IELTS Reading: Công Nghệ Tăng Cường Ứng Phó Thảm Họa – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Chủ đề “How Is Technology Enhancing Disaster Preparedness And Response?” (Công nghệ đang cải thiện sự chuẩn bị và ứng phó với thảm họa như thế nào?) là một trong những đề tài khoa học – xã hội phổ biến trong IELTS Reading. Với sự gia tăng của các thảm họa tự nhiên và vai trò ngày càng quan trọng của công nghệ, chủ đề này xuất hiện thường xuyên trong các kỳ thi IELTS từ năm 2018 đến nay, đặc biệt trong các đề thi Cambridge IELTS 15-19.

Trong bài viết này, bạn sẽ được luyện tập với một đề thi IELTS Reading hoàn chỉnh gồm 3 passages từ dễ đến khó, bao gồm 40 câu hỏi đa dạng giống thi thật. Mỗi passage được thiết kế chuyên biệt cho các band điểm khác nhau: Passage 1 (Band 5.0-6.5), Passage 2 (Band 6.0-7.5), và Passage 3 (Band 7.0-9.0). Bên cạnh đề thi, bạn sẽ nhận được đáp án chi tiết kèm giải thích, từ vựng quan trọng với phiên âm và ví dụ, cùng các chiến lược làm bài hiệu quả.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với các dạng câu hỏi phổ biến như True/False/Not Given, Matching Headings, Summary Completion, và Multiple Choice. Hãy dành 60 phút để hoàn thành bài test này như điều kiện thi thật để đánh giá chính xác trình độ hiện tại của bạn.

Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading là phần thi kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Độ khó tăng dần từ Passage 1 đến Passage 3, tương ứng với việc yêu cầu kỹ năng đọc hiểu từ cơ bản đến nâng cao. Mỗi câu trả lời đúng được tính 1 điểm, không có điểm âm cho câu sai.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (13 câu hỏi) – Nội dung dễ hiểu, từ vựng cơ bản
• Passage 2: 18-20 phút (13 câu hỏi) – Nội dung phức tạp hơn, yêu cầu suy luận
• Passage 3: 23-25 phút (14 câu hỏi) – Nội dung học thuật, từ vựng chuyên sâu

Lưu ý: Không có thời gian bổ sung để chuyển đáp án, vì vậy hãy viết đáp án trực tiếp vào phiếu trả lời trong 60 phút.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:

1. Multiple Choice – Câu hỏi trắc nghiệm 4 phương án
2. True/False/Not Given – Xác định thông tin đúng/sai/không được nhắc đến
3. Matching Headings – Nối tiêu đề với đoạn văn
4. Summary Completion – Hoàn thành đoạn tóm tắt
5. Sentence Completion – Hoàn thành câu
6. Matching Features – Nối đặc điểm với đối tượng
7. Short-answer Questions – Câu hỏi trả lời ngắn

IELTS Reading Practice Test

PASSAGE 1 – Early Warning Systems: Technology’s First Line of Defense

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

When natural disasters strike, every second counts. The difference between life and death often depends on how quickly people receive warnings and take protective action. Over the past two decades, technology has revolutionized the way we detect, monitor, and communicate information about impending disasters such as earthquakes, tsunamis, hurricanes, and floods.

Early warning systems represent one of the most significant technological advancements in disaster preparedness. These systems use a combination of sensors, satellites, and communication networks to detect potential threats and alert communities at risk. For example, seismic sensors placed along fault lines can detect the initial waves of an earthquake and send warnings to populated areas before the more destructive waves arrive. Although this window may be only seconds or minutes, it provides crucial time for people to take cover or evacuate.

Tsunami warning systems operate on a similar principle but on a much larger scale. After the devastating 2004 Indian Ocean tsunami, which killed more than 230,000 people, the international community invested heavily in ocean monitoring technology. Today, a network of buoys equipped with pressure sensors can detect sudden changes in water levels caused by underwater earthquakes. When a potential tsunami is identified, warnings are transmitted via satellite to coastal communities, often providing hours of advance notice. This system has already saved thousands of lives in subsequent events.

Weather monitoring technology has also become increasingly sophisticated. Meteorological satellites now provide real-time data about atmospheric conditions, allowing scientists to track developing storms with unprecedented accuracy. For hurricanes and typhoons, forecasters can predict the storm’s path, intensity, and likely impact zone days in advance. This information enables authorities to order timely evacuations and helps residents prepare their homes and emergency supplies.

Mobile technology has transformed how warnings reach the public. In many countries, government agencies can now send emergency alerts directly to cell phones in affected areas through SMS messages or dedicated apps. These location-based alerts ensure that people receive relevant information about threats in their immediate vicinity. Japan’s earthquake early warning system, for instance, sends alerts to millions of phones simultaneously when seismic activity is detected, giving people precious seconds to seek safety.

Social media platforms have emerged as powerful tools for disaster communication. During emergency situations, official accounts can rapidly disseminate information about evacuation routes, shelter locations, and safety instructions to large audiences. Citizens also use social media to share real-time updates about conditions in their areas, creating a crowdsourced information network that complements official channels. However, this also presents challenges, as misinformation can spread quickly during chaotic situations.

Radio and television broadcasting remain essential components of warning systems, particularly in areas with limited internet access. Many countries require that broadcast stations participate in emergency alert systems that can interrupt regular programming to deliver urgent warnings. Battery-powered or hand-crank radios ensure that people can receive information even when electricity networks fail.

Despite these technological advances, challenges remain in ensuring that warnings reach vulnerable populations. Rural communities with limited infrastructure, elderly individuals unfamiliar with modern technology, and people with disabilities may not receive or understand warnings in time. Effective disaster preparedness therefore requires not just advanced technology but also community education, regular drills, and inclusive communication strategies that reach all segments of society.

The future of early warning systems lies in artificial intelligence and machine learning. These technologies can analyze vast amounts of data from multiple sources to identify patterns and predict disasters with even greater accuracy. Some systems are now being developed to provide personalized warnings based on an individual’s location and vulnerability factors, making alerts more actionable and effective.

Questions 1-13

Questions 1-4

Choose the correct letter, A, B, C, or D.

1. According to the passage, early warning systems primarily use:
A. Only satellite technology
B. A combination of sensors, satellites, and communication networks
C. Social media platforms exclusively
D. Traditional radio broadcasting

2. The 2004 Indian Ocean tsunami led to:
A. The development of the first early warning system
B. Reduced investment in ocean monitoring
C. Significant investment in ocean monitoring technology
D. The closure of coastal communities

3. Mobile technology has transformed disaster warnings by:
A. Replacing all other forms of communication
B. Sending location-based alerts to affected areas
C. Eliminating the need for evacuation
D. Reducing the cost of emergency services

4. What challenge does the passage identify with social media during disasters?
A. Too few people use it
B. It is too expensive
C. Misinformation can spread quickly
D. It only works in urban areas

Questions 5-9

Do the following statements agree with the information given in the passage?

Write:

• TRUE if the statement agrees with the information
• FALSE if the statement contradicts the information
• NOT GIVEN if there is no information on this

5. Seismic sensors can provide several hours of warning before an earthquake strikes.

6. Modern meteorological satellites can track developing storms with greater accuracy than before.

7. All countries use the same emergency alert system for mobile phones.

8. Radio broadcasting is no longer necessary due to internet technology.

9. Artificial intelligence may improve the accuracy of disaster prediction.

Questions 10-13

Complete the sentences below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

10. Tsunami warning systems use __ equipped with pressure sensors to detect changes in water levels.

11. Japan’s earthquake early warning system can send alerts to __ of phones at the same time.

12. People with __ may not receive or understand warnings in time.

13. Future warning systems may provide __ warnings based on individual circumstances.

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PASSAGE 2 – Drones and Robots: Technology on the Front Lines

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The deployment of unmanned aerial vehicles (UAVs), commonly known as drones, and robotic systems has fundamentally altered the landscape of disaster response operations. These technologies enable emergency responders to gather critical information, deliver supplies, and conduct search and rescue missions in environments that would be too dangerous or inaccessible for human teams. As drone technology becomes more sophisticated and affordable, its applications in humanitarian crises continue to expand.

Aerial reconnaissance represents one of the most valuable contributions drones make to disaster response. In the immediate aftermath of earthquakes, floods, or hurricanes, infrastructure damage often makes traditional assessment methods impossible. Roads may be destroyed, bridges collapsed, and entire areas submerged or blocked by debris. Drones equipped with high-resolution cameras and thermal imaging sensors can quickly survey affected regions, providing incident commanders with comprehensive overviews of damage extent and severity. This aerial perspective allows authorities to prioritize response efforts, identify areas with the greatest need, and plan logistical operations more effectively.

Thermal imaging technology proves particularly valuable in search and rescue operations. After building collapses or landslides, survivors may be trapped beneath rubble, invisible to conventional search methods. Drones equipped with thermal cameras can detect heat signatures from human bodies, even when buried under debris. This capability dramatically reduces search times and increases the likelihood of finding survivors during the critical window when rescue attempts have the highest probability of success. Following the 2015 Nepal earthquake, drone teams helped locate survivors in remote mountain villages that ground-based rescuers could not reach for days.

Beyond reconnaissance, drones serve as delivery platforms for essential supplies. In situations where roads are impassable and helicopter access is limited, drones can transport medical supplies, water purification tablets, communication equipment, and other lightweight but critical items to isolated communities. Some organizations are developing larger cargo drones capable of carrying substantial payloads, potentially revolutionizing the logistics of humanitarian aid delivery. In remote or disaster-stricken areas, a drone can complete in minutes what might take ground convoys hours or days.

Robotic systems designed for ground operations complement aerial drones in disaster environments. Specialized robots can enter unstable structures, navigate through narrow passages, and operate in conditions with toxic gases or radiation that would endanger human rescuers. These machines often carry cameras, microphones, and other sensors that allow operators to assess situations and even communicate with trapped individuals. Some robots are equipped with manipulator arms that can move debris, deliver supplies, or attach rescue equipment.

The Snake Robot, developed by researchers at Carnegie Mellon University, exemplifies this technology’s potential. Its flexible, modular design allows it to move through rubble-filled spaces too small or dangerous for humans. Equipped with cameras and sensors, it can create three-dimensional maps of disaster sites while searching for survivors. Similar technologies have been deployed in mine collapses, train derailments, and building failures, providing invaluable intelligence that informs rescue strategies.

Communication infrastructure often suffers severe damage during disasters, leaving affected populations unable to call for help or receive information. Emergency communications drones can provide temporary mobile phone or internet connectivity by acting as aerial cell towers. These systems, sometimes called “flying COWs” (Cell on Wings), can restore basic communication services within hours, enabling both coordination of relief efforts and allowing victims to contact loved ones. Following Hurricane Maria‘s devastation of Puerto Rico in 2017, telecommunications companies deployed drones to restore connectivity while permanent infrastructure was being repaired.

Water rescue operations have been transformed by specialized aquatic drones. These devices can reach drowning victims faster than human swimmers or boats, delivering flotation devices while professional rescuers make their way to the scene. Some models include underwater cameras for search operations in murky water conditions. Autonomous underwater vehicles (AUVs) assist in post-disaster assessments of submerged infrastructure like bridges and port facilities, work that would otherwise require skilled divers to perform in potentially dangerous conditions.

Despite these impressive capabilities, drone and robot deployment faces significant challenges. Regulatory frameworks governing airspace usage during emergencies vary by country and can complicate rapid deployment. Battery life limits operational duration, though advances in power technology continue to extend flight times. Operator training remains essential; effectively piloting drones in complex disaster environments requires considerable skill and experience. Furthermore, coordination between multiple drone operators and traditional response teams demands careful planning to prevent interference and ensure optimal resource allocation.

Privacy concerns also arise when surveillance technology is deployed in populated areas, even for humanitarian purposes. Establishing clear protocols about data collection, storage, and usage helps maintain public trust while allowing responders to leverage these powerful tools. Some jurisdictions have developed specific guidelines for disaster-related drone operations that balance operational needs with individual rights.

Looking forward, artificial intelligence integration promises to enhance drone and robot effectiveness further. Machine learning algorithms can enable autonomous navigation, automatic identification of hazards or survivors, and intelligent analysis of collected data. Swarm technology, where multiple drones coordinate their actions, could allow comprehensive coverage of large disaster zones with minimal human intervention. As these technologies mature, they will likely become standard equipment for emergency response organizations worldwide.

Questions 14-26

Questions 14-18

Choose the correct letter, A, B, C, or D.

14. According to the passage, drones are particularly useful after disasters because:
A. They are inexpensive to operate
B. They can access areas dangerous or impossible for humans
C. They eliminate the need for human responders
D. They work without any operator training

15. Thermal imaging technology helps in rescue operations by:
A. Providing light in dark environments
B. Melting ice and snow
C. Detecting heat signatures from trapped people
D. Communicating with survivors

16. The Snake Robot is characterized by its:
A. High speed across open ground
B. Ability to fly short distances
C. Flexible, modular design for tight spaces
D. Capacity to carry heavy loads

17. “Flying COWs” are used to:
A. Deliver food supplies
B. Provide temporary communication services
C. Monitor weather conditions
D. Transport injured victims

18. The passage suggests that regulatory frameworks for drone use:
A. Are identical worldwide
B. Do not exist yet
C. Vary by country and can complicate deployment
D. Have completely solved all problems

Questions 19-23

The passage describes several types of drone and robot applications. Match each technology with its primary function.

Choose your answers from the box below and write the letters A-H next to questions 19-23.

Functions:
A. Entering unstable structures
B. Restoring mobile phone connectivity
C. Detecting heat from trapped people
D. Delivering flotation devices to drowning victims
E. Transporting medical supplies to isolated areas
F. Predicting future disasters
G. Removing large debris
H. Creating three-dimensional maps

19. Delivery drones __

20. Thermal imaging drones __

21. Emergency communications drones __

22. Specialized robots with manipulator arms __

23. Aquatic rescue drones __

Questions 24-26

Complete the summary below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

Despite their advantages, drones face several challenges. The 24. __ limits how long drones can operate, though technology continues to improve. Effective use requires 25. __ because piloting in complex environments is difficult. Additionally, 26. __ must be addressed when surveillance technology is used in areas where people live.

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PASSAGE 3 – Big Data and Predictive Analytics: Anticipating the Unpredictable

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The convergence of big data analytics, artificial intelligence, and ubiquitous sensor networks is ushering in a new paradigm for disaster risk reduction—one characterized by anticipatory rather than merely reactive strategies. This transformation represents a fundamental epistemological shift in how humanity approaches the seemingly chaotic nature of catastrophic events, moving from post-hoc response to probabilistic forecasting and preemptive intervention. The implications of this shift extend far beyond technical capabilities, touching upon questions of resource allocation, ethical responsibility, and the very definition of what constitutes effective governance in an era of increasing environmental volatility.

Predictive analytics in disaster management harnesses computational power to process heterogeneous data streams from sources as diverse as satellite imagery, social media activity, seismological monitors, meteorological stations, infrastructure sensors, and historical disaster databases. Machine learning algorithms identify subtle patterns and correlations that escape human observation, generating probabilistic models of when and where disasters might occur. These models transcend simple extrapolation from past events; they incorporate dynamic variables including climate change trajectories, urbanization patterns, ecological degradation, and socioeconomic vulnerabilities that modify disaster risk in complex, non-linear ways.

The granularity and temporal resolution of modern predictive systems represent qualitative advances over previous generations of forecasting. Consider flood prediction: traditional models relied primarily on river gauge measurements and rainfall data, providing warnings with relatively coarse spatial resolution and limited lead times. Contemporary systems integrate real-time precipitation data from weather radar networks, soil moisture measurements from distributed sensors, topographical information from high-resolution elevation models, land use data from satellite imagery, and even social media posts that may indicate localized flooding. Advanced algorithms process this multifaceted information to generate hyperlocal flood predictions with unprecedented accuracy, sometimes identifying risk at the individual building level hours or even days before inundation occurs.

This predictive capacity enables targeted interventions that can dramatically reduce disaster impacts. When models indicate elevated risk for specific locations, authorities can preposition emergency resources, implement temporary protective measures, and conduct focused evacuations of high-risk areas while allowing normal activities to continue elsewhere. Such precision in response planning optimizes resource utilization—a critical consideration given the perpetual constraints facing disaster management budgets. Moreover, anticipatory action based on predictive data can prevent disasters from occurring in the first place or at least mitigate their severity. For instance, forecasts of extreme heat events allow public health authorities to activate cooling centers, conduct wellness checks on vulnerable individuals, and disseminate preventive health guidance before casualties occur.

The integration of social media data into predictive frameworks exemplifies how unconventional information sources can enhance disaster intelligence. During crises, people often share information about emerging threats, damage, or needs through social platforms before official channels receive reports. Natural language processing algorithms can analyze millions of posts in real time, identifying geographically specific information about disaster conditions. This “social sensing” provides situational awareness that complements physical sensor networks, particularly in areas with sparse instrumental coverage. However, this approach raises methodological challenges: determining signal from noise in vast streams of unstructured text, accounting for demographic biases in social media usage, and validating crowdsourced information all require sophisticated analytical techniques.

Artificial intelligence applications in disaster prediction are evolving beyond pattern recognition toward more nuanced understanding of causal mechanisms. Deep learning models trained on extensive historical datasets can identify precursor conditions for events like landslides, wildfires, or disease outbreaks with remarkable accuracy. Some systems employ “explainable AI” approaches that not only generate predictions but also articulate the reasoning behind their forecasts, enabling human experts to evaluate the model’s logic and integrate domain knowledge that may not be captured in training data. This collaborative intelligence—combining algorithmic processing power with human judgment—appears more robust than either approach alone.

Ethical considerations permeate the application of predictive analytics in disaster management. Probabilistic forecasts are inherently uncertain; acting on predictions that ultimately prove incorrect can incur substantial costs through unnecessary evacuations, economic disruptions, or erosion of public trust. Conversely, failing to act on predictions that materialize into disasters can result in preventable casualties. This dilemma is exacerbated by the asymmetric distribution of disaster impacts: vulnerable populations—often marginalized communities with limited political influence—typically suffer disproportionately from disasters yet may have minimal input into decisions about predictive system development or resource allocation based on forecasts.

Data privacy concerns also warrant careful attention. Comprehensive risk assessment often requires granular information about individuals and households, including location data, health status, economic circumstances, and social connections. While such information enables personalized warnings and targeted assistance, it also creates vulnerabilities to surveillance, discrimination, or exploitation. Establishing robust governance frameworks that safeguard privacy while enabling beneficial applications represents an ongoing challenge for policymakers and technologists.

The prospect of reliable disaster prediction raises profound questions about responsibility and preparedness. If advanced warning systems can forecast disasters with reasonable certainty, does failure to adequately prepare or respond constitute a form of culpable negligence? Some legal scholars argue that predictive capabilities create enhanced obligations for governments to protect citizens, potentially establishing grounds for liability when foreseeable disasters overwhelm inadequate preparedness measures. This emerging jurisprudence could incentivize greater investment in risk reduction but might also create perverse incentives to downplay or suppress unfavorable predictions.

Climate change adds another layer of complexity to predictive efforts. Many disaster forecasting models rely on assumptions of stationarity—the idea that future patterns will resemble historical precedents. However, anthropogenic climate change is fundamentally altering the baseline conditions that shape disaster risk. Extreme weather events are becoming more frequent and severe; sea level rise is expanding flood-prone areas; changing precipitation patterns affect drought and wildfire risk. Predictive systems must therefore incorporate climate projections and continuously adapt as observed changes deviate from historical norms. This non-stationary environment challenges traditional modeling approaches and demands flexible, adaptive systems capable of learning from emerging patterns.

Despite these complexities, the trajectory is clear: data-driven predictive approaches are becoming central to disaster risk management strategies globally. International frameworks like the Sendai Framework for Disaster Risk Reduction explicitly emphasize the importance of risk knowledge and early warning systems. As computational capabilities expand, sensor networks proliferate, and analytical techniques mature, the vision of accurately anticipating most disaster events appears increasingly attainable. The critical question is not whether technology can enhance predictive capacity—it demonstrably can—but rather how societies will navigate the ethical, political, and practical challenges of translating predictions into effective action that equitably protects all community members.

Questions 27-40

Questions 27-31

Choose the correct letter, A, B, C, or D.

27. According to the passage, the new paradigm in disaster management is characterized by:
A. Increased spending on emergency services
B. Moving from reactive to anticipatory strategies
C. Eliminating all natural disasters
D. Reducing the role of technology

28. Modern flood prediction systems differ from traditional systems by:
A. Using only historical data
B. Requiring less computational power
C. Integrating multiple data sources for hyperlocal predictions
D. Providing less accurate forecasts

29. The passage suggests that social media data in disaster prediction:
A. Is completely unreliable
B. Replaces all other data sources
C. Requires sophisticated techniques to validate
D. Is only useful after disasters occur

30. “Explainable AI” in disaster prediction:
A. Only makes predictions without reasoning
B. Articulates the reasoning behind its forecasts
C. Cannot be evaluated by human experts
D. Is less accurate than other AI systems

31. The passage indicates that climate change affects disaster prediction by:
A. Making all predictions impossible
B. Having no impact on forecasting models
C. Challenging assumptions of stationarity in models
D. Simplifying the prediction process

Questions 32-36

Do the following statements agree with the claims of the writer in the passage?

Write:

• YES if the statement agrees with the claims of the writer
• NO if the statement contradicts the claims of the writer
• NOT GIVEN if it is impossible to say what the writer thinks about this

32. Predictive analytics can identify risk at the individual building level for floods.

33. All countries have adopted the same ethical standards for using predictive disaster technology.

34. Vulnerable populations are usually the most heavily consulted in developing predictive systems.

35. Predictive capabilities may create new legal obligations for governments.

36. The Sendai Framework for Disaster Risk Reduction emphasizes the importance of early warning systems.

Questions 37-40

Complete each sentence with the correct ending, A-H, below.

37. Machine learning algorithms in disaster prediction

38. Anticipatory action based on predictive data

39. The integration of social media data

40. Probabilistic forecasts

Sentence endings:
A. are always completely accurate and require no human oversight.
B. identify patterns and correlations that humans might miss.
C. eliminate the need for traditional emergency response teams.
D. can prevent disasters from occurring or mitigate their severity.
E. provides situational awareness that complements physical sensors.
F. are inherently uncertain and involve difficult decisions about action.
G. replaces the need for satellite imagery entirely.
H. have no relevance to modern disaster management systems.

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Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. B
4. C
5. FALSE
6. TRUE
7. NOT GIVEN
8. FALSE
9. TRUE
10. buoys
11. millions
12. disabilities
13. personalized

PASSAGE 2: Questions 14-26

14. B
15. C
16. C
17. B
18. C
19. E
20. C
21. B
22. A (hoặc G – cả hai đều chấp nhận được)
23. D
24. battery life
25. operator training
26. privacy concerns

PASSAGE 3: Questions 27-40

27. B
28. C
29. C
30. B
31. C
32. YES
33. NOT GIVEN
34. NO
35. YES
36. YES
37. B
38. D
39. E
40. F

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Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: early warning systems, primarily use
• Vị trí trong bài: Đoạn 2, dòng 1-2
• Giải thích: Bài văn nói rõ “These systems use a combination of sensors, satellites, and communication networks” – hệ thống sử dụng sự kết hợp của các cảm biến, vệ tinh và mạng lưới truyền thông. Đáp án A sai vì không chỉ dùng vệ tinh, C và D sai vì không phải là công cụ duy nhất.

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: 2004 Indian Ocean tsunami, led to
• Vị trí trong bài: Đoạn 3, dòng 2-3
• Giải thích: Bài văn đề cập “the international community invested heavily in ocean monitoring technology” sau thảm họa 2004. Đây là paraphrase của “significant investment in ocean monitoring technology”.

Câu 3: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: mobile technology, transformed
• Vị trí trong bài: Đoạn 5, dòng 2-3
• Giải thích: “These location-based alerts ensure that people receive relevant information about threats in their immediate vicinity” – công nghệ di động gửi cảnh báo dựa trên vị trí đến khu vực bị ảnh hưởng.

Câu 4: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: challenge, social media
• Vị trí trong bài: Đoạn 6, dòng cuối
• Giải thích: Bài văn nói rõ “this also presents challenges, as misinformation can spread quickly during chaotic situations” – thông tin sai lệch có thể lan truyền nhanh.

Câu 5: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: seismic sensors, several hours warning
• Vị trí trong bài: Đoạn 2, dòng 4-6
• Giải thích: Bài văn nói “this window may be only seconds or minutes” – chỉ vài giây hoặc phút, không phải vài giờ. Câu phát biểu mâu thuẫn với thông tin.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: meteorological satellites, track storms, greater accuracy
• Vị trí trong bài: Đoạn 4, dòng 2-3
• Giải thích: “allowing scientists to track developing storms with unprecedented accuracy” – độ chính xác chưa từng có nghĩa là chính xác hơn trước đây.

Câu 7: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: all countries, same emergency alert system
• Vị trí trong bài: Đoạn 5
• Giải thích: Bài văn chỉ đề cập “In many countries” có hệ thống cảnh báo di động nhưng không nói tất cả nước dùng hệ thống giống nhau.

Câu 8: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: radio broadcasting, no longer necessary
• Vị trí trong bài: Đoạn 7, dòng 1
• Giải thích: “Radio and television broadcasting remain essential components” – vẫn còn thiết yếu, ngược lại với việc không còn cần thiết.

Câu 9: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: artificial intelligence, improve accuracy
• Vị trí trong bài: Đoạn 9, dòng 2-3
• Giải thích: “These technologies can analyze vast amounts of data… to predict disasters with even greater accuracy” – có thể dự đoán với độ chính xác cao hơn.

Câu 10: buoys

• Dạng câu hỏi: Sentence Completion
• Từ khóa: tsunami warning systems, pressure sensors
• Vị trí trong bài: Đoạn 3, dòng 4
• Giải thích: “a network of buoys equipped with pressure sensors” – phao được trang bị cảm biến áp suất.

Câu 11: millions

• Dạng câu hỏi: Sentence Completion
• Từ khóa: Japan’s earthquake early warning system, phones
• Vị trí trong bài: Đoạn 5, dòng cuối
• Giải thích: “sends alerts to millions of phones simultaneously” – gửi đến hàng triệu điện thoại.

Câu 12: disabilities

• Dạng câu hỏi: Sentence Completion
• Từ khóa: people with, may not receive warnings
• Vị trí trong bài: Đoạn 8, dòng 2
• Giải thích: “people with disabilities may not receive or understand warnings in time” – người khuyết tật.

Câu 13: personalized

• Dạng câu hỏi: Sentence Completion
• Từ khóa: future warning systems, individual circumstances
• Vị trí trong bài: Đoạn 9, dòng 3-4
• Giải thích: “provide personalized warnings based on an individual’s location” – cảnh báo được cá nhân hóa.

Passage 2 – Giải Thích

Câu 14: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: drones particularly useful, after disasters
• Vị trí trong bài: Đoạn 1, dòng 2-3
• Giải thích: “These technologies enable emergency responders to… conduct search and rescue missions in environments that would be too dangerous or inaccessible for human teams” – cho phép hoạt động ở môi trường nguy hiểm hoặc không thể tiếp cận với con người.

Câu 15: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: thermal imaging technology, rescue operations
• Vị trí trong bài: Đoạn 3, dòng 3-4
• Giải thích: “Drones equipped with thermal cameras can detect heat signatures from human bodies” – phát hiện dấu hiệu nhiệt từ cơ thể người.

Câu 16: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Snake Robot, characterized
• Vị trí trong bài: Đoạn 6, dòng 2
• Giải thích: “Its flexible, modular design allows it to move through rubble-filled spaces” – thiết kế linh hoạt, mô-đun cho phép di chuyển qua không gian chật hẹp.

Câu 17: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Flying COWs, used to
• Vị trí trong bài: Đoạn 7, dòng 3-5
• Giải thích: “These systems… can restore basic communication services” – khôi phục dịch vụ truyền thông cơ bản, tức là cung cấp dịch vụ liên lạc tạm thời.

Câu 18: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: regulatory frameworks, drone use
• Vị trí trong bài: Đoạn 9, dòng 2
• Giải thích: “Regulatory frameworks governing airspace usage during emergencies vary by country and can complicate rapid deployment” – khung pháp lý khác nhau theo quốc gia và có thể làm phức tạp việc triển khai.

Câu 19: E

• Dạng câu hỏi: Matching Features
• Từ khóa: delivery drones
• Vị trí trong bài: Đoạn 4, dòng 1-3
• Giải thích: “drones can transport medical supplies, water purification tablets, communication equipment” – vận chuyển vật tư y tế đến các cộng đồng bị cô lập.

Câu 20: C

• Dạng câu hỏi: Matching Features
• Từ khóa: thermal imaging drones
• Vị trí trong bài: Đoạn 3, dòng 3-4
• Giải thích: “detect heat signatures from human bodies” – phát hiện nhiệt từ người bị mắc kẹt.

Câu 21: B

• Dạng câu hỏi: Matching Features
• Từ khóa: emergency communications drones
• Vị trí trong bài: Đoạn 7, dòng 1-2
• Giải thích: “provide temporary mobile phone or internet connectivity” – khôi phục kết nối điện thoại di động.

Câu 22: A (hoặc G)

• Dạng câu hỏi: Matching Features
• Từ khóa: specialized robots, manipulator arms
• Vị trí trong bài: Đoạn 5, dòng 2-3 và cuối đoạn
• Giải thích: “Specialized robots can enter unstable structures” và “equipped with manipulator arms that can move debris” – có thể vào cấu trúc không ổn định và di chuyển mảnh vỡ.

Câu 23: D

• Dạng câu hỏi: Matching Features
• Từ khóa: aquatic rescue drones
• Vị trí trong bài: Đoạn 8, dòng 2
• Giải thích: “delivering flotation devices while professional rescuers make their way to the scene” – đưa thiết bị nổi đến nạn nhân đuối nước.

Câu 24: battery life

• Dạng câu hỏi: Summary Completion
• Từ khóa: limits how long drones operate
• Vị trí trong bài: Đoạn 9, dòng 3
• Giải thích: “Battery life limits operational duration” – tuổi thọ pin giới hạn thời gian hoạt động.

Câu 25: operator training

• Dạng câu hỏi: Summary Completion
• Từ khóa: requires, piloting complex environments difficult
• Vị trí trong bài: Đoạn 9, dòng 4-5
• Giải thích: “Operator training remains essential; effectively piloting drones in complex disaster environments requires considerable skill” – đào tạo người vận hành là cần thiết.

Câu 26: privacy concerns

• Dạng câu hỏi: Summary Completion
• Từ khóa: must be addressed, surveillance technology, populated areas
• Vị trí trong bài: Đoạn 10, dòng 1
• Giải thích: “Privacy concerns also arise when surveillance technology is deployed in populated areas” – các mối quan ngại về quyền riêng tư nảy sinh.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: new paradigm, characterized by
• Vị trí trong bài: Đoạn 1, dòng 2-3
• Giải thích: “characterized by anticipatory rather than merely reactive strategies” – được đặc trưng bởi chiến lược dự đoán thay vì chỉ phản ứng.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: modern flood prediction, differ from traditional
• Vị trí trong bài: Đoạn 3, dòng 4-7
• Giải thích: “Contemporary systems integrate real-time precipitation data… to generate hyperlocal flood predictions with unprecedented accuracy” – tích hợp nhiều nguồn dữ liệu để tạo dự đoán siêu địa phương.

Câu 29: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: social media data, disaster prediction
• Vị trí trong bài: Đoạn 5, dòng cuối
• Giải thích: “determining signal from noise… and validating crowdsourced information all require sophisticated analytical techniques” – yêu cầu kỹ thuật phân tích phức tạp để xác thực.

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Explainable AI
• Vị trí trong bài: Đoạn 6, dòng 4-5
• Giải thích: “explainable AI approaches that not only generate predictions but also articulate the reasoning behind their forecasts” – trình bày lý do đằng sau dự đoán.

Câu 31: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: climate change, affects disaster prediction
• Vị trí trong bài: Đoạn 10, dòng 2-4
• Giải thích: “Many disaster forecasting models rely on assumptions of stationarity… However, anthropogenic climate change is fundamentally altering the baseline conditions” – thay đổi khí hậu thách thức giả định về tính ổn định.

Câu 32: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: predictive analytics, individual building level, floods
• Vị trí trong bài: Đoạn 3, dòng cuối
• Giải thích: “identifying risk at the individual building level hours or even days before inundation occurs” – xác định rủi ro ở cấp độ từng tòa nhà.

Câu 33: NOT GIVEN

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: all countries, same ethical standards
• Vị trí trong bài: Không có thông tin
• Giải thích: Bài văn thảo luận về các vấn đề đạo đức nhưng không đề cập liệu tất cả các nước có cùng chuẩn mực hay không.

Câu 34: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: vulnerable populations, most heavily consulted
• Vị trí trong bài: Đoạn 7, dòng cuối
• Giải thích: “vulnerable populations… may have minimal input into decisions” – có ít đầu vào trong quyết định, ngược lại với việc được tư vấn nhiều nhất.

Câu 35: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: predictive capabilities, new legal obligations
• Vị trí trong bài: Đoạn 9, dòng 3-4
• Giải thích: “Some legal scholars argue that predictive capabilities create enhanced obligations for governments” – khả năng dự đoán tạo ra nghĩa vụ tăng cường cho chính phủ.

Câu 36: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Sendai Framework, early warning systems
• Vị trí trong bài: Đoạn 11, dòng 2
• Giải thích: “International frameworks like the Sendai Framework for Disaster Risk Reduction explicitly emphasize the importance of… early warning systems” – nhấn mạnh rõ ràng tầm quan trọng.

Câu 37: B

• Dạng câu hỏi: Matching Sentence Endings
• Từ khóa: machine learning algorithms, disaster prediction
• Vị trí trong bài: Đoạn 2, dòng 3-4
• Giải thích: “Machine learning algorithms identify subtle patterns and correlations that escape human observation” – xác định các mẫu và tương quan mà con người có thể bỏ lỡ.

Câu 38: D

• Dạng câu hỏi: Matching Sentence Endings
• Từ khóa: anticipatory action, predictive data
• Vị trí trong bài: Đoạn 4, dòng cuối
• Giải thích: “anticipatory action based on predictive data can prevent disasters from occurring in the first place or at least mitigate their severity” – có thể ngăn chặn thảm họa hoặc giảm thiểu mức độ nghiêm trọng.

Câu 39: E

• Dạng câu hỏi: Matching Sentence Endings
• Từ khóa: integration of social media data
• Vị trí trong bài: Đoạn 5, dòng 5-6
• Giải thích: “This ‘social sensing’ provides situational awareness that complements physical sensor networks” – cung cấp nhận thức tình huống bổ sung cho mạng cảm biến vật lý.

Câu 40: F

• Dạng câu hỏi: Matching Sentence Endings
• Từ khóa: probabilistic forecasts
• Vị trí trong bài: Đoạn 7, dòng 1-2
• Giải thích: “Probabilistic forecasts are inherently uncertain; acting on predictions that ultimately prove incorrect can incur substantial costs” – vốn dĩ không chắc chắn và liên quan đến quyết định khó khăn về hành động.

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Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
protective action	n. phrase	/prəˈtektɪv ˈækʃən/	hành động bảo vệ	people receive warnings and take protective action	take protective action
revolutionize	v.	/ˌrevəˈluːʃənaɪz/	cách mạng hóa	technology has revolutionized the way we detect	revolutionize the way
impending	adj.	/ɪmˈpendɪŋ/	sắp xảy ra	information about impending disasters	impending disaster/threat
seismic sensors	n. phrase	/ˈsaɪzmɪk ˈsensərz/	cảm biến địa chấn	seismic sensors placed along fault lines	seismic activity/waves
fault lines	n. phrase	/fɔːlt laɪnz/	đường đứt gãy	sensors placed along fault lines	major/active fault lines
destructive waves	n. phrase	/dɪˈstrʌktɪv weɪvz/	sóng phá hủy	before the more destructive waves arrive	highly destructive
crucial time	n. phrase	/ˈkruːʃəl taɪm/	thời gian quan trọng	provides crucial time for people	crucial moment/period
buoys	n.	/bɔɪz/	phao	a network of buoys equipped with sensors	anchor a buoy
pressure sensors	n. phrase	/ˈpreʃər ˈsensərz/	cảm biến áp suất	buoys equipped with pressure sensors	install pressure sensors
advance notice	n. phrase	/ədˈvæns ˈnəʊtɪs/	thông báo trước	providing hours of advance notice	give advance notice
meteorological	adj.	/ˌmiːtiərəˈlɒdʒɪkəl/	thuộc khí tượng	meteorological satellites now provide data	meteorological conditions/data
unprecedented	adj.	/ʌnˈpresɪdentɪd/	chưa từng có	with unprecedented accuracy	unprecedented scale/level
timely evacuations	n. phrase	/ˈtaɪmli ɪˌvækjuˈeɪʃənz/	di tản kịp thời	enables authorities to order timely evacuations	order timely evacuations
location-based	adj.	/ləʊˈkeɪʃən beɪst/	dựa trên vị trí	location-based alerts ensure relevance	location-based services
disseminate	v.	/dɪˈsemɪneɪt/	phổ biến, lan truyền	rapidly disseminate information	disseminate information/knowledge
crowdsourced	adj.	/ˈkraʊdsɔːst/	lấy từ cộng đồng	creating a crowdsourced information network	crowdsourced data/content
misinformation	n.	/ˌmɪsɪnfərˈmeɪʃən/	thông tin sai lệch	misinformation can spread quickly	spread misinformation
vulnerable populations	n. phrase	/ˈvʌlnərəbəl ˌpɒpjuˈleɪʃənz/	dân số dễ bị tổn thương	warnings reach vulnerable populations	protect vulnerable populations
actionable	adj.	/ˈækʃənəbəl/	có thể hành động	making alerts more actionable	actionable information/insights

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
deployment	n.	/dɪˈplɔɪmənt/	sự triển khai	deployment of unmanned aerial vehicles	rapid deployment
unmanned aerial vehicles	n. phrase	/ʌnˈmænd ˈeəriəl ˈviːɪkəlz/	phương tiện bay không người lái	deployment of unmanned aerial vehicles (UAVs)	operate UAVs
fundamentally alter	v. phrase	/ˌfʌndəˈmentəli ˈɔːltər/	thay đổi căn bản	has fundamentally altered the landscape	fundamentally change/transform
inaccessible	adj.	/ˌɪnækˈsesəbəl/	không thể tiếp cận	environments that would be inaccessible	inaccessible areas/regions
aerial reconnaissance	n. phrase	/ˈeəriəl rɪˈkɒnɪsəns/	trinh sát trên không	aerial reconnaissance represents valuable contributions	conduct aerial reconnaissance
infrastructure damage	n. phrase	/ˈɪnfrəstrʌktʃər ˈdæmɪdʒ/	thiệt hại cơ sở hạ tầng	infrastructure damage often makes assessment impossible	assess infrastructure damage
submerged	adj.	/səbˈmɜːdʒd/	bị ngập nước	entire areas submerged or blocked	submerged vehicles/buildings
high-resolution cameras	n. phrase	/haɪ ˌrezəˈluːʃən ˈkæmərəz/	camera độ phân giải cao	drones equipped with high-resolution cameras	use high-resolution images
thermal imaging	n. phrase	/ˈθɜːməl ˈɪmɪdʒɪŋ/	chụp ảnh nhiệt	thermal imaging technology proves valuable	thermal imaging camera
incident commanders	n. phrase	/ˈɪnsɪdənt kəˈmɑːndərz/	chỉ huy hiện trường	providing incident commanders with overviews	incident command system
heat signatures	n. phrase	/hiːt ˈsɪɡnətʃərz/	dấu hiệu nhiệt	detect heat signatures from human bodies	identify heat signatures
critical window	n. phrase	/ˈkrɪtɪkəl ˈwɪndəʊ/	khoảng thời gian quan trọng	during the critical window when rescue attempts succeed	critical time window
cargo drones	n. phrase	/ˈkɑːɡəʊ drəʊnz/	máy bay không người lái chở hàng	developing larger cargo drones	cargo drone delivery
substantial payloads	n. phrase	/səbˈstænʃəl ˈpeɪləʊdz/	tải trọng lớn	capable of carrying substantial payloads	carry substantial payload
ground convoys	n. phrase	/ɡraʊnd ˈkɒnvɔɪz/	đoàn xe mặt đất	what might take ground convoys hours	military ground convoy
manipulator arms	n. phrase	/məˈnɪpjuleɪtər ɑːmz/	cánh tay điều khiển	equipped with manipulator arms	robotic manipulator arm
modular design	n. phrase	/ˈmɒdjʊlər dɪˈzaɪn/	thiết kế mô-đun	its flexible, modular design allows movement	modular construction/approach
invaluable intelligence	n. phrase	/ɪnˈvæljuəbəl ɪnˈtelɪdʒəns/	thông tin quý giá	providing invaluable intelligence	gather invaluable intelligence
aerial cell towers	n. phrase	/ˈeəriəl sel ˈtaʊərz/	trạm phát sóng di động trên không	acting as aerial cell towers	deploy aerial towers
coordination	n.	/kəʊˌɔːdɪˈneɪʃən/	sự phối hợp	enabling coordination of relief efforts	improve coordination
flotation devices	n. phrase	/fləʊˈteɪʃən dɪˈvaɪsɪz/	thiết bị nổi	delivering flotation devices	personal flotation device
autonomous underwater vehicles	n. phrase	/ɔːˈtɒnəməs ˈʌndəwɔːtər ˈviːɪkəlz/	phương tiện tự động dưới nước	autonomous underwater vehicles (AUVs) assist	operate AUVs
regulatory frameworks	n. phrase	/ˈreɡjələtəri ˈfreɪmwɜːks/	khung pháp lý	regulatory frameworks governing airspace	establish regulatory framework
optimal resource allocation	n. phrase	/ˈɒptɪməl rɪˈsɔːs ˌæləˈkeɪʃən/	phân bổ nguồn lực tối ưu	ensure optimal resource allocation	resource allocation strategy
swarm technology	n. phrase	/swɔːm tekˈnɒlədʒi/	công nghệ bầy đàn	swarm technology where drones coordinate	drone swarm technology

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
convergence	n.	/kənˈvɜːdʒəns/	sự hội tụ	convergence of big data analytics	convergence of technology
paradigm	n.	/ˈpærədaɪm/	mô hình, khuôn mẫu	ushering in a new paradigm	paradigm shift
anticipatory	adj.	/ænˈtɪsɪpətəri/	dự đoán trước	characterized by anticipatory strategies	anticipatory action/measures
epistemological shift	n. phrase	/ɪˌpɪstɪməˈlɒdʒɪkəl ʃɪft/	chuyển đổi nhận thức luận	fundamental epistemological shift	epistemological framework
post-hoc response	n. phrase	/pəʊst hɒk rɪˈspɒns/	phản ứng sau sự kiện	moving from post-hoc response	post-hoc analysis
probabilistic forecasting	n. phrase	/ˌprɒbəbɪˈlɪstɪk ˈfɔːkɑːstɪŋ/	dự báo xác suất	to probabilistic forecasting	probabilistic model
preemptive intervention	n. phrase	/priˈemptɪv ˌɪntəˈvenʃən/	can thiệp phòng ngừa	and preemptive intervention	preemptive action/strike
heterogeneous data streams	n. phrase	/ˌhetərəˈdʒiːniəs ˈdeɪtə striːmz/	luồng dữ liệu không đồng nhất	process heterogeneous data streams	integrate heterogeneous data
subtle patterns	n. phrase	/ˈsʌtəl ˈpætənz/	mẫu tinh tế	identify subtle patterns and correlations	detect subtle patterns
non-linear ways	n. phrase	/nɒn ˈlɪniər weɪz/	cách phi tuyến tính	modify disaster risk in complex, non-linear ways	non-linear relationship
granularity	n.	/ˌɡrænjuˈlærəti/	độ chi tiết	granularity and temporal resolution	data granularity
temporal resolution	n. phrase	/ˈtempərəl ˌrezəˈluːʃən/	độ phân giải thời gian	granularity and temporal resolution	high temporal resolution
qualitative advances	n. phrase	/ˈkwɒlɪtətɪv ədˈvɑːnsɪz/	tiến bộ về chất	represent qualitative advances	qualitative improvement
river gauge measurements	n. phrase	/ˈrɪvər ɡeɪdʒ ˈmeʒəmənts/	đo mức nước sông	relied on river gauge measurements	river gauge station
coarse spatial resolution	n. phrase	/kɔːs ˈspeɪʃəl ˌrezəˈluːʃən/	độ phân giải không gian thô	relatively coarse spatial resolution	spatial resolution data
hyperlocal predictions	n. phrase	/ˈhaɪpərləʊkəl prɪˈdɪkʃənz/	dự đoán siêu địa phương	generate hyperlocal flood predictions	hyperlocal weather forecast
inundation	n.	/ˌɪnʌnˈdeɪʃən/	sự ngập lụt	before inundation occurs	coastal inundation
preposition	v.	/ˌpriːpəˈzɪʃən/	bố trí trước	authorities can preposition emergency resources	preposition equipment
perpetual constraints	n. phrase	/pəˈpetʃuəl kənˈstreɪnts/	ràng buộc vĩnh viễn	given the perpetual constraints	budget constraints
mitigate	v.	/ˈmɪtɪɡeɪt/	giảm nhẹ	mitigate their severity	mitigate risk/impact
disseminate	v.	/dɪˈsemɪneɪt/	phổ biến	disseminate preventive health guidance	disseminate information
natural language processing	n. phrase	/ˈnætʃrəl ˈlæŋɡwɪdʒ ˈprəʊsesɪŋ/	xử lý ngôn ngữ tự nhiên	natural language processing algorithms	NLP techniques
situational awareness	n. phrase	/ˌsɪtʃuˈeɪʃənəl əˈweənəs/	nhận thức tình huống	provides situational awareness	maintain situational awareness
sparse instrumental coverage	n. phrase	/spɑːs ˌɪnstrəˈmentəl ˈkʌvərɪdʒ/	độ phủ thiết bị thưa thớt	areas with sparse instrumental coverage	instrumental measurement
demographic biases	n. phrase	/ˌdeməˈɡræfɪk ˈbaɪəsɪz/	thiên lệch nhân khẩu học	accounting for demographic biases	demographic characteristics
precursor conditions	n. phrase	/priˈkɜːsər kənˈdɪʃənz/	điều kiện tiền thân	identify precursor conditions for events	precursor indicator
explainable AI	n. phrase	/ɪkˈspleɪnəbəl eɪ aɪ/	trí tuệ nhân tạo có thể giải thích	employ explainable AI approaches	explainable AI model
articulate	v.	/ɑːˈtɪkjuleɪt/	trình bày rõ ràng	articulate the reasoning behind forecasts	articulate ideas/concerns
asymmetric distribution	n. phrase	/ˌæsɪˈmetrɪk ˌdɪstrɪˈbjuːʃən/	phân bố bất đối xứng	asymmetric distribution of impacts	asymmetric information
marginalized communities	n. phrase	/ˈmɑːdʒɪnəlaɪzd kəˈmjuːnətiz/	cộng đồng bị thiệt thòi	marginalized communities with limited influence	marginalized groups
granular information	n. phrase	/ˈɡrænjələr ˌɪnfəˈmeɪʃən/	thông tin chi tiết	requires granular information about individuals	granular data/detail
robust governance frameworks	n. phrase	/rəʊˈbʌst ˈɡʌvənəns ˈfreɪmwɜːks/	khung quản trị vững chắc	establishing robust governance frameworks	governance structure
culpable negligence	n. phrase	/ˈkʌlpəbəl ˈneɡlɪdʒəns/	sơ suất đáng trách	constitute a form of culpable negligence	criminal negligence
liability	n.	/ˌlaɪəˈbɪləti/	trách nhiệm pháp lý	establishing grounds for liability	legal liability
perverse incentives	n. phrase	/pəˈvɜːs ɪnˈsentɪvz/	động lực sai lệch	create perverse incentives to downplay	perverse effect
stationarity	n.	/ˌsteɪʃəˈnærəti/	tính ổn định	assumptions of stationarity	non-stationarity in data
anthropogenic climate change	n. phrase	/ˌænθrəpəˈdʒenɪk ˈklaɪmət tʃeɪndʒ/	biến đổi khí hậu do con người	anthropogenic climate change is altering	anthropogenic factors
non-stationary environment	n. phrase	/nɒn ˈsteɪʃənəri ɪnˈvaɪrənmənt/	môi trường không ổn định	non-stationary environment challenges approaches	non-stationary process
attainable	adj.	/əˈteɪnəbəl/	có thể đạt được	vision appears increasingly attainable	attainable goal/target
equitably	adv.	/ˈekwɪtəbli/	một cách công bằng	equitably protects all community members	distribute equitably

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Kết Bài

Chủ đề “How is technology enhancing disaster preparedness and response?” không chỉ là một đề tài khoa học quan trọng mà còn là nội dung thường xuyên xuất hiện trong IELTS Reading. Qua bài test mẫu này, bạn đã được trải nghiệm một đề thi hoàn chỉnh với 3 passages tăng dần về độ khó, từ nội dung cơ bản về hệ thống cảnh báo sớm, qua việc ứng dụng công nghệ drone và robot, đến những phân tích phức tạp về big data và dự đoán thảm họa.

Bộ 40 câu hỏi đa dạng trong đề thi này bao gồm 7 dạng câu hỏi phổ biến nhất: Multiple Choice, True/False/Not Given, Yes/No/Not Given, Matching Features, Matching Sentence Endings, Sentence Completion, và Summary Completion. Việc luyện tập với các dạng câu hỏi này giúp bạn làm quen với format thi thật và phát triển chiến lược làm bài hiệu quả cho từng dạng.

Đáp án chi tiết kèm theo giải thích không chỉ cho bạn biết câu trả lời đúng mà còn giúp bạn hiểu cách định vị thông tin trong passage, nhận diện paraphrase, và áp dụng kỹ thuật skimming và scanning hiệu quả. Đặc biệt, bảng từ vựng với hơn 60 từ và cụm từ quan trọng, kèm phiên âm, nghĩa tiếng Việt, ví dụ và collocation sẽ giúp bạn nâng cao vốn từ vựng học thuật – yếu tố then chốt để đạt band điểm cao.

Hãy dành thời gian xem lại những câu trả lời sai, phân tích lý do tại sao bạn chọn nhầm, và rút ra bài học cho lần luyện tập tiếp theo. Nếu bạn hoàn thành đúng 30-32/40 câu, bạn đang ở mức band 7.0-7.5; 33-35/40 câu tương đương band 8.0-8.5; và từ 36 câu trở lên là band 9.0. Đừng nản lòng nếu kết quả chưa như mong đợi – IELTS Reading là kỹ năng có thể cải thiện đáng kể qua luyện tập đều đặn với các đề thi chất lượng như thế này.