IELTS Reading: The Rise of Energy-Efficient Data Centers – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Trong kỷ nguyên số hóa, các trung tâm dữ liệu (data centers) đóng vai trò then chốt trong việc lưu trữ và xử lý khối lượng thông tin khổng lồ phục vụ cuộc sống hàng ngày của chúng ta. Chủ đề “The Rise Of Energy-efficient Data Centers” ngày càng xuất hiện thường xuyên trong các đề thi IELTS Reading, phản ánh xu hướng toàn cầu về công nghệ xanh và phát triển bền vững.

Bài viết này cung cấp một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages theo đúng chuẩn quốc tế, bao gồm độ khó tăng dần từ Easy (Band 5.0-6.5), Medium (Band 6.0-7.5) đến Hard (Band 7.0-9.0). Bạn sẽ được thực hành với 40 câu hỏi đa dạng, bao gồm các dạng phổ biến như Multiple Choice, True/False/Not Given, Matching Headings, và Summary Completion.

Mỗi passage đi kèm với đáp án chi tiết, giải thích cụ thể về vị trí thông tin, kỹ thuật paraphrase, và hướng dẫn cách làm bài hiệu quả. Bộ từ vựng chuyên ngành được tổng hợp giúp bạn nâng cao vốn từ học thuật. Bài thi mẫu này phù hợp cho học viên từ band 5.0 trở lên, đặc biệt hữu ích cho những ai đang ôn luyện cho kỳ thi IELTS Academic.

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test là phần thi kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được tính 1 điểm, không có điểm âm cho câu trả lời sai. Điểm số thô (raw score) sau đó được chuyển đổi thành band điểm từ 1-9.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (độ khó thấp nhất)
• Passage 2: 18-20 phút (độ khó trung bình)
• Passage 3: 23-25 phút (độ khó cao nhất)

Lưu ý quan trọng: Không có thời gian bổ sung để chép đáp án vào answer sheet, vì vậy bạn cần ghi đáp án trực tiếp trong khi làm bài.

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 nhiều lựa chọn
2. True/False/Not Given – Xác định thông tin đúng/sai/không có trong bài
3. Matching Information – Nối thông tin với đoạn văn tương ứng
4. Matching Headings – Chọn tiêu đề phù hợp cho các đoạn văn
5. Summary Completion – Hoàn thiện đoạn tóm tắt
6. Sentence Completion – Hoàn thiện câu
7. Short-answer Questions – Câu hỏi ngắn yêu cầu trả lời cụ thể

2. IELTS Reading Practice Test

PASSAGE 1 – The Evolution of Green Data Centers

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The global digital revolution has brought about an unprecedented demand for data storage and processing capabilities. Every time we stream a video, send an email, or use a smartphone application, we rely on massive data centers scattered across the world. These facilities, often described as the engines of the internet, consume enormous amounts of electricity to power servers and cooling systems. In fact, data centers currently account for approximately 1-2% of global electricity consumption, a figure that continues to rise as our digital dependency deepens.

The environmental impact of traditional data centers has become a pressing concern for both technology companies and environmental advocates. A typical data center requires as much electricity as a small town, with nearly half of that energy dedicated to cooling the equipment. The heat generated by thousands of servers running continuously creates a significant challenge. Without proper cooling, these machines would overheat and fail within minutes. Traditional cooling methods, which often rely on air conditioning systems powered by fossil fuels, contribute substantially to carbon emissions and climate change.

In response to these challenges, the technology industry has begun a remarkable transformation toward energy-efficient data centers. Leading companies like Google, Microsoft, and Facebook have invested billions of dollars in developing and implementing sustainable technologies. These innovations range from advanced cooling techniques to the use of renewable energy sources such as solar and wind power. The shift represents not only an environmental imperative but also an economic opportunity, as energy costs typically represent the largest operational expense for data center operators.

One of the most significant innovations in this field is free cooling, a technique that takes advantage of natural environmental conditions. In cooler climates, data centers can use outside air to cool their servers instead of relying solely on mechanical cooling systems. This approach has proven particularly successful in countries like Iceland and Norway, where cold temperatures and abundant hydroelectric power create ideal conditions for sustainable data centers. Some facilities have reported energy savings of up to 40% compared to traditional designs.

Liquid cooling systems represent another breakthrough in data center efficiency. Unlike air, which has limited heat capacity, liquids can absorb and transfer heat much more effectively. Modern liquid cooling technologies involve circulating special coolants directly through server components or immersing entire servers in non-conductive liquids. While initially more expensive to install, these systems can reduce cooling energy consumption by up to 95% and allow for much denser server configurations, reducing the overall physical footprint of data centers.

The integration of artificial intelligence (AI) and machine learning has also revolutionized data center management. These technologies can predict cooling needs, optimize power distribution, and identify inefficiencies in real-time. Google’s DeepMind AI system, for example, reduced cooling costs at the company’s data centers by 40% by learning to predict temperature and pressure dynamics and adjusting cooling systems accordingly. Such intelligent systems continuously improve their performance, leading to ongoing efficiency gains.

Renewable energy adoption has become a cornerstone of sustainable data center operations. Many major technology companies have committed to powering their facilities entirely with clean energy. Apple claims that all of its global data centers run on 100% renewable energy, primarily from solar and wind installations. Microsoft has pledged to be carbon negative by 2030, meaning it will remove more carbon from the atmosphere than it emits. These commitments have driven innovation in energy storage technologies and power purchase agreements that support the development of new renewable energy projects.

The concept of waste heat recovery is gaining traction as data centers look for ways to maximize efficiency. Rather than simply dissipating the heat generated by servers into the atmosphere, some facilities are capturing this thermal energy and putting it to productive use. In Stockholm, Sweden, a data center provides heat to warm homes and offices throughout the city. Similarly, a data center in Helsinki heats the water for a public swimming pool. These symbiotic relationships between data centers and local communities demonstrate how infrastructure can serve multiple purposes while reducing overall energy waste.

Despite these advances, significant challenges remain in the transition to fully sustainable data centers. The intermittent nature of solar and wind power creates difficulties in ensuring continuous operation. Energy storage solutions, such as large-scale batteries, are improving but remain expensive and have their own environmental considerations regarding raw material extraction and disposal. Additionally, as demand for digital services continues to grow exponentially, efficiency improvements must outpace this growth to achieve actual reductions in total energy consumption.

Questions 1-6

Do the following statements agree with the information given in Passage 1?

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

1. Data centers currently use between 1% and 2% of all electricity produced worldwide.
2. More than half of a typical data center’s electricity is used for cooling purposes.
3. Google has invested more money in sustainable technologies than Microsoft.
4. Free cooling techniques work better in warm climates than in cold regions.
5. Liquid cooling systems are initially cheaper to install than traditional cooling methods.
6. Some data centers in Sweden use their waste heat to provide heating for residential areas.

Questions 7-10

Complete the sentences below.

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

7. Traditional data centers often use __ powered by fossil fuels to keep equipment cool.
8. The use of __ represents the largest ongoing cost for data center operations.
9. Google’s __ system helped reduce cooling expenses by analyzing temperature patterns.
10. Apple states that all its data centers globally operate using 100% __.

Questions 11-13

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

11. According to the passage, what is the main reason traditional data centers need cooling?
    A. To reduce electricity costs
    B. To prevent equipment from overheating
    C. To comply with environmental regulations
    D. To maintain comfortable working conditions

12. What advantage of liquid cooling is mentioned in the passage?
    A. It is cheaper than all other cooling methods
    B. It uses less physical space
    C. It requires no maintenance
    D. It works in all climates

13. What challenge facing sustainable data centers is mentioned in the final paragraph?
    A. The high cost of servers
    B. The lack of skilled workers
    C. The unreliable nature of renewable energy sources
    D. The opposition from local communities

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PASSAGE 2 – The Economics and Infrastructure of Sustainable Data Centers

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The transition toward energy-efficient data centers represents a complex interplay between economic incentives, technological innovation, and environmental responsibility. While the ecological benefits of reducing energy consumption and carbon emissions are clear, the business case for sustainability has become equally compelling. Data center operators face substantial operational expenditures, with electricity costs comprising 30-50% of total running costs. This economic reality has transformed energy efficiency from a corporate social responsibility initiative into a strategic imperative that directly impacts profitability and competitive advantage.

The capital investment required for building sustainable data centers has decreased significantly over the past decade, making green technologies increasingly accessible. Early adopters faced prohibitive costs when implementing novel cooling systems or renewable energy infrastructure. However, as technologies have matured and economies of scale have been realized, the return on investment (ROI) timeline has shortened dramatically. Modern efficient data centers can achieve payback periods of 3-5 years on energy-saving technologies, compared to 10-15 years a decade ago. This improved financial viability has accelerated adoption across the industry, with even smaller operators now able to justify sustainability investments.

Location strategy has emerged as a critical factor in optimizing data center sustainability. The concept of “climate opportunism” involves deliberately siting facilities in regions where natural conditions reduce cooling requirements. Scandinavia has become a preferred destination, with its consistently cool temperatures eliminating the need for mechanical cooling during most of the year. Iceland offers an even more attractive proposition: abundant geothermal energy, cold air, and volcanic rock formations that provide natural insulation. These geographical advantages can reduce energy consumption by 30-60% compared to facilities in warmer regions, creating significant cost differentials that influence site selection decisions.

However, location decisions must balance energy efficiency against other operational considerations. Network latency – the time delay in data transmission – increases with distance from end users. A data center in Iceland may be extremely efficient, but if it serves customers primarily in Asia or South America, the resulting delays could be unacceptable for time-sensitive applications. This tension between efficiency and performance has led to the development of distributed architectures where data is strategically positioned across multiple locations. Edge computing, which processes data closer to where it is generated, represents an evolution of this approach, potentially reducing both energy consumption and latency simultaneously.

The design and construction of modular data centers has revolutionized the industry’s approach to scalability and efficiency. Unlike traditional facilities built to maximum anticipated capacity, modular designs allow for incremental expansion aligned with actual demand. These systems, often housed in prefabricated containers, incorporate optimized cooling and power distribution from the outset. Facebook’s data center in Prineville, Oregon, pioneered this approach, demonstrating that modular designs could achieve Power Usage Effectiveness (PUE) ratings below 1.1 – meaning that for every watt used by computing equipment, only 0.1 additional watts are consumed by infrastructure. Industry-wide, PUE has improved from an average of 2.5 in 2007 to approximately 1.6 today, representing a substantial efficiency gain.

Thermal management innovations extend beyond cooling to encompass server design itself. Traditional servers were designed to operate within narrow temperature ranges, necessitating precise climate control. Research has demonstrated that hardware can function reliably at much higher temperatures than previously believed. By expanding the acceptable operating range from the typical 20-22°C to 27-30°C or higher, data centers can dramatically reduce cooling requirements. This approach, termed “temperature tolerance,” requires collaboration between hardware manufacturers and facility operators to ensure reliability while maximizing efficiency.

The concept of workload scheduling adds another dimension to energy optimization. Not all computational tasks require immediate processing; many can be deferred to times when electricity is cheaper or cleaner. Intelligent systems can monitor real-time electricity prices and carbon intensity (the amount of CO2 emitted per unit of electricity), automatically shifting non-urgent workloads to periods of low demand or high renewable energy availability. Google employs this strategy to align its computing tasks with times when wind and solar generation peak, reducing both costs and environmental impact without compromising service quality.

Power Purchase Agreements (PPAs) have emerged as a crucial mechanism for financing renewable energy development. Through PPAs, data center operators commit to purchasing electricity from specific renewable energy projects over extended periods, typically 10-25 years. This guaranteed revenue stream reduces financial risk for renewable energy developers, facilitating project financing and construction. Amazon Web Services has become the world’s largest corporate purchaser of renewable energy through PPAs, contracting for over 12 gigawatts of capacity from solar and wind projects. These agreements create a symbiotic relationship: data centers secure long-term clean energy at predictable prices, while renewable energy projects gain the financial stability needed for development.

The emerging practice of “carbon accounting” is pushing companies toward more comprehensive sustainability strategies. Rather than simply measuring direct emissions from facilities, Scope 3 accounting includes indirect emissions throughout the entire supply chain, from manufacturing servers to disposing of obsolete equipment. This holistic view reveals that embodied carbon – emissions from producing and transporting equipment – can equal or exceed operational emissions over a data center’s lifetime. Consequently, leading operators are extending sustainability requirements to suppliers, demanding that manufacturers use renewable energy, minimize packaging, and design for recyclability and longevity.

Despite remarkable progress, the industry faces an inherent paradox: even as individual data centers become more efficient, total energy consumption continues to rise due to exponential growth in digital services. Streaming video, cloud computing, artificial intelligence, and Internet of Things devices are generating data at unprecedented rates. Some researchers warn of a potential “efficiency rebound,” where improvements in energy efficiency are outpaced by increases in demand, resulting in higher absolute energy consumption. Addressing this challenge requires not only technical solutions but also broader societal discussions about digital consumption patterns and the true environmental cost of our connected lifestyle.

Questions 14-19

Complete the summary below.

Choose NO MORE THAN TWO WORDS AND/OR A NUMBER from the passage for each answer.

The business case for sustainable data centers has strengthened considerably as the economic benefits have become clearer. Energy costs represent between 30 and 50 percent of 14. __ for data center operators. The time required to recover investments in green technologies has decreased from 10-15 years to just 15. __ years for modern facilities.

Location plays a crucial role in sustainability, with some companies practicing 16. __ by building in naturally cool regions. However, operators must consider **17. __, which refers to delays in data transmission, when deciding where to locate facilities.

Modern data centers measure their efficiency using 18. __, with the best facilities achieving ratings below 1.1. Meanwhile, 19. __ agreements help finance renewable energy projects by guaranteeing long-term electricity purchases.

Questions 20-23

Choose FOUR letters, A-H.

Which FOUR of the following are mentioned in the passage as strategies for improving data center sustainability?

A. Using volcanic rock for natural insulation
B. Processing data closer to where users are located
C. Increasing the temperature range in which servers operate
D. Reducing the salaries of data center employees
E. Scheduling non-urgent tasks for times when renewable energy is abundant
F. Purchasing electricity exclusively during nighttime hours
G. Requiring suppliers to adopt sustainable practices
H. Relocating all data centers to coastal areas

Questions 24-26

Do the following statements agree with the information given in Passage 2?

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

24. Modular data centers are more efficient than traditional facilities built to maximum capacity.
25. All computational tasks can be delayed until renewable energy is available.
26. The carbon emissions from manufacturing data center equipment can be greater than emissions from operating the facility.

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PASSAGE 3 – Future Paradigms: Reimagining Data Infrastructure in a Climate-Constrained World

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The trajectory of data center evolution over the coming decades will likely be characterized not merely by incremental efficiency improvements but by fundamental paradigm shifts in how we conceptualize and implement digital infrastructure. As climate imperatives intensify and computational demands continue their exponential ascent, the industry confronts a critical juncture: the existing model of centralized, electricity-intensive facilities may prove fundamentally incompatible with global decarbonization objectives. Emerging research suggests that achieving genuine sustainability will require disruptive innovations that challenge core assumptions about where, how, and even whether traditional data centers should exist.

One of the most radical propositions involves leveraging thermodynamic principles more fully by embracing heat rather than fighting it. Reversible computing, a theoretical framework proposed by physicist Rolf Landauer and refined by Charles Bennett, suggests that computation need not inherently generate heat. Conventional computing is “logically irreversible” – it erases information at each computational step, and according to Landauer’s principle, each bit of information erased must dissipate a minimum amount of energy as heat. Reversible computing architectures, which preserve information throughout calculations, could theoretically operate at dramatically lower temperatures and energy levels. While practical implementation remains elusive, recent experimental demonstrations using quantum computing principles have validated aspects of this approach, suggesting that future generations of processors might achieve orders-of-magnitude improvements in energy efficiency.

The prospect of underwater data centers represents another unconventional approach gaining empirical support. Microsoft’s Project Natick deployed a prototype facility on the seafloor off Scotland’s Orkney Islands, where it operated successfully for two years. The ocean provides several advantages: consistent cool temperatures for passive cooling, renewable energy integration (the Orkney deployment used local tidal and wind power), and reduced real estate costs. Perhaps most surprisingly, the failure rate of servers in the underwater environment was one-eighth that of conventional facilities, potentially due to the absence of oxygen and humidity fluctuations that contribute to corrosion. The nitrogen atmosphere within the sealed container proved remarkably protective. However, questions persist regarding environmental impacts on marine ecosystems, accessibility for maintenance, and scalability beyond demonstration projects.

Biological computing emerges from an entirely different conceptual framework, drawing inspiration from the remarkable efficiency of neural systems. The human brain performs computational tasks vastly more complex than current supercomputers while consuming merely 20 watts – less than a typical light bulb. This neuromorphic computing approach mimics biological neural networks, using analog signals and event-driven processing rather than the clock-based digital architecture of conventional computers. Prototypes have demonstrated energy efficiencies 10,000 times greater than traditional processors for specific tasks, particularly those involving pattern recognition and sensory processing. Intel’s Loihi chip and IBM’s TrueNorth represent early commercial implementations of these principles, though they remain specialized tools rather than general-purpose computing platforms.

The distributed ledger technologies underlying blockchain systems present a paradoxical case study in energy consumption. Bitcoin’s proof-of-work consensus mechanism famously consumes electricity comparable to entire nations, exemplifying how certain computing paradigms can be extraordinarily inefficient. However, alternative consensus mechanisms, such as proof-of-stake, reduce energy requirements by over 99% while maintaining security properties. More broadly, distributed systems that eliminate redundant data center infrastructure by leveraging peer-to-peer networks could fundamentally reshape computing economics. The Internet Computer project, for instance, envisions replacing traditional cloud infrastructure with a decentralized network of independent data centers, potentially improving both efficiency and resilience.

Cryogenic computing, which operates processors at extremely low temperatures, exploits superconductivity and reduced electrical resistance to achieve dramatic efficiency gains. While conventional data centers expend enormous energy fighting heat, cryogenic systems embrace cold, using liquid nitrogen or helium cooling to reach temperatures approaching absolute zero. At these temperatures, some materials become superconductors with zero electrical resistance, eliminating energy losses in circuits. Prototypes have demonstrated viability, and companies like Microsoft are exploring integration with quantum computing systems that already require cryogenic conditions. The primary obstacle remains the energy cost of refrigeration itself, though innovations in closed-cycle cooling systems are narrowing this gap.

The concept of computational conservation challenges the demand-side equation by questioning whether all computing tasks are necessary. Analogous to energy conservation efforts that reduce consumption rather than merely improving efficiency, this approach scrutinizes the proliferation of data-intensive services. Do recommendation algorithms need to analyze billions of data points? Must every email be permanently archived across multiple servers? Could differential privacy techniques achieve similar outcomes with less computational overhead? Some researchers advocate for “computational impact statements” analogous to environmental impact assessments, requiring developers to justify resource consumption and explore lighter-weight alternatives.

The intersection of data centers with carbon capture and storage (CCS) technologies represents an emerging frontier. Rather than merely reducing emissions, some facilities are exploring carbon-negative operations that actively remove CO2 from the atmosphere. Direct air capture systems, which chemically extract carbon dioxide from ambient air, require significant energy to operate – precisely the resource that data centers produce. Theoretically, excess renewable energy could power carbon capture equipment co-located with data infrastructure. Climeworks, a Swiss company, has demonstrated this concept at a small scale in Iceland, where geothermal-powered data centers and carbon capture facilities operate synergistically. Critics note, however, that current carbon capture costs remain prohibitively high, and devoting resources to prevention may be more efficient than remediation.

Regulatory frameworks and market mechanisms will likely prove as influential as technology in shaping future data infrastructure. The European Union’s proposed Digital Services Act includes provisions for environmental reporting and efficiency standards. Some jurisdictions are considering carbon taxes specifically targeting data centers or renewable energy mandates for major facilities. Conversely, tax incentives and accelerated depreciation for sustainable infrastructure can drive investment. Singapore’s moratorium on new data centers, enacted due to space and energy constraints, demonstrates how policy can fundamentally redirect industry development. As climate concerns intensify, governments may increasingly treat data infrastructure as a regulated utility requiring environmental accounting comparable to power plants or factories.

The ultimate constraint on data center sustainability may prove to be neither technological nor economic but fundamentally thermodynamic. The second law of thermodynamics establishes that all real-world processes generate entropy – disorder that manifests as waste heat. While efficiency improvements can minimize this heat, they cannot eliminate it entirely within conventional computing paradigms. Some physicists argue that humanity’s exponentially growing information processing will eventually encounter planetary thermal limits, where waste heat from computation contributes measurably to climate change regardless of energy source. This sobering perspective suggests that long-term sustainability may require not just clean energy and efficient systems, but conscious limitations on computational growth itself – a steady-state digital economy that challenges prevailing assumptions about perpetual technological expansion.

Questions 27-31

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

27. According to the passage, reversible computing differs from conventional computing in that it:
    A. operates at much higher temperatures
    B. preserves information during calculations
    C. uses more electricity than traditional processors
    D. has been widely implemented in commercial systems

28. What unexpected finding emerged from Microsoft’s underwater data center experiment?
    A. Servers failed more frequently than expected
    B. Ocean temperatures were too warm for effective cooling
    C. Server reliability was significantly better than in conventional facilities
    D. Marine life was severely impacted by the installation

29. The human brain’s computational efficiency is cited to illustrate:
    A. why biological computing cannot work practically
    B. the potential energy savings of neuromorphic approaches
    C. that computers will never match human intelligence
    D. the superiority of digital over analog processing

30. The author’s discussion of Bitcoin’s energy consumption serves to demonstrate:
    A. that all blockchain systems are environmentally harmful
    B. how different design choices affect energy requirements
    C. why cryptocurrency should be banned
    D. that distributed systems are always inefficient

31. Which statement best describes the passage’s perspective on computational conservation?
    A. It is impossible to implement effectively
    B. It focuses only on improving technical efficiency
    C. It questions whether all computing tasks are necessary
    D. It has already been widely adopted by the industry

Questions 32-36

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

32. Cryogenic computing systems __
33. Carbon capture technologies co-located with data centers __
34. Singapore’s moratorium on new data centers __
35. The second law of thermodynamics __
36. Proof-of-stake consensus mechanisms __

A. reduce energy consumption by over 99% compared to proof-of-work
B. demonstrates how government policy can reshape industry development
C. could potentially remove more CO2 than operations produce
D. operate processors at temperatures close to absolute zero
E. proves that renewable energy will solve all problems
F. establishes fundamental limits on computing efficiency
G. requires massive increases in electricity consumption
H. eliminates all forms of waste heat from computing

Questions 37-40

Do the following statements agree with the claims of the writer in Passage 3?

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

37. Practical implementation of reversible computing has been fully achieved and is widely used.

38. The nitrogen atmosphere in Microsoft’s underwater data center contributed to improved server reliability.

39. Neuromorphic computing systems are currently more efficient than traditional processors for all types of computational tasks.

40. Long-term computational sustainability may ultimately require limiting the growth of digital services.

Trung tâm dữ liệu xanh với hệ thống làm mát hiện đại và năng lượng tái tạo cho bài thi IELTS Reading

3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. TRUE
2. FALSE
3. NOT GIVEN
4. FALSE
5. FALSE
6. TRUE
7. air conditioning systems
8. energy costs / electricity costs
9. DeepMind AI
10. renewable energy
11. B
12. B
13. C

PASSAGE 2: Questions 14-26

14. operational expenditures / running costs
15. 3-5
16. climate opportunism
17. network latency
18. Power Usage Effectiveness / PUE
19. Power Purchase
    20-23. B, C, E, G (in any order)
20. YES
21. NO
22. YES

PASSAGE 3: Questions 27-40

27. B
28. C
29. B
30. B
31. C
32. D
33. C
34. B
35. F
36. A
37. NO
38. YES
39. NO
40. YES

4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: data centers, 1-2%, global electricity consumption
• Vị trí trong bài: Đoạn 1, dòng 4-6
• Giải thích: Bài đọc nói rõ “data centers currently account for approximately 1-2% of global electricity consumption” khớp chính xác với phát biểu trong câu hỏi.

Câu 2: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: half, electricity, cooling
• Vị trí trong bài: Đoạn 2, dòng 2-3
• Giải thích: Bài viết nói “nearly half” (gần một nửa) của năng lượng dùng cho làm mát, không phải “more than half” (hơn một nửa). Đây là sự khác biệt quan trọng khiến câu này FALSE.

Câu 3: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Google, Microsoft, invested more money
• Vị trí trong bài: Đoạn 3
• Giải thích: Bài đọc đề cập cả Google và Microsoft đều đầu tư vào công nghệ bền vững nhưng không so sánh số tiền cụ thể, do đó không thể xác định công ty nào đầu tư nhiều hơn.

Câu 4: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: free cooling, warm climates, cold regions
• Vị trí trong bài: Đoạn 4
• Giải thích: Bài viết nói rõ free cooling “particularly successful in countries like Iceland and Norway, where cold temperatures” – tức là hoạt động tốt ở vùng lạnh, không phải vùng nóng.

Câu 5: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: liquid cooling, initially cheaper
• Vị trí trong bài: Đoạn 5, dòng 4-5
• Giải thích: Bài đọc nói “While initially more expensive to install” – ban đầu đắt hơn để lắp đặt, trái ngược với “cheaper” trong câu hỏi.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Sweden, waste heat, heating, residential areas
• Vị trí trong bài: Đoạn 8, dòng 3-4
• Giải thích: “In Stockholm, Sweden, a data center provides heat to warm homes and offices” khớp với thông tin về việc cung cấp sưởi cho khu dân cư.

Câu 7: air conditioning systems

• Dạng câu hỏi: Sentence Completion
• Từ khóa: traditional data centers, powered by fossil fuels, cool
• Vị trí trong bài: Đoạn 2, dòng 5-6
• Giải thích: “Traditional cooling methods, which often rely on air conditioning systems powered by fossil fuels”

Câu 8: energy costs / electricity costs

• Dạng câu hỏi: Sentence Completion
• Từ khóa: largest operational expense, data center operators
• Vị trí trong bài: Đoạn 3, dòng cuối
• Giải thích: “energy costs typically represent the largest operational expense for data center operators”

Câu 9: DeepMind AI

• Dạng câu hỏi: Sentence Completion
• Từ khóa: Google, system, reduced cooling costs
• Vị trí trong bài: Đoạn 6, dòng 3-4
• Giải thích: “Google’s DeepMind AI system, for example, reduced cooling costs”

Câu 10: renewable energy

• Dạng câu hỏi: Sentence Completion
• Từ khóa: Apple, data centers, 100%
• Vị trí trong bài: Đoạn 7, dòng 3-4
• Giải thích: “Apple claims that all of its global data centers run on 100% renewable energy”

Câu 11: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: main reason, cooling
• Vị trí trong bài: Đoạn 2, dòng 3-5
• Giải thích: “Without proper cooling, these machines would overheat and fail within minutes” – lý do chính là ngăn thiết bị quá nhiệt.

Câu 12: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: liquid cooling, advantage
• Vị trí trong bài: Đoạn 5, dòng cuối
• Giải thích: “allow for much denser server configurations, reducing the overall physical footprint” – cho phép bố trí server dày đặc hơn, giảm không gian vật lý.

Câu 13: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: challenge, sustainable data centers
• Vị trí trong bài: Đoạn 9, dòng 2-3
• Giải thích: “The intermittent nature of solar and wind power creates difficulties” – tính không ổn định của năng lượng tái tạo.

Hệ thống làm mát bằng chất lỏng và AI trong trung tâm dữ liệu thế hệ mới

Passage 2 – Giải Thích

Câu 14: operational expenditures / running costs

• Dạng câu hỏi: Summary Completion
• Từ khóa: 30-50 percent, data center operators
• Vị trí trong bài: Đoạn 1, dòng 3-4
• Giải thích: “electricity costs comprising 30-50% of total running costs” hoặc có thể dùng “operational expenditures” được đề cập ngay trước đó.

Câu 15: 3-5

• Dạng câu hỏi: Summary Completion
• Từ khóa: recover investments, decreased from 10-15 years
• Vị trí trong bài: Đoạn 2, dòng 5-6
• Giải thích: “payback periods of 3-5 years on energy-saving technologies, compared to 10-15 years a decade ago”

Câu 16: climate opportunism

• Dạng câu hỏi: Summary Completion
• Từ khóa: building in naturally cool regions
• Vị trí trong bài: Đoạn 3, dòng 2
• Giải thích: Thuật ngữ chuyên môn “climate opportunism” được định nghĩa là xây dựng ở vùng có điều kiện tự nhiên thuận lợi.

Câu 17: network latency

• Dạng câu hỏi: Summary Completion
• Từ khóa: delays in data transmission
• Vị trí trong bài: Đoạn 4, dòng 1-2
• Giải thích: “Network latency – the time delay in data transmission” – định nghĩa rõ ràng trong bài.

Câu 18: Power Usage Effectiveness / PUE

• Dạng câu hỏi: Summary Completion
• Từ khóa: measure efficiency, ratings below 1.1
• Vị trí trong bài: Đoạn 5, dòng 5-7
• Giải thích: “achieve Power Usage Effectiveness (PUE) ratings below 1.1”

Câu 19: Power Purchase

• Dạng câu hỏi: Summary Completion
• Từ khóa: agreements, finance renewable energy, long-term electricity purchases
• Vị trí trong bài: Đoạn 8, dòng 1
• Giải thích: “Power Purchase Agreements (PPAs)” – chỉ cần ghi “Power Purchase” do giới hạn từ.

Câu 20-23: B, C, E, G

• Dạng câu hỏi: Multiple Selection
• Giải thích chi tiết:
  • B (Edge computing): Đoạn 4 – “Edge computing, which processes data closer to where it is generated”
  • C (Temperature tolerance): Đoạn 6 – “expanding the acceptable operating range from the typical 20-22°C to 27-30°C”
  • E (Workload scheduling): Đoạn 7 – “automatically shifting non-urgent workloads to periods of low demand”
  • G (Supplier requirements): Đoạn 9 – “extending sustainability requirements to suppliers”
  • A được đề cập nhưng không phải chiến lược, chỉ là lợi thế địa lý
  • D, F, H không được đề cập trong bài

Câu 24: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: modular data centers, more efficient, traditional facilities
• Vị trí trong bài: Đoạn 5
• Giải thích: Bài viết mô tả modular designs đạt PUE dưới 1.1, tốt hơn nhiều so với trung bình ngành 1.6, và cho phép mở rộng theo nhu cầu thay vì xây dựng tối đa ngay từ đầu.

Câu 25: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: all computational tasks, delayed
• Vị trí trong bài: Đoạn 7, dòng 1
• Giải thích: “Not all computational tasks require immediate processing; many can be deferred” – chỉ “many” (nhiều) không phải “all” (tất cả) có thể trì hoãn.

Câu 26: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: carbon emissions, manufacturing, greater than, operating
• Vị trí trong bài: Đoạn 9, dòng 4-5
• Giải thích: “embodied carbon – emissions from producing and transporting equipment – can equal or exceed operational emissions”

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: reversible computing, differs from conventional
• Vị trí trong bài: Đoạn 2, dòng 4-6
• Giải thích: “Reversible computing architectures, which preserve information throughout calculations” – khác biệt chính là bảo tồn thông tin.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Microsoft, underwater data center, unexpected finding
• Vị trí trong bài: Đoạn 3, dòng 4-6
• Giải thích: “Perhaps most surprisingly, the failure rate of servers in the underwater environment was one-eighth that of conventional facilities” – độ tin cậy cao hơn đáng kể là điều bất ngờ.

Câu 29: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: human brain, computational efficiency, cited to illustrate
• Vị trí trong bài: Đoạn 4, dòng 1-3
• Giải thích: Bộ não được đưa ra để minh họa hiệu quả năng lượng tiềm năng của neuromorphic computing: “consuming merely 20 watts” so với “Prototypes have demonstrated energy efficiencies 10,000 times greater”

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Bitcoin, energy consumption, serves to demonstrate
• Vị trí trong bài: Đoạn 5
• Giải thích: Bitcoin được đề cập để so sánh với proof-of-stake “reduce energy requirements by over 99%” – cho thấy các lựa chọn thiết kế khác nhau ảnh hưởng như thế nào đến năng lượng.

Câu 31: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: computational conservation, perspective
• Vị trí trong bài: Đoạn 7, dòng 1-3
• Giải thích: “this approach scrutinizes the proliferation of data-intensive services. Do recommendation algorithms need to analyze billions of data points?” – đặt câu hỏi về tính cần thiết.

Câu 32: D

• Dạng câu hỏi: Matching Sentence Endings
• Từ khóa: Cryogenic computing systems
• Vị trí trong bài: Đoạn 6, dòng 1-3
• Giải thích: “Cryogenic computing, which operates processors at extremely low temperatures… reach temperatures approaching absolute zero”

Câu 33: C

• Dạng câu hỏi: Matching Sentence Endings
• Từ khóa: Carbon capture technologies, co-located
• Vị trí trong bài: Đoạn 8, dòng 1-2
• Giải thích: “Rather than merely reducing emissions, some facilities are exploring carbon-negative operations that actively remove CO2”

Câu 34: B

• Dạng câu hỏi: Matching Sentence Endings
• Từ khóa: Singapore’s moratorium
• Vị trí trong bài: Đoạn 9, dòng 5-6
• Giải thích: “demonstrates how policy can fundamentally redirect industry development”

Câu 35: F

• Dạng câu hỏi: Matching Sentence Endings
• Từ khóa: second law of thermodynamics
• Vị trí trong bài: Đoạn 10, dòng 1-3
• Giải thích: “establishes that all real-world processes generate entropy… efficiency improvements can minimize this heat, they cannot eliminate it entirely”

Câu 36: A

• Dạng câu hỏi: Matching Sentence Endings
• Từ khóa: Proof-of-stake consensus mechanisms
• Vị trí trong bài: Đoạn 5, dòng 3
• Giải thích: “alternative consensus mechanisms, such as proof-of-stake, reduce energy requirements by over 99%”

Câu 37: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: reversible computing, fully achieved, widely used
• Vị trí trong bài: Đoạn 2, dòng 7
• Giải thích: “While practical implementation remains elusive” – vẫn còn khó nắm bắt, chưa được triển khai thực tế rộng rãi.

Câu 38: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: nitrogen atmosphere, underwater data center, improved reliability
• Vị trí trong bài: Đoạn 3, dòng 6-7
• Giải thích: “The nitrogen atmosphere within the sealed container proved remarkably protective” – không khí nitơ đóng vai trò bảo vệ, góp phần vào độ tin cậy cao hơn.

Câu 39: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: neuromorphic computing, more efficient, all types of tasks
• Vị trí trong bài: Đoạn 4, dòng 4-5
• Giải thích: “energy efficiencies 10,000 times greater than traditional processors for specific tasks” – chỉ cho “specific tasks” không phải “all types”

Câu 40: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: long-term sustainability, limiting growth, digital services
• Vị trí trong bài: Đoạn 10, dòng cuối
• Giải thích: “long-term sustainability may require not just clean energy and efficient systems, but conscious limitations on computational growth itself”

Các công nghệ tiên tiến cho trung tâm dữ liệu bền vững trong tương lai

5. Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
unprecedented	adj	/ʌnˈpresɪdentɪd/	chưa từng có, không có tiền lệ	unprecedented demand for data storage	unprecedented growth/scale/level
environmental impact	noun phrase	/ɪnˌvaɪrənˈmentl ˈɪmpækt/	tác động môi trường	environmental impact of traditional data centers	assess/reduce environmental impact
carbon emissions	noun phrase	/ˈkɑːrbən ɪˈmɪʃnz/	khí thải carbon	contribute substantially to carbon emissions	reduce/cut carbon emissions
sustainable	adj	/səˈsteɪnəbl/	bền vững	sustainable technologies	sustainable development/practices
renewable energy	noun phrase	/rɪˈnjuːəbl ˈenədʒi/	năng lượng tái tạo	use of renewable energy sources	renewable energy sources/generation
free cooling	noun phrase	/friː ˈkuːlɪŋ/	làm mát tự nhiên	free cooling technique	implement free cooling
hydroelectric power	noun phrase	/ˌhaɪdrəʊɪˈlektrɪk ˈpaʊər/	thủy điện	abundant hydroelectric power	generate hydroelectric power
energy savings	noun phrase	/ˈenədʒi ˈseɪvɪŋz/	tiết kiệm năng lượng	energy savings of up to 40%	achieve energy savings
physical footprint	noun phrase	/ˈfɪzɪkl ˈfʊtprɪnt/	diện tích vật lý	reducing the overall physical footprint	reduce/minimize physical footprint
artificial intelligence	noun phrase	/ˌɑːrtɪˈfɪʃl ɪnˈtelɪdʒəns/	trí tuệ nhân tạo	integration of artificial intelligence	apply/use artificial intelligence
waste heat recovery	noun phrase	/weɪst hiːt rɪˈkʌvəri/	thu hồi nhiệt thải	waste heat recovery is gaining traction	implement waste heat recovery
energy storage	noun phrase	/ˈenədʒi ˈstɔːrɪdʒ/	lưu trữ năng lượng	energy storage solutions	develop energy storage

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
operational expenditures	noun phrase	/ˌɒpəˈreɪʃənl ɪkˈspendɪtʃəz/	chi phí vận hành	face substantial operational expenditures	reduce operational expenditures
strategic imperative	noun phrase	/strəˈtiːdʒɪk ɪmˈperətɪv/	yêu cầu chiến lược	transformed into a strategic imperative	become a strategic imperative
capital investment	noun phrase	/ˈkæpɪtl ɪnˈvestmənt/	đầu tư vốn	capital investment required	make capital investment
return on investment	noun phrase	/rɪˈtɜːn ɒn ɪnˈvestmənt/	lợi tức đầu tư	improved ROI timeline	maximize return on investment
economies of scale	noun phrase	/ɪˈkɒnəmiz əv skeɪl/	lợi thế kinh tế theo quy mô	as economies of scale have been realized	achieve economies of scale
network latency	noun phrase	/ˈnetwɜːk ˈleɪtənsi/	độ trễ mạng	Network latency increases with distance	reduce/minimize network latency
distributed architectures	noun phrase	/dɪˈstrɪbjuːtɪd ˈɑːkɪtektʃəz/	kiến trúc phân tán	led to distributed architectures	develop distributed architectures
modular design	noun phrase	/ˈmɒdʒələr dɪˈzaɪn/	thiết kế mô-đun	modular designs allow incremental expansion	adopt modular design
thermal management	noun phrase	/ˈθɜːml ˈmænɪdʒmənt/	quản lý nhiệt	thermal management innovations	improve thermal management
workload scheduling	noun phrase	/ˈwɜːkləʊd ˈʃedjuːlɪŋ/	lập lịch khối lượng công việc	workload scheduling adds another dimension	optimize workload scheduling
supply chain	noun phrase	/səˈplaɪ tʃeɪn/	chuỗi cung ứng	throughout the entire supply chain	manage/optimize supply chain
exponential growth	noun phrase	/ˌekspəˈnenʃl ɡrəʊθ/	tăng trưởng theo cấp số nhân	exponential growth in digital services	experience exponential growth
embodied carbon	noun phrase	/ɪmˈbɒdid ˈkɑːbən/	carbon nội tại	embodied carbon can exceed operational emissions	reduce embodied carbon
Power Purchase Agreement	noun phrase	/ˈpaʊər ˈpɜːtʃəs əˈɡriːmənt/	hợp đồng mua điện	through Power Purchase Agreements	sign/negotiate PPA
carbon intensity	noun phrase	/ˈkɑːbən ɪnˈtensəti/	cường độ carbon	monitor carbon intensity	reduce carbon intensity

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
paradigm shift	noun phrase	/ˈpærədaɪm ʃɪft/	chuyển đổi mô hình	fundamental paradigm shifts	undergo a paradigm shift
decarbonization	noun	/diːˌkɑːbənaɪˈzeɪʃn/	khử carbon	global decarbonization objectives	achieve decarbonization
disruptive innovation	noun phrase	/dɪsˈrʌptɪv ˌɪnəˈveɪʃn/	đổi mới đột phá	require disruptive innovations	drive disruptive innovation
thermodynamic	adj	/ˌθɜːməʊdaɪˈnæmɪk/	thuộc nhiệt động lực học	thermodynamic principles	thermodynamic efficiency/laws
reversible computing	noun phrase	/rɪˈvɜːsəbl kəmˈpjuːtɪŋ/	điện toán đảo ngược	Reversible computing architectures	develop reversible computing
logically irreversible	adj phrase	/ˈlɒdʒɪkli ˌɪrɪˈvɜːsəbl/	không thể đảo ngược về mặt logic	Conventional computing is logically irreversible	logically irreversible process
prototype facility	noun phrase	/ˈprəʊtətaɪp fəˈsɪləti/	cơ sở nguyên mẫu	deployed a prototype facility	test prototype facility
failure rate	noun phrase	/ˈfeɪljər reɪt/	tỷ lệ hỏng hóc	failure rate was one-eighth	reduce failure rate
neuromorphic computing	noun phrase	/ˌnjʊərəʊˈmɔːfɪk kəmˈpjuːtɪŋ/	điện toán thần kinh	neuromorphic computing approach	advance neuromorphic computing
distributed ledger	noun phrase	/dɪˈstrɪbjuːtɪd ˈledʒər/	sổ cái phân tán	distributed ledger technologies	implement distributed ledger
blockchain	noun	/ˈblɒktʃeɪn/	chuỗi khối	blockchain systems	adopt blockchain technology
consensus mechanism	noun phrase	/kənˈsensəs ˈmekənɪzəm/	cơ chế đồng thuận	proof-of-work consensus mechanism	alternative consensus mechanism
peer-to-peer network	noun phrase	/pɪər tə pɪər ˈnetwɜːk/	mạng ngang hàng	leveraging peer-to-peer networks	establish peer-to-peer network
cryogenic	adj	/ˌkraɪəˈdʒenɪk/	siêu lạnh	Cryogenic computing	cryogenic temperatures/systems
superconductivity	noun	/ˌsuːpəkɒndʌkˈtɪvəti/	siêu dẫn	exploits superconductivity	achieve superconductivity
carbon capture	noun phrase	/ˈkɑːbən ˈkæptʃər/	thu giữ carbon	carbon capture and storage technologies	implement carbon capture
carbon-negative	adj	/ˈkɑːbən ˈneɡətɪv/	âm carbon	exploring carbon-negative operations	become carbon-negative
regulatory framework	noun phrase	/ˈreɡjələtəri ˈfreɪmwɜːk/	khung pháp lý	Regulatory frameworks will prove influential	establish regulatory framework
thermodynamic limit	noun phrase	/ˌθɜːməʊdaɪˈnæmɪk ˈlɪmɪt/	giới hạn nhiệt động	encounter planetary thermal limits	approach thermodynamic limit

Kết Bài

Chủ đề “The rise of energy-efficient data centers” không chỉ phản ánh xu hướng công nghệ đương đại mà còn là chủ đề quan trọng thường xuyên xuất hiện trong các kỳ thi IELTS Reading. Bộ đề thi mẫu này đã cung cấp cho bạn trải nghiệm hoàn chỉnh với ba passages độ khó tăng dần, từ Easy (Band 5.0-6.5) qua Medium (Band 6.0-7.5) đến Hard (Band 7.0-9.0), giống như trong kỳ thi thật.

Qua 40 câu hỏi đa dạng bao gồm True/False/Not Given, Multiple Choice, Matching Headings, Summary Completion và các dạng khác, bạn đã được rèn luyện kỹ năng xác định thông tin, paraphrase, và quản lý thời gian. Đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin và kỹ thuật làm bài sẽ giúp bạn hiểu rõ cách tiếp cận từng dạng câu hỏi một cách bài bản.

Bộ từ vựng chuyên ngành được tổng hợp theo từng passage, bao gồm phiên âm, nghĩa tiếng Việt, ví dụ và collocations, sẽ là tài liệu quý giá giúp bạn mở rộng vốn từ học thuật. Hãy lưu lại những từ vựng này và thực hành sử dụng chúng trong các bài viết của bạn.

Để đạt kết quả cao trong IELTS Reading, hãy thường xuyên luyện tập với các đề thi mẫu chất lượng như thế này, phân tích kỹ đáp án, và không ngừng cải thiện tốc độ đọc cũng như khả năng hiểu sâu văn bản học thuật. Chúc bạn ôn tập hiệu quả và đạt band điểm mong muốn trong kỳ thi IELTS sắp tới!