Question

18．With the coming of big data age，data science is supposed to be starved for，of which the adaption can point a profound change in corporate competitiveness．Companies，both born in the digital era and traditional world are showing off their skills in data science．Therefore，it seems to have been creating a great demand for the experts of this type．
Mr Carlos Guestrin，machine learning professor from University of Washington argues that all software applications will need inbuilt intelligence within five years，making data scientists-people trained to analyze large bodies of information-key workers in this emerging"cognitive"technology economy．There are already critical applications that depend on machine learning，a subfield of data science，led by recommendation programs，fraud detection systems，forecasting tools and applications for predicting customer behavior．
Many companies that are born digital-particularly internet companies that have a great number of real-time customer interactions to handle-are all-in when it comes to data science．Pinterest，for instance，maintains more than 100 machine learning models that could be applied to different classes of problems，and it constantly fields requests from managers eager to use this resource to deal with their business problem．
The factors weighing on many traditional companies will be the high cost of mounting a serious machine-learning operation．Netflix is estimated to spend﹩150m a year on a single application and the total bill is probably four times that once all its uses of the technology are taken into account．
Another problem for many non-technology companies is talent．Of the computer science experts who use Kaggle，only about 1，000 have deep learning skills，compared to 100，000who can apply other machine learning techniques，says Mr Goldbloom．He adds that even some big companies of this type are often reluctant to expand their pay scales to hire the top talent in this field．
The biggest barrier to adapting to the coming era of"smart"applications，however，is likely to be cultural．Some companies，such as General Electric，have been building their own Silicon Valley presence to attract and develop the digital skills they will need．
Despite the obstacles，some may master this difficult transition．But companies that were built，from the beginning，with data science at their center，are likely to represent serious competition．

63．Which one is obstacle for many traditional companies to popularize learning operation？C
A．Technological problem．
B．Expert crisis．
C．High cost．
D．Customer interactions．
64．What can not be inferred from the passage about the machine learning？D
A．Machine learning operations are costly in Netflix．
B．Machine learning plays an important role in existent applications．
C．Machine learning experts are not highly paid in some non-technology companies．
D．Machine learning models are not sufficient to solve business problems in Pinterest．
65．The underlined word in the 3rd paragraph"fields"most probably meansC．
A．avoids  B．createsC．solvesD．classifies
66．Which of the following is the best title for this passage？A
A．Data science：a forefront force in tech business
B．Corporate competition：an obstacle to the transition
C．Machine learning：a key to smart technology
D．Technique experts：a decisive factor of the coming era．

Answer 1

分析 本文属于说明文阅读，作者通过这篇文章主要向我们描述了数据科学是科技行业的前沿力量，许多大型企业都要变革发展．

解答 63．C．细节理解题．根据第四段The factors weighing on many traditional companies will be the high cost of mounting a serious machine-learning operation可知高成本是传统企业推广学习操作的障碍；故选C．
64．D．细节理解题．根据文章Pinterest，for instance，maintains more than 100 machine learning models that could be applied to different classes of problems，and it constantly fields requests from managers eager to use this resource to deal with their business problem可知管理者认为Pinterest的机器学习模型是可以解决现有的商业问题的；故选D．
65．C．词义猜测题．根据文章Pinterest，for instance，maintains more than 100 machine learning models that could be applied to different classes of problems，and it constantly fields requests from managers eager to use this resource to deal with their business problem可知管理者认为Pinterest的机器学习模型是可以解决现有的商业问题的；意为解决；故选C．
66．A．推理判断题．根据第一段With the coming of big data age，data science is supposed to be starved for，of which the adaption can point a profound change in corporate competitiveness可知这篇文章最好的题目为数据科学：科技行业的前沿力量；故选A．

点评 本文是一篇广告布告类阅读，题目涉及多道主旨大意题，细节理解题，推理判断题．做题时学生应仔细阅读原文，把握文章主要内容，联系文章上下文内容并结合所给选项含义，从中选出正确答案，一定要做到有理有据，切忌胡乱猜测．