영어 모의고사 (자동 편성)

As technology ①developing rapidly, many new jobs are appearing that did not exist a decade ago. Data analysts, for example, help companies make decisions ②based on huge amounts of information. Renewable energy technicians, ③who install and repair solar panels and wind turbines, are meeting the growing demand for clean power. Healthcare is expanding beyond hospitals, creating roles for specialists ④designing digital health tools for elderly patients. Experts predict that future workers will need ⑤to keep learning new skills throughout their careers.

Central High School will host its annual career fair next Thursday in the main gymnasium. More than thirty companies ①are expected to set up booths to introduce different careers. Students can talk directly with workers ②who represent fields such as medicine, engineering, and design. Each booth will offer a short activity ③designed to let visitors experience a small part of the job. In addition, three guest speakers will give short talks ④about their career paths during the lunch break. Teachers strongly encourage all students ⑤attend, even those who already have a career in mind.

For decades, productivity advice has focused almost entirely on ①managing time efficiently, yet many experts now argue this approach overlooks something important. This alternative approach, ②known as energy management, suggests that controlling personal energy matters more than controlling the clock. Unlike time, which flows at a constant rate, human energy naturally rises and falls in cycles ③shaped by sleep, food, and mental fatigue. A person who tackles difficult tasks during a low-energy period, for instance, may need two hours ④to finish what takes thirty minutes at peak energy. Similarly, someone who forces themselves to keep working without rest often ⑤produce careless mistakes that require additional time to correct later.

Many people believe that a new habit becomes automatic after only twenty-one days, but researchers who ①examined real behavior found a different number. This project, ②called the 66-day habit formation study, tracked volunteers who tried to adopt a new daily habit, like drinking water with lunch. Researchers measured how automatic each action felt over time and found that habits ③needing an average of sixty-six days to become effortless. Yet the actual time varied widely, ranging from about eighteen days for simple habits to over two hundred days for ④difficult ones. This wide range explains why the popular twenty-one-day claim, though easy ⑤to remember, rarely matches real experience.

Hospitals are increasingly adopting artificial intelligence tools that can scan medical images and flag potential tumors ① faster and, in some trials, more accurately than experienced radiologists. Patients understandably welcome any technology that catches disease earlier, yet the growing reliance on these tools raises an uncomfortable legal question that hospitals have only begun to address. When an AI system misses a diagnosis that a human doctor would likely have caught, responsibility becomes ② easy to assign. The doctor may argue that they simply trusted a tool the hospital required them to use, the hospital may argue that the software company guaranteed its accuracy, and the software company may argue that no algorithm can be expected to be ③ perfect. Until legal systems ④ clarify this chain of responsibility, patients harmed by an algorithmic error may find themselves with no clear party to hold ⑤ accountable.

Soyeon joined an online study group chat where dozens of students planned a class project together every evening. When one member suggested skipping a required survey step to save time, several others quickly agreed, and no one raised any objections. Soyeon privately worried that skipping the survey might weaken their project, but seeing everyone else nodding along, she stayed silent rather than risk sounding difficult. The group proceeded confidently, convinced their shortcut was harmless, until their teacher pointed out serious gaps once the project was submitted. Afterward, Soyeon learned about groupthink, a concept introduced by psychologist Irving Janis, describing how tightly connected groups often suppress doubts in order to maintain harmony and reach quick agreement. She realized that ___________.

Not all relationships that people form are equally close or meaningful. (A) Robin Dunbar, an anthropologist, argued that the human brain can only support about one hundred fifty stable relationships at once. (B) Consequently, most of the hundreds of online followers a person accumulates never become genuinely close acquaintances. (C) This cognitive limit applies regardless of how many friends or followers a person collects on social media.

Every semester, students groan when the fire alarm rings for another routine drill, filing out of the building as if the exercise were simply a break from class. (A) During an actual fire, thick smoke can fill hallways within minutes, cutting visibility to almost nothing and making it dangerously easy to panic or take a wrong turn. (B) This casual attitude, however, overlooks why the drill exists in the first place. (C) Repeating the same evacuation route again and again trains the body to move automatically, even when the mind is too frightened to think clearly.

When engineers design the software that controls a self-driving car, they inevitably face a question that philosophers have debated for decades: who should the vehicle protect in an unavoidable crash? (A) This gap between the ideal and the personal choice suggests that ethical programming cannot be settled by public opinion alone. (B) Surveys reveal an uncomfortable contradiction: most people say a car should minimize total harm, yet the same people admit they would never buy a vehicle designed to sacrifice its own owner. (C) Manufacturers, regulators, and philosophers must instead agree on transparent rules before autonomous vehicles are allowed to make such decisions on the road.

Few of these later buyers, however, possess any independent basis for judging whether the asset's price still bears any relation to its underlying value. A speculative bubble typically begins with a genuine, defensible reason for optimism, such as a promising new technology or an unusually favorable economic environment. ( ① ) Early investors who recognize this genuine opportunity purchase the asset and, as expected, often see its price rise accordingly. ( ② ) As word of these early gains spreads, a second wave of investors enters the market, drawn less by independent analysis than by the simple observation that the price has already climbed. ( ③ ) They are, in effect, purchasing confidence rather than value, trusting that the crowd's continued enthusiasm will sustain or further elevate the price. ( ④ ) This reliance on collective momentum rather than fundamental analysis is precisely what transforms a reasonable initial rally into an unsustainable bubble. ( ⑤ ) When the momentum eventually falters, as it inevitably must, the absence of any solid fundamental foundation leaves the price with nothing to support it.