Thi thử tốt nghiệp THPT · môn Tiếng Anh

Khảo sát chất lượng học sinh lớp 12 THPT đợt 2 · Tiếng Anh · Sở GD&ĐT Phú Thọ · Năm học 2025-2026

Đề thi khảo sát chất lượng học sinh lớp 12 THPT đợt 2, năm học 2025–2026, môn Tiếng Anh của Sở GD&ĐT Phú Thọ. Đề gồm 40 câu trắc nghiệm, thời gian làm bài 50 phút.

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Phần I · Trắc nghiệm nhiều phương án
40 câu
10 điểm

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3 câu đầu của đề. Vào phần làm thử để trả lời và biết ngay mình đúng hay sai.

Phần thông tin chung
Read the passage and mark the letter A, B, C or D on your answer sheet to indicate the best answer to each of the following questions from 1 to 8.

For much of the 20th century, AI struggled not from a lack of ambition, but because available hardware wasn't powerful enough. Early systems hit limits on processing speed and memory, contributing to "AI winters" as progress stalled and funding dried up. Today, this problem is largely resolved. AI models are now trained on specialized chips in vast data centers. Compute, which used to be the main bottleneck, can now simply be purchased. Companies like Nvidia mass-produce powerful graphics processing units (GPUs) — originally designed for gaming but perfectly suited to AI calculations. What holds AI back now? The physical limit of electricity.

Modern AI models don't just train once; they operate continuously, powering chatbots, search engines, and autonomous agents. This shift has made AI a constant, large-scale electricity consumer. According to Sampsa Samila of IESE Business School, "the core issue is not a shortage of energy in absolute terms, but rather the availability of reliable, firm capacity at the right place and the right time".

Predictions for AI energy consumption show this strain. The International Energy Agency (IEA) projects data centers will consume more than twice as much electricity by the decade's end. In parts of the U.S., data center power usage already rivals heavy industry.

How AI is used matters as much as how it is trained. Training large models consumes immense power but occurs infrequently. What is growing faster is the everyday work of models responding to users. Samila notes that newer "reasoning" AI systems, which deliberate longer, push energy demands into everyday operations rather than occasional large training runs.

(Adapted from: https://www.livescience.com)
*Read the passage and mark the letter A, B, C or D on your answer sheet to indicate the best answer to each of the following questions from 1 to 8.* For much of the 20th century, AI struggled not from a lack of ambition, but because available hardware wasn't powerful enough. Early systems hit limits on processing speed and memory, contributing to "AI winters" as progress **stalled** and funding dried up. Today, this problem is largely resolved. AI models are now trained on specialized chips in vast data centers. Compute, which used to be the main **bottleneck**, can now simply be purchased. Companies like Nvidia mass-produce powerful graphics processing units (GPUs) — originally designed for gaming but perfectly suited to AI calculations. What holds AI back now? The physical limit of electricity. Modern AI models don't just train once; they operate continuously, powering chatbots, search engines, and autonomous agents. **This shift** has made AI a constant, large-scale electricity consumer. According to Sampsa Samila of IESE Business School, "the core issue is not a shortage of energy in absolute terms, but rather the availability of reliable, firm capacity at the right place and the right time". Predictions for AI energy consumption show this strain. The International Energy Agency (IEA) projects data centers will consume more than twice as much electricity by the decade's end. In parts of the U.S., data center power usage already rivals heavy industry. How AI is used matters as much as how it is trained. Training large models consumes immense power but occurs infrequently. What is growing faster is the everyday work of models responding to users. Samila notes that newer "reasoning" AI systems, which deliberate longer, push energy demands into everyday operations rather than occasional large training runs. *(Adapted from: https://www.livescience.com)*
Câu 1
Đọc hiểu · chi tiết không được nhắc0,25 điểm
Question 1: Which of the following is NOT mentioned in the passage as a factor that contributed to AI's slow progress in the 20th century?
Insufficient processing speed
Limited memory capacity
Lack of research funding after stalled progress
Absence of skilled AI engineers and scientists
Phần thông tin chung
Read the passage and mark the letter A, B, C or D on your answer sheet to indicate the best answer to each of the following questions from 1 to 8.

For much of the 20th century, AI struggled not from a lack of ambition, but because available hardware wasn't powerful enough. Early systems hit limits on processing speed and memory, contributing to "AI winters" as progress stalled and funding dried up. Today, this problem is largely resolved. AI models are now trained on specialized chips in vast data centers. Compute, which used to be the main bottleneck, can now simply be purchased. Companies like Nvidia mass-produce powerful graphics processing units (GPUs) — originally designed for gaming but perfectly suited to AI calculations. What holds AI back now? The physical limit of electricity.

Modern AI models don't just train once; they operate continuously, powering chatbots, search engines, and autonomous agents. This shift has made AI a constant, large-scale electricity consumer. According to Sampsa Samila of IESE Business School, "the core issue is not a shortage of energy in absolute terms, but rather the availability of reliable, firm capacity at the right place and the right time".

Predictions for AI energy consumption show this strain. The International Energy Agency (IEA) projects data centers will consume more than twice as much electricity by the decade's end. In parts of the U.S., data center power usage already rivals heavy industry.

How AI is used matters as much as how it is trained. Training large models consumes immense power but occurs infrequently. What is growing faster is the everyday work of models responding to users. Samila notes that newer "reasoning" AI systems, which deliberate longer, push energy demands into everyday operations rather than occasional large training runs.

(Adapted from: https://www.livescience.com)
*Read the passage and mark the letter A, B, C or D on your answer sheet to indicate the best answer to each of the following questions from 1 to 8.* For much of the 20th century, AI struggled not from a lack of ambition, but because available hardware wasn't powerful enough. Early systems hit limits on processing speed and memory, contributing to "AI winters" as progress **stalled** and funding dried up. Today, this problem is largely resolved. AI models are now trained on specialized chips in vast data centers. Compute, which used to be the main **bottleneck**, can now simply be purchased. Companies like Nvidia mass-produce powerful graphics processing units (GPUs) — originally designed for gaming but perfectly suited to AI calculations. What holds AI back now? The physical limit of electricity. Modern AI models don't just train once; they operate continuously, powering chatbots, search engines, and autonomous agents. **This shift** has made AI a constant, large-scale electricity consumer. According to Sampsa Samila of IESE Business School, "the core issue is not a shortage of energy in absolute terms, but rather the availability of reliable, firm capacity at the right place and the right time". Predictions for AI energy consumption show this strain. The International Energy Agency (IEA) projects data centers will consume more than twice as much electricity by the decade's end. In parts of the U.S., data center power usage already rivals heavy industry. How AI is used matters as much as how it is trained. Training large models consumes immense power but occurs infrequently. What is growing faster is the everyday work of models responding to users. Samila notes that newer "reasoning" AI systems, which deliberate longer, push energy demands into everyday operations rather than occasional large training runs. *(Adapted from: https://www.livescience.com)*
Câu 2
Đọc hiểu · từ trái nghĩa0,25 điểm
Question 2: The word "stalled" in paragraph 1 is OPPOSITE in meaning to ______?
prevented
accelerated
paused
frozen
Phần thông tin chung
Read the passage and mark the letter A, B, C or D on your answer sheet to indicate the best answer to each of the following questions from 1 to 8.

For much of the 20th century, AI struggled not from a lack of ambition, but because available hardware wasn't powerful enough. Early systems hit limits on processing speed and memory, contributing to "AI winters" as progress stalled and funding dried up. Today, this problem is largely resolved. AI models are now trained on specialized chips in vast data centers. Compute, which used to be the main bottleneck, can now simply be purchased. Companies like Nvidia mass-produce powerful graphics processing units (GPUs) — originally designed for gaming but perfectly suited to AI calculations. What holds AI back now? The physical limit of electricity.

Modern AI models don't just train once; they operate continuously, powering chatbots, search engines, and autonomous agents. This shift has made AI a constant, large-scale electricity consumer. According to Sampsa Samila of IESE Business School, "the core issue is not a shortage of energy in absolute terms, but rather the availability of reliable, firm capacity at the right place and the right time".

Predictions for AI energy consumption show this strain. The International Energy Agency (IEA) projects data centers will consume more than twice as much electricity by the decade's end. In parts of the U.S., data center power usage already rivals heavy industry.

How AI is used matters as much as how it is trained. Training large models consumes immense power but occurs infrequently. What is growing faster is the everyday work of models responding to users. Samila notes that newer "reasoning" AI systems, which deliberate longer, push energy demands into everyday operations rather than occasional large training runs.

(Adapted from: https://www.livescience.com)
*Read the passage and mark the letter A, B, C or D on your answer sheet to indicate the best answer to each of the following questions from 1 to 8.* For much of the 20th century, AI struggled not from a lack of ambition, but because available hardware wasn't powerful enough. Early systems hit limits on processing speed and memory, contributing to "AI winters" as progress **stalled** and funding dried up. Today, this problem is largely resolved. AI models are now trained on specialized chips in vast data centers. Compute, which used to be the main **bottleneck**, can now simply be purchased. Companies like Nvidia mass-produce powerful graphics processing units (GPUs) — originally designed for gaming but perfectly suited to AI calculations. What holds AI back now? The physical limit of electricity. Modern AI models don't just train once; they operate continuously, powering chatbots, search engines, and autonomous agents. **This shift** has made AI a constant, large-scale electricity consumer. According to Sampsa Samila of IESE Business School, "the core issue is not a shortage of energy in absolute terms, but rather the availability of reliable, firm capacity at the right place and the right time". Predictions for AI energy consumption show this strain. The International Energy Agency (IEA) projects data centers will consume more than twice as much electricity by the decade's end. In parts of the U.S., data center power usage already rivals heavy industry. How AI is used matters as much as how it is trained. Training large models consumes immense power but occurs infrequently. What is growing faster is the everyday work of models responding to users. Samila notes that newer "reasoning" AI systems, which deliberate longer, push energy demands into everyday operations rather than occasional large training runs. *(Adapted from: https://www.livescience.com)*
Câu 3
Đọc hiểu · đại từ quy chiếu0,25 điểm
Question 3: In paragraph 2, the phrase "this shift" refers to ______.
the development of more powerful GPU chips by companies such as Nvidia and AMD for AI use
the change from AI training occasionally to AI operating continuously across multiple applications
the transition from AI being used for research to being used commercially
the move from running AI computations on local machines to large-scale cloud data centers

Nộp bài xong là có đáp án và lời giải chi tiết cho từng câu.

Cấu trúc đề thi

Phần I · Trắc nghiệm nhiều phương án

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