Questões de Concurso
Sobre verbos | verbs em inglês
Foram encontradas 2.952 questões


Identify the item that best replaces the phrasal verb in bold type.
I’ve finally got over the problem.
I’ve just ran into your sister on the mall.
He tore up all her letters when she decided to move.
Considering the excerpt taken from the text 04 "In this way, educators should draw on the Multiliteracies framework and reconsider their instructional approaches in order to familiarize students..." (lines 28 to 31), answer the question.
The modal verb “should” brings the idea of:
Atenção: Para responder à questão, considere o texto abaixo.
been received by the purchaser. To illustrate goods in transit, let's use the following example. Company J ships a truckload of merchandise on December 30 to Customer K, which is located 2,000 miles away. The truckload of merchandise arrives at Customer K on January 2. Between December 30 and January 2, the truckload of merchandise is goods in transit. The goods in transit requires special attention if the companies issue financial statements as of December 31. The reason is that the merchandise is the inventory of one of the two companies. However, the merchandise is not physically present at either company. One of the two companies must add the cost of the goods in transit to the cost of the inventory that it has in its possession.
include the goods in transit as its inventory. On December 31, Customer K is the owner of the goods in transit and will need to report a purchase, a payable, and must add the cost of the goods in transit to the cost of the inventory which is in its possessionIf the terms of the sale are FOB destination, Company J will not have a sale and receivable until January 2. This means Company J must report the cost of the goods in transit in its inventory on December 31. (Customer K will not have a purchase, payable, or inventory of these goods until January 2.)
(Adapted from http://www.accountingcoach.com/blog/what-are-goods-in-transit)
In the text CB3A1AAA,
the verb “realize” (l.7) can be replaced by accomplish without
any change in the meaning of the sentence.
TEXT II
The backlash against big data
[…]
Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voice-recognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data.
The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem.
There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers.
(From http://www.economist.com/blogs/economist explains/201 4/04/economist-explains-10)

