영어 모의고사 (자동 편성)

Every plastic product carries a small recycling mark, but few students know ①what these marks mean. Each symbol contains a number ②that shows what material the item is made of. Reading these marks is similar to ③reading nutrition labels on snacks, which tell you exactly what is inside. Likewise, ④throwing plastic away without checking its mark can send a reusable item straight to a landfill. Some marks even show that a material cannot be recycled at all, so it must ⑤sorted separately.

As companies collect enormous amounts of personal data to improve their products and target advertisements, serious ethical questions ①have emerged about how that information should be handled. Employees who work with big data now face responsibilities ②that 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 ③slow innovation and increase costs for businesses. Others insist that self-regulation within companies, guided by clear ethical training, can ④protects consumers just as effectively without stifling progress. Universities have begun adding data ethics courses to computer science programs, ⑤recognizing that future employees will need to balance innovation with responsibility.

Building brand loyalty has traditionally been considered one of the most valuable goals in marketing, since loyal customers ①tend to spend more and recommend products to others. Companies invest significant resources in loyalty programs, ②offering points, discounts, and exclusive access to encourage repeat purchases. Genuine loyalty, critics contend, should be earned through consistent quality and honest treatment of customers, not through artificial incentives that merely ③delays a customer's departure. Furthermore, excessive focus on retaining existing customers can cause companies ④to neglect innovation, since they assume loyal buyers will continue purchasing regardless of improvements. Relying solely on loyalty programs without addressing underlying product quality may ultimately ⑤backfire.

When Daniel was a child, he dreamed of becoming an astronaut, yet he assumed such careers belonged only to a handful of government scientists. By the time he entered university, however, the space industry had changed ①dramatically, as private companies began launching satellites and building reusable rockets. Daniel studied aerospace engineering, drawn to the idea that space exploration was no longer ②limited to a few national agencies. During an internship, he helped design a small satellite intended to ③monitor crop health from orbit. 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. After graduation, Daniel joined a startup developing lunar landing equipment, a job that ④barely existed when he first imagined his future career. His story shows how commercial space ventures have ⑤closed doors that once seemed impossibly narrow.

Last month, I attended a three-day career camp during summer vacation. On the first day, I chose the broadcasting workshop, where students practiced writing short news scripts and reading them in front of a camera. It was ①harder than I expected, since staying calm while a camera recorded my every move felt strange at first. By the second day, however, I began to enjoy the challenge, and my confidence ②grew with each attempt. The instructors, who all had real broadcasting experience, gave detailed feedback rather than simple praise, which helped us improve ③quickly. On the final day, each team presented a short news segment to the entire camp. Although I still feel ④certain about my future career, this camp helped me ⑤understand what working in media actually involves.

Online courses have become a popular way for students to learn practical job skills outside of school. Unlike traditional classes, these courses let learners study at their own pace and repeat difficult lessons as many times as needed. Many courses also include practical projects, so students can build a portfolio while they learn. However, taking an online course requires ___________, since no teacher checks whether students are keeping up. Experts recommend setting a fixed weekly schedule and finishing small assignments regularly.

Factories that once employed hundreds of workers on an assembly line now run with a fraction of that staff, as robotic arms handle tasks that used to require human hands. (A) However, critics point out that earlier transitions unfolded over generations, giving workers time to retrain, whereas today's robots are being deployed much faster. (B) Historical evidence offers some comfort: previous waves of automation eventually created new categories of work that no one had predicted. (C) A factory worker displaced this year, therefore, cannot simply wait for a new industry to emerge decades from now.

Some researchers worry that casual users may misinterpret a single unusual reading as a serious problem, leading to needless anxiety rather than useful insight. 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. ( ① ) Heart rate, sleep quality, running cadence, and even subtle changes in gait can now be monitored during ordinary daily activity rather than only in a controlled clinical setting. ( ② ) Coaches use this constant stream of information to detect early warning signs of overtraining, sometimes noticing a decline in performance before the athlete consciously feels tired. ( ③ ) 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 do not have. ( ④ ) For this reason, most experts recommend treating wearable data as a general trend to observe over time, not as a precise diagnostic tool. ( ⑤ )

At that distant point in time, the future increase feels almost costless, since no immediate sacrifice is required. One practical application of hyperbolic discounting involves a savings program that gradually raises an employee's contribution rate over several years. ( ① ) Employees are first asked to commit, well in advance, to increasing their savings rate at a specific future date, often timed to coincide with an expected salary raise. ( ② ) Because the commitment applies only to a point far in the future, the vast majority of employees readily agree to the plan. ( ③ ) When the scheduled date finally arrives, the increase takes effect automatically unless the employee actively opts out, and remarkably few do. ( ④ ) The program succeeds precisely because it separates the decision from the discomfort, asking employees to commit when the cost feels abstract rather than immediate. ( ⑤ ) In this way, a well-timed program can help individuals overcome the very bias that would otherwise prevent them from saving adequately for the future.

The software company, in turn, may argue that no algorithm can be expected to be perfect. Hospitals are increasingly adopting artificial intelligence tools that can scan medical images and flag potential tumors faster than experienced radiologists. ( ① ) When an AI system misses a diagnosis that a human doctor would likely have caught, responsibility becomes difficult to assign. ( ② ) The doctor may argue that they simply trusted a tool the hospital required them to use. ( ③ ) The hospital, meanwhile, may argue that the software company guaranteed its accuracy. ( ④ ) Until legal systems clarify this chain of responsibility, patients harmed by an algorithmic error may find themselves with no clear party to hold accountable. ( ⑤ )