영어 모의고사 (자동 편성)

Before making a purchase, most online shoppers now read reviews ①written by previous customers rather than relying solely on advertisements. These reviews, posted by people who have no financial connection to the seller, often ②seem more trustworthy than promotional claims. Research shows that products with a large number of positive reviews ③sell significantly more than those with few or no reviews at all. However, fake reviews written by paid writers or even automated programs ④has become a serious problem, undermining consumer trust. To address this issue, several review platforms have begun ⑤using artificial intelligence to identify suspicious patterns.

The metaverse, a network of interconnected virtual worlds, has begun ①generate job opportunities that did not exist just a few years ago. Companies now hire virtual world designers who build digital environments ②where users can socialize, shop, or attend events. Avatar designers create digital identities ③that represent users within these spaces, blending artistic skill with technical programming knowledge. Some analysts remain skeptical, ④pointing out that the metaverse has not yet attracted the massive user base that early predictions promised. Nevertheless, major companies continue ⑤investing heavily in this technology.

Years ago, a small shoe company ①struggled to compete with larger, well-funded rivals in the market. One day, the founder decided to share the story behind the company: how her grandfather had once repaired shoes for factory workers ②who could not afford new pairs. Surprisingly, customers started responding not just to the shoes themselves but to the values the story represented. Sales slowly increased as people felt they ③was supporting something meaningful rather than simply buying a product. Other companies noticed this shift and began crafting their own narratives, ④hoping to create similar emotional connections. Eventually, the founder realized that a compelling story, ⑤told honestly, could turn ordinary customers into loyal supporters.

Modern factories increasingly rely on collaborative robots, often called cobots, which are designed to work ①safely alongside human employees. Unlike traditional industrial robots that operate behind safety cages, cobots use sensors to detect nearby workers and adjust their movements accordingly. Cobots typically handle repetitive or physically demanding tasks, while human workers focus on tasks that require ②judgment and fine motor skills. This division of labor increases overall ③efficiency, since each side contributes what it does best. Manufacturers who have introduced cobots report ④more workplace injuries, largely because employees no longer perform exhausting repetitive motions all day. As the technology becomes more affordable, smaller companies are also beginning to ⑤adopt cobots.

As technology continues to reshape the job market, educators and employers increasingly agree on which skills will matter most in the coming decades. Technical literacy, including basic coding and data analysis, has become nearly as ①fundamental as reading and writing once were. Beyond technical knowledge, however, employers consistently rank problem-solving and adaptability among the most ②valuable qualities in job candidates. Workers who can quickly learn new tools and adjust to unfamiliar situations tend to remain ③employable even as entire industries transform. Schools have begun redesigning curricula to emphasize these competencies, moving away from memorization toward project-based learning that mirrors real workplace ④comfort. Most experts insist that a balanced combination of technical skill, adaptability, and communication will define successful careers ⑤ahead.

As companies collect enormous amounts of personal data to improve their products and target advertisements, serious ethical questions have emerged about how that information should be handled. Employees who work with big data now face ①minimal responsibilities that previous generations rarely encountered, since a single careless decision can expose millions of people's private information. Some argue that strict regulations should govern data collection, even if such rules ②slow innovation and increase costs for businesses. Others insist that self-regulation within companies, guided by clear ethical training, can protect consumers just as ③effectively without stifling progress. Most experts agree that professionals handling sensitive data must be trained not only in technical skills but also in ethical ④decision-making. Universities have begun adding data ethics courses, recognizing that future employees will need to ⑤balance innovation with responsibility.

Three-dimensional printing technology is changing how products are designed and manufactured across many industries. Unlike traditional manufacturing, which often requires expensive molds and long production times, ___________. This flexibility allows companies to produce small batches of customized products without the massive costs typically associated with mass production.

When Daniel was a child, he dreamed of becoming an astronaut, yet he assumed such careers belonged only to a handful of government scientists. During an internship, he helped design a small satellite intended to monitor crop health from orbit, a project that combined engineering with agricultural science. He realized that ___________. After graduation, Daniel joined a startup developing lunar landing equipment, a job that barely existed when he first imagined his future career.

The growing presence of AI in creative fields has sparked lively discussion among artists and critics alike. (A) In contrast, some artists worry that relying too heavily on such tools may weaken the value placed on original human creativity. (B) Many artists have embraced AI as a way to explore new styles quickly and overcome creative blocks. (C) Given these opposing views, most experts now agree that AI works best as a partner rather than a replacement for human creators.

주어진 문장: Back then, few people could have predicted that such a job would even exist. When Mina graduated from college five years ago, she had never even heard the term "data scientist." ( ① ) She had studied statistics simply because she enjoyed solving puzzles with numbers. ( ② ) Everything changed when a small tech startup, which was building an app to predict traffic patterns, hired her to analyze massive amounts of data. ( ③ ) Mina quickly discovered that she loved turning raw numbers into insights that helped real people make better decisions. ( ④ ) Within two years, demand for professionals like her had exploded, as companies in every industry realized how valuable data could be. ( ⑤ ) Today, Mina leads a team of ten analysts, mentoring young graduates.