영어 모의고사 (자동 편성)

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.

Internet activist Eli Pariser introduced the term filter bubble to describe a phenomenon ①created by online algorithms. Social media platforms and search engines constantly track which links users click and how long they linger on certain posts. Based on this data, algorithms predict what each individual is most likely ②to enjoy and quietly prioritize similar content in future results. As a result, two people who type the same search query can receive strikingly different results, each ③tailoring to personal history. This automatic filtering happens silently, without users ever ④deciding to limit what they see. Consequently, many people gradually lose access to viewpoints ⑤that challenge their existing beliefs, even though they never intended to avoid them.

Remote work has moved from a rare privilege to a common practice for millions of employees worldwide. Many companies that once required workers ①to commute daily now allow them to complete tasks from home. Supporters argue that remote work increases productivity, since employees avoid long commutes and ②distracting office environments. It also allows companies to hire talented people regardless of where they live, ③widening the pool of candidates. Critics, however, worry that remote work weakens teamwork and ④make it harder for new employees to learn from experienced colleagues. Without regular face-to-face interaction, some workers feel isolated and ⑤struggle to build strong professional relationships.

Artificial intelligence has begun to play a surprising role in creative industries ①once thought to be uniquely human. Programs can now compose music, generate paintings, and ②draft short stories within seconds. Some artists welcome this technology, ③used AI tools to explore new styles or overcome creative blocks that once took weeks to resolve. Others, however, argue that AI-generated art lacks the emotional depth ④that comes from genuine human experience. Therefore, artists who learn to work alongside AI, rather than compete against it, may find themselves ⑤with a distinct advantage.

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.

As artificial intelligence grows more capable of performing tasks once considered exclusively human, many people wonder which skills will remain valuable in the future. AI can now analyze data, generate text, and even diagnose certain medical conditions with ①impressive accuracy. However, most researchers argue that AI still ②struggles with tasks requiring genuine empathy, ethical judgment, and creative problem-solving in unfamiliar situations. A machine can recommend a treatment plan based on statistics, for instance, but it can ③easily comfort a frightened patient the way a compassionate nurse can. Similarly, AI can generate countless design variations, yet it ④lacks the lived experience that allows humans to judge which idea truly resonates with people. For this reason, educators increasingly emphasize skills such as collaboration and emotional intelligence, arguing that these human capabilities will become more ⑤valuable as automation spreads.

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, 3D printers can create ①simple objects directly from digital files within hours. This flexibility allows companies to produce small batches of customized products without the massive costs typically associated with mass production. Hospitals now use 3D printing to create prosthetic limbs tailored ②precisely to individual patients, while automakers use it to build lightweight parts that ③reduce vehicle weight. Small businesses, too, have benefited enormously, since the technology ④lowers the barrier to entry for entrepreneurs. However, experts caution that 3D printing still struggles to ⑤match the speed of mass production for extremely large orders.

Automation is transforming labor markets more ①quickly than economists predicted a decade ago. Factories once staffed by hundreds of workers now rely on robotic arms that assemble products with remarkable ②precision. This shift does not simply eliminate jobs; it also creates roles that never existed before, so technicians who maintain robotic equipment are now in high ③demand. Companies that invested early in automation often report steady increases in productivity, since machines handle repetitive tasks while employees focus on ④creative problem-solving. Without proper retraining support, the benefits of automation may not be shared fairly, ⑤narrowing inequality between skilled and unskilled labor.

Not all obstacles to renewable energy are technical or financial; some are psychological. (A) In contrast, switching demands research, upfront cost, and trust in unfamiliar technology, all of which can seem risky compared with doing nothing. For example, a neighborhood association might repeatedly postpone a vote on shared solar panels, not because the plan is flawed, but because members are reluctant to leave their routine. Therefore, boosting renewable adoption may depend less on proving its advantages and more on designing programs that make the first step feel easy. (B) Behavioral economists use the term status quo bias to describe a tendency to prefer the present situation over a new alternative, even when the alternative would bring greater benefits. (C) This bias helps explain why many households hesitate to switch to renewable energy, such as installing solar panels on their roofs. Although solar panels can lower electricity bills over time, homeowners often stick with their current power source because it feels safe and requires no immediate effort.

The classification of platform workers has become one of the most debated issues in modern labor law. (A) For this reason, companies generally prefer classifying these workers as independent contractors rather than full employees. (B) Being classified this way, however, means workers miss out on protections such as minimum wage and paid leave. (C) Employee status requires companies to provide benefits and follow strict labor regulations, which increases operating costs.