영어 모의고사 (자동 편성)

Many people struggling to fall asleep in noisy environments turn to white noise machines, devices that ① emit a steady, uniform sound covering a broad range of frequencies. The appeal lies in a phenomenon called auditory masking, in which a constant background sound makes sudden noises ② less noticeable to the sleeping brain. Without this masking effect, an abrupt change in sound is far more likely ③ to trigger a brief awakening, even if the sleeper does not consciously remember it the next morning. Studies comparing sleep in quiet rooms with sleep alongside white noise have found that participants using white noise ④ experienced fewer of these brief awakenings. However, researchers caution that the benefit depends heavily on volume and consistency, since noise that is too loud or that ⑤ varying unpredictably can itself become a source of disruption rather than a remedy.

A smart thermostat that learns a family's daily rhythm can lower the temperature automatically before everyone wakes up cold, while smart lights ① that follow a resident's schedule no longer need to be switched on and off by hand. These small conveniences rest on a shared foundation: sensors collect data about temperature, motion, and light throughout the home, and an algorithm gradually learns ② which settings match each household's actual habits rather than a factory default. Over months of use, the system becomes noticeably better at ③ anticipate needs, often adjusting settings moments before a resident would have reached for a switch. Yet convenience comes with a trade-off that many households ④ overlook. Every one of these small adjustments depends on constant data collection, meaning that a detailed record of when people wake, leave, and return home is quietly ⑤ being stored somewhere beyond their immediate control.

Modern factories increasingly rely on collaborative robots, often called cobots, ①which are designed to work safely alongside human employees. Unlike traditional industrial robots that operate behind safety cages, cobots use sensors ②detect nearby workers and adjust their movements accordingly. In practice, cobots typically handle repetitive or physically demanding tasks, such as ③lifting heavy parts, while human workers focus on tasks that require judgment. This division of labor increases overall efficiency, since each side ④contributes what it does best. Manufacturers who have introduced cobots report fewer workplace injuries, largely because employees no longer ⑤perform exhausting repetitive motions all day.

Among strategies for improving concentration, the Pomodoro Technique remains one of the most widely ①recommended methods for students and workers. This technique involves working with full attention for twenty-five minutes, then ②taking a short five-minute break before starting the next session. After completing four of these cycles, a longer break of fifteen to thirty minutes ③is usually taken to allow mental recovery. The method assumes that sustained focus becomes harder to maintain as time passes, so frequent short breaks prevent mental fatigue from ④build 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 manageable. Because each interval has a clear start and end, procrastination becomes less tempting, since beginning a single twenty-five-minute session requires less commitment ⑤than beginning an entire project.

Artificial intelligence has begun to play a surprising role in creative industries once thought to be uniquely human. Some artists ①welcome this technology, using AI tools to explore new styles or overcome creative blocks that once took weeks to resolve. Others, however, argue that AI-generated art ②lacks the emotional depth that comes from genuine human experience. Despite this debate, most experts agree that AI is unlikely to fully ③replace artists anytime soon. Instead, it functions best as a collaborative tool, helping creators experiment ④faster while leaving final artistic decisions to humans. Therefore, artists who learn to work alongside AI may find themselves with a distinct ⑤disadvantage in this rapidly changing industry.

Internet activist Eli Pariser introduced the term filter bubble to describe a phenomenon created 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 to 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 tailored to personal history. This automatic filtering happens silently, without users ever deciding to limit what they see. Consequently, many people gradually lose access to viewpoints that challenge their existing beliefs, even though they never intended to avoid them. Pariser warned that this invisible curation could weaken democratic debate by narrowing public discussion. Therefore, some experts argue that platforms should reveal how content is filtered, _______________.

주어진 문장: Once audiences discover that a recommendation was insincere, they rarely trust that influencer again. Influencer marketing has become one of the most powerful tools that companies use to reach young consumers. ( ① ) Unlike traditional advertisements, which often feel distant and impersonal, posts created by influencers seem more like advice from a trusted friend. ( ② ) This sense of closeness, built through years of sharing daily life online, makes followers more willing to trust product recommendations than ordinary commercials. ( ③ ) However, this trust can be exploited when influencers promote products they have never actually tried. ( ④ ) As a result, several countries now require influencers to clearly disclose paid partnerships before posting sponsored content. ( ⑤ ) In the end, influencer marketing works best when it stays grounded in authenticity rather than mere popularity.

주어진 문장: This gap between old and new skill sets can make the transition especially challenging for older workers. As governments and companies work to address climate change, a new category of employment known as green jobs has expanded rapidly. ( ① ) These positions include solar panel installers, wind turbine technicians, and specialists who design energy-efficient buildings. ( ② ) Critics point out that transitioning workers from fossil fuel industries to green jobs is not always simple, since the required skills can differ significantly. ( ③ ) Governments are therefore introducing training programs designed to help such workers shift smoothly into renewable energy careers. ( ④ ) Countries that have invested heavily in renewable energy have seen employment in this sector grow far faster than in the economy overall. ( ⑤ ) As climate goals become more urgent worldwide, demand for skilled workers in this expanding field is expected to continue rising steadily.

Because the keyboard nudges people toward its own suggestions, some linguists worry that autocomplete may be quietly narrowing everyday vocabulary. Long before a user finishes typing a sentence, the keyboard on most smartphones has already begun guessing what word comes next. ( ① ) 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, saving users countless keystrokes over the course of a day. ( ② ) 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 recognize. ( ③ ) This constant personalization, however, creates a subtle side effect that few users notice. ( ④ ) This small habit, choosing a convenient suggestion rather than searching for the more precise or original word one might otherwise have chosen, can add up over years of daily use. ( ⑤ )

In response, some platforms have begun inserting occasional wildcard suggestions that are deliberately unrelated to past viewing habits. After a viewer finishes an episode, a streaming service rarely leaves the choice of what to watch next entirely up to chance. ( ① ) Instead, a recommendation algorithm compares that viewer's habits with patterns drawn from millions of other users who watched similar content, then ranks thousands of titles in order of predicted interest. ( ② ) This approach keeps viewers engaged far longer than a simple list of new releases ever could, which is precisely why streaming companies invest so heavily in refining it. ( ③ ) The system is not without drawbacks, however. Because the algorithm favors titles similar to what a viewer already enjoys, it can quietly narrow someone's exposure to unfamiliar genres, directors, or cultures, creating a comfortable but repetitive bubble. ( ④ ) These wildcard picks are meant to counteract this narrowing effect and keep recommendations from becoming too predictable. ( ⑤ )