. When your team is operating in the middle of their workflow and needs data to inform a decision, they shouldn’t have to exit the application to enter yet another application, gather data and then jump back in. The likelihood of delays in report deliverability also factors into this headache. This process dramatically slows down any workflow and causes frustration for employees, especially when the data secured isn’t always useful.

        Additionally, many BI tools are not designed for business users but instead for more technical individuals within the organization. Traditional vendors often try to cover the complexity of their solution with self-service options and features, but users continue to feel like they need an advanced engineering or computer science degree to navigate them. This sucks up valuable time for non-technical users as they work to navigate a difficult platform to get the information they need.

Internet:: <rtinsights.com>  (adapted). 

Based on the previous text, judge the following item.  


The most frequent reason for low BI adoption is that users discover provided data ever-more insightful and directly applicable in the current workflow. 


#Questão 1077265 - Língua Inglesa, Interpretação de texto | Reading comprehension, CESPE / CEBRASPE, 2025, MPE/CE, Analista Ministerial - Especialidade: Ciências da Computação

        The business intelligence (BI) industry has been challenged with poor user adoption for years. Yet, many CIOs continue to push BI as a core initiative. Billions of dollars have been spent on traditional BI, but adoption rates are below 30%. Why? Successfully levering the full capabilities of business intelligence is still difficult to achieve, and product managers are searching for more. These individuals are looking for ways to expand the impact and value of their BI tools but are lost about where to start.

        The overall goal of BI is to provide business teams with the proper data and information at the right time to create insightful, data-driven decision-making. However, these solutions fall short and continually fail the industry through inefficiency, hefty costs, and an overall lack of value and insightful data production.

        Currently, traditional BI solutions force users to exit their current workflow to even attempt and secure any valuable data. When your team is operating in the middle of their workflow and needs data to inform a decision, they shouldn’t have to exit the application to enter yet another application, gather data and then jump back in. The likelihood of delays in report deliverability also factors into this headache. This process dramatically slows down any workflow and causes frustration for employees, especially when the data secured isn’t always useful.

        Additionally, many BI tools are not designed for business users but instead for more technical individuals within the organization. Traditional vendors often try to cover the complexity of their solution with self-service options and features, but users continue to feel like they need an advanced engineering or computer science degree to navigate them. This sucks up valuable time for non-technical users as they work to navigate a difficult platform to get the information they need.

Internet:: <rtinsights.com>  (adapted). 

Based on the previous text, judge the following item.  


According to the text, overall, BI solutions are not accomplishing the purpose they were designed for, since they have not been able to provide timely and readily available data to business users within their working cycles.

#Questão 1077266 - Língua Inglesa, Interpretação de texto | Reading comprehension, CESPE / CEBRASPE, 2025, MPE/CE, Analista Ministerial - Especialidade: Ciências da Computação

        The business intelligence (BI) industry has been challenged with poor user adoption for years. Yet, many CIOs continue to push BI as a core initiative. Billions of dollars have been spent on traditional BI, but adoption rates are below 30%. Why? Successfully levering the full capabilities of business intelligence is still difficult to achieve, and product managers are searching for more. These individuals are looking for ways to expand the impact and value of their BI tools but are lost about where to start.

        The overall goal of BI is to provide business teams with the proper data and information at the right time to create insightful, data-driven decision-making. However, these solutions fall short and continually fail the industry through inefficiency, hefty costs, and an overall lack of value and insightful data production.

        Currently, traditional BI solutions force users to exit their current workflow to even attempt and secure any valuable data. When your team is operating in the middle of their workflow and needs data to inform a decision, they shouldn’t have to exit the application to enter yet another application, gather data and then jump back in. The likelihood of delays in report deliverability also factors into this headache. This process dramatically slows down any workflow and causes frustration for employees, especially when the data secured isn’t always useful.

        Additionally, many BI tools are not designed for business users but instead for more technical individuals within the organization. Traditional vendors often try to cover the complexity of their solution with self-service options and features, but users continue to feel like they need an advanced engineering or computer science degree to navigate them. This sucks up valuable time for non-technical users as they work to navigate a difficult platform to get the information they need.

Internet:: <rtinsights.com>  (adapted). 

Based on the previous text, judge the following item.  


It is correct to conclude from the text that one main hindrance to the successful implementation of BI is the need to stop ongoing operations to gather and secure data.

#Questão 1077945 - Língua Inglesa, Interpretação de texto | Reading comprehension, CESPE / CEBRASPE, 2025, EMBRAPA,

        In the 20th century, we made tremendous advances in discovering fundamental principles in different scientific disciplines that created major breakthroughs in management and technology for agricultural systems, mostly by empirical means. However, as we enter the 21st century, agricultural research has more difficult and complex problems to solve.


        The environmental consciousness of the general public is requiring us to modify farm management to protect water, air, and soil quality, while staying economically profitable. At the same time, market-based global competition in agricultural products is challenging economic viability of the traditional agricultural systems, and requires the development of new and dynamic production systems. Fortunately, the new electronic technologies can provide us a vast amount of real-time information about crop conditions and near-term weather via remote sensing by satellites or ground-based instruments and the Internet, that can be utilized to develop a whole new level of management. However, we need the means to capture and make sense of this vast amount of site-specific data.


        Our customers, the agricultural producers, are asking for a quicker transfer of research results in an integrated usable form for site-specific management. Such a request can only be met with system models, because system models are indeed the integration and quantification of current knowledge based on fundamental principles and laws. Models enhance understanding of data taken under certain conditions and help extrapolate their applications to other conditions and locations.


Lajpat R. Ahuja; Liwang Ma; Terry A. Howell. Whole System Integration and Modeling — Essential to

Agricultural Science and Technology in the 21st Century. In: Lajpat R. Ahuja; Liwang Ma; Terry A. Howell

(eds.) Agricultural system models in field research and technology transfer.

Boca Raton, CRC Press LLC, 2002 (adapted). 

Considering the text presented above, judge the following item. 


The text focuses on showing how the advances made in the 20th century were essential to the development of the notion of agricultural systems.  

#Questão 1078319 - Língua Inglesa, Palavras conectivas | Connective words, CESPE / CEBRASPE, 2025, EMBRAPA,

        In the 20th century, we made tremendous advances in discovering fundamental principles in different scientific disciplines that created major breakthroughs in management and technology for agricultural systems, mostly by empirical means. However, as we enter the 21st century, agricultural research has more difficult and complex problems to solve.


        The environmental consciousness of the general public is requiring us to modify farm management to protect water, air, and soil quality, while staying economically profitable. At the same time, market-based global competition in agricultural products is challenging economic viability of the traditional agricultural systems, and requires the development of new and dynamic production systems. Fortunately, the new electronic technologies can provide us a vast amount of real-time information about crop conditions and near-term weather via remote sensing by satellites or ground-based instruments and the Internet, that can be utilized to develop a whole new level of management. However, we need the means to capture and make sense of this vast amount of site-specific data.


        Our customers, the agricultural producers, are asking for a quicker transfer of research results in an integrated usable form for site-specific management. Such a request can only be met with system models, because system models are indeed the integration and quantification of current knowledge based on fundamental principles and laws. Models enhance understanding of data taken under certain conditions and help extrapolate their applications to other conditions and locations.


Lajpat R. Ahuja; Liwang Ma; Terry A. Howell. Whole System Integration and Modeling — Essential to

Agricultural Science and Technology in the 21st Century. In: Lajpat R. Ahuja; Liwang Ma; Terry A. Howell

(eds.) Agricultural system models in field research and technology transfer.

Boca Raton, CRC Press LLC, 2002 (adapted).

Considering the text presented above, judge the following item. 


The use of “However”, in the last sentence of the second paragraph, helps to indicate that the vast amount of data that technology can provide is not enough to meet the needs of agricultural producers. 

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