Đề Thi Phân Loại Học Sinh Giỏi (Đề Số 03 — Mức 9.5+) Giữa Học Kỳ 1 Tiếng Anh 12 — Global Success Units 1–3: Phonetics, Present/Past Tenses & Reading

Câu 1: Read the following language focus item from the Grade 12 English test (Giữa HK1 • Mức Vận dụng cao, Set 396.1): Read the following passage and mark the letter A, B, C, or D to indicate the correct word or phrase that best fits each of the numbered blanks from 18 to 22: The concept of the workplace has undergone a revolutionary shift in recent years. Enabled by rapid advances in cloud computing and high-speed internet, millions of employees now work remotely. This transformation has given rise to the 'digital nomad' lifestyle, where professionals travel the globe while (18)______ their regular jobs. Advocates highlight that remote arrangements offer greater flexibility and eliminate grueling daily commutes. Workers can design environments that (19)______ their productivity and maintain a superior work-life balance. Furthermore, corporations are able to tap into global talent pools without geographic restrictions. However, remote work also presents formidable challenges. Isolation and the blurring of boundaries between professional duties and personal time can trigger severe burnout. (20)______, companies must formulate deliberate policies to safeguard employee wellbeing. (21)______ effective communication tools like video conferencing are widespread, they cannot entirely replace spontaneous human interaction. In the long run, hybrid models that blend remote efficiency with periodic in-person collaboration appear to be the most (22)______ solution for sustainable organizational success. Question 21: Mark the correct option for blank (21):

Câu 2: Complete the communicative and lexical task for Grade 12 English (Giữa HK1 • Mức Vận dụng cao — Code 396, Question 2): Read the following passage and mark the letter A, B, C, or D to indicate the correct word or phrase that best fits each of the numbered blanks from 18 to 22: The concept of the workplace has undergone a revolutionary shift in recent years. Enabled by rapid advances in cloud computing and high-speed internet, millions of employees now work remotely. This transformation has given rise to the 'digital nomad' lifestyle, where professionals travel the globe while (18)______ their regular jobs. Advocates highlight that remote arrangements offer greater flexibility and eliminate grueling daily commutes. Workers can design environments that (19)______ their productivity and maintain a superior work-life balance. Furthermore, corporations are able to tap into global talent pools without geographic restrictions. However, remote work also presents formidable challenges. Isolation and the blurring of boundaries between professional duties and personal time can trigger severe burnout. (20)______, companies must formulate deliberate policies to safeguard employee wellbeing. (21)______ effective communication tools like video conferencing are widespread, they cannot entirely replace spontaneous human interaction. In the long run, hybrid models that blend remote efficiency with periodic in-person collaboration appear to be the most (22)______ solution for sustainable organizational success. Question 22: Mark the correct option for blank (22):

Câu 3: In the Grade 12 Global Success English assessment (Giữa HK1 • Mức Vận dụng cao — Paper 396, Item 3): Read the following passage and mark the letter A, B, C, or D to indicate the correct answer to each of the questions from 23 to 30: Artificial Intelligence (AI) is ushering in an unprecedented era in medical science, fundamentally revolutionizing how diseases are detected, monitored, and treated. Machine learning algorithms, trained on vast repositories of medical imaging data, can now identify pathological anomalies with a degree of precision that rivals, and in some instances surpasses, experienced radiologists. From pinpointing early-stage malignant tumors in mammograms to recognizing subtle retinal signs of diabetic retinopathy, AI diagnostic tools are serving as invaluable assistants to physicians worldwide. Beyond diagnostic imaging, AI is dramatically accelerating the drug discovery trajectory. Traditionally, developing a novel pharmaceutical compound requires over a decade of painstaking laboratory synthesis and billions of dollars in clinical trials. Today, generative molecular AI models can simulate biochemical interactions within hours, predicting how synthetic proteins bind to virus receptors. This predictive capability reduces the initial exploratory phase from years to mere months, proving pivotal during global health emergencies. Moreover, the emergence of precision medicine tailored to individual genetic profiles is gaining remarkable momentum through AI analysis. By decoding genomic sequencing alongside lifestyle and electronic health records, predictive systems can determine which cancer therapies will yield the highest efficacy for a specific patient while minimizing detrimental side effects. This tailored approach departs from traditional 'one-size-fits-all' healthcare models. Nevertheless, the deployment of clinical AI is not devoid of ethical and technical dilemmas. Algorithmic bias represents a grave concern: if an AI system is trained predominantly on demographic data from affluent populations, its diagnostic reliability may decline precipitously when applied to historically underserved ethnic groups. Additionally, questions regarding data privacy and liability—who bears legal responsibility when an algorithmic recommendation leads to medical injury—remain fiercely contested. As AI tools integrate deeper into hospital workflows, establishing robust regulatory frameworks and ensuring human physicians retain final decision-making authority will be paramount. Question 23: What is the best title for the passage?

Câu 4: Read the following language focus item from the Grade 12 English test (Giữa HK1 • Mức Vận dụng cao, Set 396.4): Read the following passage and mark the letter A, B, C, or D to indicate the correct answer to each of the questions from 23 to 30: Artificial Intelligence (AI) is ushering in an unprecedented era in medical science, fundamentally revolutionizing how diseases are detected, monitored, and treated. Machine learning algorithms, trained on vast repositories of medical imaging data, can now identify pathological anomalies with a degree of precision that rivals, and in some instances surpasses, experienced radiologists. From pinpointing early-stage malignant tumors in mammograms to recognizing subtle retinal signs of diabetic retinopathy, AI diagnostic tools are serving as invaluable assistants to physicians worldwide. Beyond diagnostic imaging, AI is dramatically accelerating the drug discovery trajectory. Traditionally, developing a novel pharmaceutical compound requires over a decade of painstaking laboratory synthesis and billions of dollars in clinical trials. Today, generative molecular AI models can simulate biochemical interactions within hours, predicting how synthetic proteins bind to virus receptors. This predictive capability reduces the initial exploratory phase from years to mere months, proving pivotal during global health emergencies. Moreover, the emergence of precision medicine tailored to individual genetic profiles is gaining remarkable momentum through AI analysis. By decoding genomic sequencing alongside lifestyle and electronic health records, predictive systems can determine which cancer therapies will yield the highest efficacy for a specific patient while minimizing detrimental side effects. This tailored approach departs from traditional 'one-size-fits-all' healthcare models. Nevertheless, the deployment of clinical AI is not devoid of ethical and technical dilemmas. Algorithmic bias represents a grave concern: if an AI system is trained predominantly on demographic data from affluent populations, its diagnostic reliability may decline precipitously when applied to historically underserved ethnic groups. Additionally, questions regarding data privacy and liability—who bears legal responsibility when an algorithmic recommendation leads to medical injury—remain fiercely contested. As AI tools integrate deeper into hospital workflows, establishing robust regulatory frameworks and ensuring human physicians retain final decision-making authority will be paramount. Question 24: According to paragraph 1, what can machine learning algorithms do with high accuracy?

Câu 5: Complete the communicative and lexical task for Grade 12 English (Giữa HK1 • Mức Vận dụng cao — Code 396, Question 5): Read the following passage and mark the letter A, B, C, or D to indicate the correct answer to each of the questions from 23 to 30: Artificial Intelligence (AI) is ushering in an unprecedented era in medical science, fundamentally revolutionizing how diseases are detected, monitored, and treated. Machine learning algorithms, trained on vast repositories of medical imaging data, can now identify pathological anomalies with a degree of precision that rivals, and in some instances surpasses, experienced radiologists. From pinpointing early-stage malignant tumors in mammograms to recognizing subtle retinal signs of diabetic retinopathy, AI diagnostic tools are serving as invaluable assistants to physicians worldwide. Beyond diagnostic imaging, AI is dramatically accelerating the drug discovery trajectory. Traditionally, developing a novel pharmaceutical compound requires over a decade of painstaking laboratory synthesis and billions of dollars in clinical trials. Today, generative molecular AI models can simulate biochemical interactions within hours, predicting how synthetic proteins bind to virus receptors. This predictive capability reduces the initial exploratory phase from years to mere months, proving pivotal during global health emergencies. Moreover, the emergence of precision medicine tailored to individual genetic profiles is gaining remarkable momentum through AI analysis. By decoding genomic sequencing alongside lifestyle and electronic health records, predictive systems can determine which cancer therapies will yield the highest efficacy for a specific patient while minimizing detrimental side effects. This tailored approach departs from traditional 'one-size-fits-all' healthcare models. Nevertheless, the deployment of clinical AI is not devoid of ethical and technical dilemmas. Algorithmic bias represents a grave concern: if an AI system is trained predominantly on demographic data from affluent populations, its diagnostic reliability may decline precipitously when applied to historically underserved ethnic groups. Additionally, questions regarding data privacy and liability—who bears legal responsibility when an algorithmic recommendation leads to medical injury—remain fiercely contested. As AI tools integrate deeper into hospital workflows, establishing robust regulatory frameworks and ensuring human physicians retain final decision-making authority will be paramount. Question 25: The word 'accelerating' in paragraph 2 is closest in meaning to:

Câu 6: In the Grade 12 Global Success English assessment (Giữa HK1 • Mức Vận dụng cao — Paper 396, Item 6): Read the following passage and mark the letter A, B, C, or D to indicate the correct answer to each of the questions from 23 to 30: Artificial Intelligence (AI) is ushering in an unprecedented era in medical science, fundamentally revolutionizing how diseases are detected, monitored, and treated. Machine learning algorithms, trained on vast repositories of medical imaging data, can now identify pathological anomalies with a degree of precision that rivals, and in some instances surpasses, experienced radiologists. From pinpointing early-stage malignant tumors in mammograms to recognizing subtle retinal signs of diabetic retinopathy, AI diagnostic tools are serving as invaluable assistants to physicians worldwide. Beyond diagnostic imaging, AI is dramatically accelerating the drug discovery trajectory. Traditionally, developing a novel pharmaceutical compound requires over a decade of painstaking laboratory synthesis and billions of dollars in clinical trials. Today, generative molecular AI models can simulate biochemical interactions within hours, predicting how synthetic proteins bind to virus receptors. This predictive capability reduces the initial exploratory phase from years to mere months, proving pivotal during global health emergencies. Moreover, the emergence of precision medicine tailored to individual genetic profiles is gaining remarkable momentum through AI analysis. By decoding genomic sequencing alongside lifestyle and electronic health records, predictive systems can determine which cancer therapies will yield the highest efficacy for a specific patient while minimizing detrimental side effects. This tailored approach departs from traditional 'one-size-fits-all' healthcare models. Nevertheless, the deployment of clinical AI is not devoid of ethical and technical dilemmas. Algorithmic bias represents a grave concern: if an AI system is trained predominantly on demographic data from affluent populations, its diagnostic reliability may decline precipitously when applied to historically underserved ethnic groups. Additionally, questions regarding data privacy and liability—who bears legal responsibility when an algorithmic recommendation leads to medical injury—remain fiercely contested. As AI tools integrate deeper into hospital workflows, establishing robust regulatory frameworks and ensuring human physicians retain final decision-making authority will be paramount. Question 26: How has AI benefited the pharmaceutical drug discovery process according to paragraph 2?

Câu 7: Read the following language focus item from the Grade 12 English test (Giữa HK1 • Mức Vận dụng cao, Set 396.7): Read the following passage and mark the letter A, B, C, or D to indicate the correct answer to each of the questions from 23 to 30: Artificial Intelligence (AI) is ushering in an unprecedented era in medical science, fundamentally revolutionizing how diseases are detected, monitored, and treated. Machine learning algorithms, trained on vast repositories of medical imaging data, can now identify pathological anomalies with a degree of precision that rivals, and in some instances surpasses, experienced radiologists. From pinpointing early-stage malignant tumors in mammograms to recognizing subtle retinal signs of diabetic retinopathy, AI diagnostic tools are serving as invaluable assistants to physicians worldwide. Beyond diagnostic imaging, AI is dramatically accelerating the drug discovery trajectory. Traditionally, developing a novel pharmaceutical compound requires over a decade of painstaking laboratory synthesis and billions of dollars in clinical trials. Today, generative molecular AI models can simulate biochemical interactions within hours, predicting how synthetic proteins bind to virus receptors. This predictive capability reduces the initial exploratory phase from years to mere months, proving pivotal during global health emergencies. Moreover, the emergence of precision medicine tailored to individual genetic profiles is gaining remarkable momentum through AI analysis. By decoding genomic sequencing alongside lifestyle and electronic health records, predictive systems can determine which cancer therapies will yield the highest efficacy for a specific patient while minimizing detrimental side effects. This tailored approach departs from traditional 'one-size-fits-all' healthcare models. Nevertheless, the deployment of clinical AI is not devoid of ethical and technical dilemmas. Algorithmic bias represents a grave concern: if an AI system is trained predominantly on demographic data from affluent populations, its diagnostic reliability may decline precipitously when applied to historically underserved ethnic groups. Additionally, questions regarding data privacy and liability—who bears legal responsibility when an algorithmic recommendation leads to medical injury—remain fiercely contested. As AI tools integrate deeper into hospital workflows, establishing robust regulatory frameworks and ensuring human physicians retain final decision-making authority will be paramount. Question 27: The word 'efficacy' in paragraph 3 is closest in meaning to:

Câu 8: Complete the communicative and lexical task for Grade 12 English (Giữa HK1 • Mức Vận dụng cao — Code 396, Question 8): Read the following passage and mark the letter A, B, C, or D to indicate the correct answer to each of the questions from 23 to 30: Artificial Intelligence (AI) is ushering in an unprecedented era in medical science, fundamentally revolutionizing how diseases are detected, monitored, and treated. Machine learning algorithms, trained on vast repositories of medical imaging data, can now identify pathological anomalies with a degree of precision that rivals, and in some instances surpasses, experienced radiologists. From pinpointing early-stage malignant tumors in mammograms to recognizing subtle retinal signs of diabetic retinopathy, AI diagnostic tools are serving as invaluable assistants to physicians worldwide. Beyond diagnostic imaging, AI is dramatically accelerating the drug discovery trajectory. Traditionally, developing a novel pharmaceutical compound requires over a decade of painstaking laboratory synthesis and billions of dollars in clinical trials. Today, generative molecular AI models can simulate biochemical interactions within hours, predicting how synthetic proteins bind to virus receptors. This predictive capability reduces the initial exploratory phase from years to mere months, proving pivotal during global health emergencies. Moreover, the emergence of precision medicine tailored to individual genetic profiles is gaining remarkable momentum through AI analysis. By decoding genomic sequencing alongside lifestyle and electronic health records, predictive systems can determine which cancer therapies will yield the highest efficacy for a specific patient while minimizing detrimental side effects. This tailored approach departs from traditional 'one-size-fits-all' healthcare models. Nevertheless, the deployment of clinical AI is not devoid of ethical and technical dilemmas. Algorithmic bias represents a grave concern: if an AI system is trained predominantly on demographic data from affluent populations, its diagnostic reliability may decline precipitously when applied to historically underserved ethnic groups. Additionally, questions regarding data privacy and liability—who bears legal responsibility when an algorithmic recommendation leads to medical injury—remain fiercely contested. As AI tools integrate deeper into hospital workflows, establishing robust regulatory frameworks and ensuring human physicians retain final decision-making authority will be paramount. Question 28: The word 'its' in paragraph 4 refers to: