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.

Adaptive learning apps promise something no traditional classroom can easily offer: a lesson plan ① that adjusts itself to each individual student in real time. As a student answers practice questions, the app tracks which concepts are mastered and which remain shaky, then quietly ② reshuffles the next set of problems to target the weaker areas. Early studies suggest that this personalized pacing can help struggling students ③ catch up faster than a one-size-fits-all worksheet ever could. However, critics caution that an app cannot replace the encouragement, humor, and flexibility a human teacher brings to a lesson, especially when a student ④ frustrating rather than simply behind. There is also a subtler concern: students who grow used to instant, algorithm-generated feedback may struggle later with the slower, more ambiguous kind of feedback that real-world tasks and mentors typically ⑤ provide.

After a viewer finishes an episode, a streaming service rarely ① leave the choice of what to watch next entirely up to chance. Instead, a recommendation algorithm compares that viewer's habits with patterns ② drawn from millions of other users who watched similar content, then ranks thousands of titles in order of predicted interest. 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 ③ refining it. 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, ④ creating a comfortable but repetitive bubble. Some platforms have begun inserting occasional wildcard suggestions, deliberately unrelated to past viewing habits, in an attempt ⑤ to counteract this narrowing effect.

Many companies now advertise themselves as environmentally responsible, yet a growing number of these claims turn out to be ①misleading. This practice, known as greenwashing, occurs when a business spends more effort marketing its products as eco-friendly than actually reducing environmental harm. A company might launch a recycled-packaging line while continuing to pollute rivers through its main factories, creating a false impression of genuine change. Such tactics ②deceive well-intentioned consumers who wish to shop responsibly but lack the tools to ③verify corporate claims. In contrast, truly sustainable businesses publish detailed, independently verified data about their emissions and supply chains, allowing outsiders to check their progress. Therefore, shoppers should look beyond vague labels like green or natural and instead search for ④recognized certifications, transparent reports, and measurable targets. Without such scrutiny, greenwashing will keep ⑤declining, undermining public trust and slowing the broader shift toward an economy that truly protects the planet.

Turning on automatic captions used to mean settling for garbled text full of obvious errors, but recent speech-recognition models have made same-language captions ① accurate enough that many viewers now leave them on by default, even when watching content in their native language. The technology listens to audio, breaks it into individual sounds, and matches those sounds against a massive library of words and phrases to predict what was actually said. For viewers who are deaf or hard of hearing, this improvement has quietly ② transformed everyday access to video content that once required a human transcriber and a considerable amount of time to caption properly. The system still ③ excels at overlapping speakers, strong accents, and specialized vocabulary such as medical or technical terms, occasionally producing captions that are unintentionally funny. Even with these ④ flaws, most users agree that imperfect captions are far more ⑤ useful than no captions at all.

When people discuss climate change, they often focus only on rising temperatures or melting ice, treating it as a purely scientific issue. However, the concept of climate justice reminds us that the causes and consequences of climate change are distributed ①**evenly** across the globe. Wealthy, industrialized nations have historically released the ②**largest** share of greenhouse gases while building their economies, yet developing nations, which contributed comparatively little, frequently suffer the most ③**severe** droughts and floods. This gap between responsibility and suffering raises a serious ④**ethical** question that science alone cannot answer. For example, a farming community in a low-income country may lose its entire harvest to an unusual flood, even though its members have never owned a car or flown on a plane. Therefore, tackling climate change requires more than new technology; it demands ⑤**fair** policies supporting the very communities least responsible for causing the crisis.

Smart speakers have quietly become a fixture in many households, responding to spoken requests for weather updates, music, or kitchen timers. Behind this ① simple exchange lies a complex process: the device converts sound waves into text, interprets the meaning using language models, and then generates a spoken reply, all within a second or two. Because these assistants ② improve by learning from millions of interactions, they gradually become better at recognizing regional accents and casual phrasing that once confused them. Still, the technology remains far from ③ perfect. Background noise, overlapping voices, or an unusual sentence structure can cause the assistant to misunderstand a simple request, sometimes with ④ flawless results. Engineers continue to refine these systems, but many admit that truly natural conversation remains one of the hardest ⑤ goals in the entire field of artificial intelligence.

Scientists have identified certain gene variants that appear more frequently among elite endurance athletes than among the general population, along with a separate set that seems to favor explosive, power-based sports such as sprinting. One frequently studied variant, for instance, influences the proportion of slow-twitch versus fast-twitch muscle fibers a person naturally develops, a ratio that shapes whether a body leans toward endurance or explosive power. However, researchers caution strongly against interpreting these findings as evidence that athletic greatness is simply inherited, since _______________. Genetics may set a rough ceiling on an individual's ultimate potential, but where any particular athlete lands within that range depends overwhelmingly on training quality, nutrition, coaching, and sheer determination.

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, saving users countless keystrokes over the course of a day. This constant personalization, however, creates a subtle side effect that few users notice: some linguists worry that _______________.

Imagine two factories making the same product: one in a country with strict carbon rules, and one in a country with almost none. (A) However, critics claim it unfairly burdens developing countries, which often lack money to modernize factories quickly. For example, a small exporter in a poorer nation might struggle to pay the added charge, though it contributes little to global emissions. Still, if wealthier nations pair the policy with technical support, it could become a genuinely fair tool for cutting emissions worldwide. (B) Supporters argue that the system protects domestic industries and pushes foreign manufacturers toward cleaner methods. (C) The factory facing stricter rules often spends more, which can make its goods pricier than those made elsewhere. To fix this imbalance, some governments have introduced the carbon border adjustment mechanism, a policy charging imported goods a fee based on how much carbon was released while producing them.