영어 모의고사 (자동 편성)

When summer vacation began, I decided to make a daily schedule so that I would not waste my free time. At first, my plan was too strict, with study sessions ①packed back to back and almost no breaks. After only three days, I felt exhausted and ②gave up on the schedule completely. My older sister suggested that I ③make a more realistic plan instead. I also learned to include short breaks between tasks, since resting actually ④helps me focus better afterward. Since making these changes, I ⑤has been able to follow my schedule much more consistently.

Online advertising algorithms analyze enormous amounts of user data ①to determine which products a person is most likely to purchase. Every click, search, and pause while scrolling provides information that these systems use ②to refine future advertisements. Supporters argue that this level of precision benefits consumers by ③reducing exposure to irrelevant advertisements and highlighting products they genuinely need. Critics, however, worry that constant exposure to tailored ads can encourage unnecessary spending, since algorithms ④is specifically designed to identify and exploit individual weaknesses. Greater transparency, many experts argue, would allow consumers ⑤to make more informed decisions about their own online behavior.

Imagine two factories making the same product: one in a country with ①strict carbon rules, and one in a country with almost none. The factory facing stricter rules often spends more, which can make its goods ②pricier 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. Supporters argue that the system ③protects domestic industries and pushes foreign manufacturers toward cleaner methods. However, critics claim it unfairly ④benefits 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 ⑤little to global emissions. Still, if wealthier nations pair the policy with technical support, it could become a genuinely fair tool for cutting emissions worldwide.

Consider a large heap containing ten thousand grains of sand. Nearly everyone agrees that removing a single grain from such a heap leaves another heap behind; surely one grain could never make the difference between a heap and a non-heap. Yet if this principle is applied repeatedly, grain by grain, the heap eventually ②grows to a single lonely grain, or even to no grains at all, and logic alone seems to force us to accept that this final result still counts as a heap. Something has clearly ①gone wrong along the way, since no reasonable person would call one grain of sand a heap, but pinpointing exactly which single removal transformed a heap into a non-heap seems ③impossible. The puzzle exposes a deep discomfort with vague terms like "heap," "tall," or "bald," concepts that seem to admit no sharp ④boundary yet are used confidently in everyday language. Some philosophers conclude that classical logic itself may need to be ⑤revised to accommodate the genuine vagueness built into ordinary human language.

Factories that once employed hundreds of workers on an assembly line now run with a ①fraction of that staff, as robotic arms handle tasks that used to require human hands. Economists have long debated whether this shift destroys jobs or simply transforms them. Historical evidence offers some ②comfort: previous waves of automation eventually created new categories of work that no one had predicted. However, critics point out that earlier transitions unfolded over generations, giving workers ③ample time to retrain, whereas today's robots are being deployed at a pace that leaves little room for adjustment. A factory worker displaced this year cannot simply wait for a new industry to emerge decades from now. For this reason, many economists argue that the real ④challenge is not the robots themselves, but the ⑤sluggish speed at which society is expected to adapt to them.

Generative AI systems learn to write, draw, and compose by studying ①enormous collections of existing human work, much of which was created without the artists' knowledge or consent. Supporters of the technology argue that this process resembles how a human student learns by studying the masters, ②absorbing style and technique rather than copying specific pieces. Critics respond that the comparison ③holds up perfectly at scale, since a single AI model can absorb millions of works in days. Courts around the world are now being asked to decide whether training an algorithm on copyrighted material counts as fair use or as ④infringement. Until clearer rules emerge, artists, developers, and publishers remain locked in a legal ⑤gray zone that satisfies almost no one.

A few years ago, spotting a manipulated video required a trained eye and ①specialized software. Today, freely available applications allow almost anyone to swap a face or fabricate a voice ②convincingly enough to fool casual viewers within minutes. This shift has quietly ③reinforced one of the oldest assumptions in human communication: that seeing and hearing something is a reasonably reliable way to confirm it happened. Journalists now face the burden of verifying footage that once would have been accepted as ④self-evident proof. Perhaps more troubling than any single fake video is the broader effect on public trust, in which genuine footage can be ⑤dismissed as fabricated simply because deepfakes exist.

Military researchers have developed weapons systems capable of identifying and engaging targets without a human pressing the final button. Advocates argue that autonomous systems can react faster than any human operator. Human rights organizations counter that removing a person from the final decision strips away moral responsibility, since _______________. Diplomatic negotiations toward an international treaty have repeatedly stalled, partly because military powers disagree on how to define autonomy precisely enough to regulate it.

The eventual collapse of a speculative bubble often unfolds with surprising speed compared with the gradual climb that preceded it. (A) As prices begin to fall, investors who bought near the peak, motivated largely by fear of missing further gains, are often the first to sell, since they have the least conviction in the asset's underlying value. (B) Their selling triggers further price declines, which in turn prompt additional investors to sell out of a newly urgent fear of continued loss rather than a fear of missing out. (C) This reversal illustrates that the same herd psychology driving prices upward can just as easily reverse direction, converting collective optimism into collective panic within a remarkably short period.

Critics note a further irony: when something goes wrong, such as a hacked exchange or a lost password, there is often no institution left to appeal to. Blockchain technology was originally promoted as a way to remove the need for trusted middlemen, allowing strangers to exchange value directly and verify transactions through a shared, tamper-resistant ledger. ( ① ) In theory, this design distributes power away from any single institution and toward the network as a whole. ( ② ) In practice, however, a small number of large players have come to dominate many blockchain networks. ( ③ ) They control enough computing power or currency to influence outcomes that were supposed to be decided collectively. ( ④ ) Whether decentralization can survive this concentration of power remains one of the technology's most unresolved questions. ( ⑤ )