영어 모의고사 (자동 편성)

문제 1

Automation is transforming labor markets more quickly than economists predicted only a decade ago. Factories once staffed by hundreds of workers now rely on robotic arms ①that assemble products with remarkable precision. Warehouses use automated systems ②to sort and deliver packages within a few hours. This shift does not simply eliminate jobs; it also creates roles that ③never exists before. Technicians who maintain robotic equipment are now in high demand, and engineers who design efficient systems ④are highly valued. Policymakers argue that retraining programs ⑤are essential to help displaced workers move into emerging fields.

문제 2

As artificial intelligence grows more capable of performing tasks once considered exclusively human, many people wonder which skills ①remains valuable in the future. AI can now analyze data, generate text, and even diagnose certain medical conditions ②with impressive accuracy. However, most researchers argue that AI still struggles with tasks ③requiring genuine empathy, ethical judgment, and creative problem-solving in unfamiliar situations. A machine can recommend a treatment plan based on statistics, but it cannot comfort a frightened patient the way a compassionate nurse ④can. For this reason, educators increasingly emphasize skills such as collaboration, critical thinking, and emotional intelligence, ⑤arguing that these human capabilities will become more valuable.

문제 3

Long before a user finishes typing a sentence, the keyboard on most smartphones has already begun guessing what word comes next. This feature relies on a language model trained to notice which words commonly follow one another, so it can offer a shortlist of likely candidates above the keyboard, ① saving users countless keystrokes over the course of a day. The system also adapts to each individual, gradually learning a person's favorite phrases, slang, and even the names of friends that no generic dictionary would ② recognize. This constant personalization, however, creates a subtle side effect that few users notice. Because the keyboard ③ nudges people toward its own suggestions, some linguists worry that autocomplete may be quietly ④ widening everyday vocabulary, encouraging writers to accept a convenient suggestion rather than searching for the more precise or original word they might otherwise have ⑤ chosen.

문제 4

Until last spring, Minji ①**rarely** thought twice about tossing a paper cup into the trash after finishing her coffee. Environmental issues seemed ②**distant**, something scientists and politicians should handle rather than an ordinary student like her. That changed when her class was assigned a documentary about plastic waste drifting through the ocean, footage she initially expected to find boring. Instead, watching seabirds mistake plastic fragments for food left her strangely ③**comforted**, and she found herself thinking about the assignment long after class ended. The next morning, almost without deciding to, she slipped her old tumbler into her backpack instead of buying a disposable cup on the way to school. It felt like a small, ④**meaningless** gesture at first. However, as weeks passed, she realized that consistently choosing the tumbler, declining a plastic straw, and reusing a shopping bag were quietly ⑤**adding up**. Small choices, she discovered, become meaningful once repeated.

문제 5

Modern sports wearables, from wrist-worn fitness trackers to smart insoles embedded in running shoes, continuously collect data that would once have required a laboratory to measure. Heart rate, sleep quality, running cadence, and even subtle changes in gait can now be ① monitored during ordinary daily activity rather than only in a controlled clinical setting. Coaches use this constant stream of information to detect early warning signs of overtraining, sometimes noticing a decline in performance ② after the athlete consciously feels tired. However, the flood of data has created a new challenge: distinguishing a meaningful pattern from ordinary daily fluctuation requires expertise that many amateur athletes simply ③ do not have. Some researchers worry that casual users may misinterpret a single unusual reading as a serious problem, leading to ④ needless anxiety rather than useful insight. For this reason, most experts recommend treating wearable data as a general trend to observe over time, not as a precise ⑤ diagnostic tool.

문제 6

The metaverse, a network of interconnected virtual worlds, has begun to generate job opportunities that did not ①exist just a few years ago. Avatar designers create digital identities that represent users within these spaces, ②blending artistic skill with technical programming knowledge. Virtual event planners organize concerts, conferences, and product launches entirely within online platforms, reaching audiences that would be ③easy to gather in person. Some analysts remain skeptical, pointing out that the metaverse has not yet attracted the massive user base that early predictions promised. Nevertheless, major companies continue ④investing heavily in this technology, betting that virtual spaces will eventually become as ⑤significant as physical retail venues.

문제 7

Smartphones, tablets, and computer screens all emit a significant amount of blue light, a short-wavelength light that our eyes are especially sensitive to. During the day, exposure to blue light from the sun helps keep us ① drowsy and mentally sharp, which is generally beneficial. At night, however, the same wavelength can interfere with the body's natural preparation for sleep. Specialized cells in the retina detect blue light and send signals to the brain's internal clock, ② suppressing the release of melatonin, the hormone that signals it is time to rest. Studies have found that people who scroll through their phones in bed before sleeping take longer to fall asleep and experience ③ lighter, more easily disrupted sleep than those who avoid screens. Some devices now offer a night mode that shifts the display toward ④ warmer tones, reducing blue light output in the evening. While this feature may help slightly, sleep researchers generally recommend limiting screen use altogether in the hour before bedtime for the best ⑤ results.

문제 8

Among strategies for improving ①concentration, the Pomodoro Technique remains one of the most widely recommended methods for students and workers. This technique involves working with full attention for twenty-five minutes, then taking a short five-minute break before starting the next session. After completing four of these cycles, a longer break of fifteen to thirty minutes is usually taken to allow mental ②recovery. The method assumes that sustained focus becomes harder to maintain as time passes, so frequent short breaks prevent mental ③fatigue from building up. Students preparing for exams report finishing reading assignments faster when they follow this structured rhythm instead of studying without interruption. Office workers find that dividing large projects into short, timed intervals makes overwhelming tasks feel more ④manageable. Because each interval has a clear start and end, procrastination becomes ⑤more tempting, since beginning a single twenty-five-minute session requires less commitment than beginning an entire project.

문제 9

Long before social media existed, people naturally clustered into groups based on shared traits. (A) These algorithms track patterns such as shared interests, mutual friends, and browsing habits to recommend new connections. (B) Neighborhoods, schools, and workplaces often reflected this tendency, as individuals gravitated toward others who resembled themselves. (C) Today, however, this same tendency operates through recommendation algorithms built into social media platforms.

문제 10

주어진 문장: This unexpected combination of skills fascinated him more than he had expected. When Daniel was a child, he dreamed of becoming an astronaut, yet he assumed such careers belonged only to a handful of government scientists. ( ① ) By the time he entered university, however, the space industry had changed dramatically, as private companies began launching satellites and building reusable rockets. ( ② ) Daniel studied aerospace engineering, drawn to the idea that space exploration was no longer limited to a few national agencies. ( ③ ) During an internship, he helped design a small satellite intended to monitor crop health from orbit, a project that combined engineering with agricultural science. ( ④ ) He realized that the space industry now needed not only pilots and engineers but also data analysts, technicians, and even lawyers familiar with international space regulations. ( ⑤ ) After graduation, Daniel joined a startup developing lunar landing equipment, a job that barely existed when he first imagined his future career.