This is the Vocab24 daily quiz of 4 June 2025, the same 30 questions the app served that day, on the day's vocabulary and editorial. One mark for a right answer, minus 0.25 for a wrong one; the explanation opens as soon as you tap.
Out of the given alternatives select the alternative which best expresses the meaning of given word.
Truce
Out of the given alternatives select the alternative which best expresses the meaning of given word.
Incidents
Out of the given alternatives select the alternative which best expresses the meaning of given word.
Hampering
Out of the given alternatives select the alternative which best expresses the meaning of given word.
Proposal
Out of the given alternatives select the word opposite in meaning to the given word.
Campaign
Out of the given alternatives select the word opposite in meaning to the given word.
Condemned
Out of the given alternatives select the word opposite in meaning to the given word.
Delegations
Out of the given alternatives select the word opposite in meaning to the given word.
Occupied
Out of given alternatives, choose the word which can be substituted for the given words/ sentence.
An agreement between enemies or opponents to stop fighting or arguing for a certain time.
Out of given alternatives, choose the word which can be substituted for the given words/ sentence.
Hinder or impede the movement or progress of.
Out of given alternatives, choose the word which can be substituted for the given words/ sentence.
Express complete disapproval of
Out of given alternatives, choose the word which can be substituted for the given words/ sentence.
Regarded as likely.
A statement with one blank is given below. Choose the set of words from the given options which can be used to fill the given blank.
Listen, a nice song ____
Explanation: Sentence is in present continuous tense with passive voice. <br> Rule : <br> Subject + is/am/are + being + V3 + other agents. <br> Listen, a nice song is being sung.
A statement with one blank is given below. Choose the set of words from the given options which can be used to fill the given blank.
Listen, an announcement ____ to cancel all the flights due to heavy ice-fall.
Explanation: Sentence is in present continuous tense with passive voice. <br> Rule : <br> Subject + is/am/are + being + V3 + other agents. <br> Listen, an announcement is being made to cancel all the flights due to heavy ice-fall.
A statement with one blank is given below. Choose the set of words from the given options which can be used to fill the given blank.
Listen, she is talking ____ you.
Explanation: Listen, she is talking about you.
Out of given alternatives select the option which best expresses the meaning of given idiom/ phrase.
He is a queer fish, I have failed to understand him.
Explanation: queer fish: a person whose behaviour seems strange or unusual.
Out of given alternatives select the option which best expresses the meaning of given idiom/ phrase.
The new C.M stuck his neck out today and promised 10 kgs free wheat a month for all rural families.
Explanation: stuck his neck out: To personally assume or expose oneself to some risk, danger, or responsibility.
Out of given alternatives select the option which best expresses the meaning of given idiom/ phrase.
He is a wolf in sheep's clothing.
Explanation: A wolf in sheep's clothing: a person or thing that appears friendly or harmless but is really hostile.
Out of given alternatives select the word which is correctly spelt.
Choose the Correct Spelling.
Out of given alternatives select the word which is correctly spelt.
Choose the Correct Spelling.
Out of given alternatives select the word which is correctly spelt.
Choose the Correct Spelling.
Out of given alternatives select the word which is correctly spelt.
Choose the Correct Spelling.
Which of phrases given below each sentence should replace the phrase printed in bold type to make the grammatically correct? If the sentence is correct as it is, mark 'd' as the answer.
Of the numerous events which helped bring about the US Civil War, (a)/ perhaps the most decisive was the election of Lincoln, (b)/ which finally forced the disagreement to its crisis. (c)/ No error (d)
Which of phrases given below each sentence should replace the phrase printed in bold type to make the grammatically correct? If the sentence is correct as it is, mark 'd' as the answer.
Though the American political system differs with the British (a)/ in many respect the fundamental legal protections it affords the individual (b)/ are derived almost entirely from the British model. (c)/ No error (d)
Explanation: differs from
Which of phrases given below each sentence should replace the phrase printed in bold type to make the grammatically correct? If the sentence is correct as it is, mark 'd' as the answer.
While one part of the TV programme carried the football game, (a)/ the other part (b)/ shows the training of the team. (c)/ No error (d)
Explanation: showed
Direction: Study the following information carefully and answer the question given below. <br><br> In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors - agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It's an idea with great potential: India has 30 million farmers with smartphones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. <br><br> However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a data-guzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like 'deep learning', attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. <br><br> In fact, the lack of data means that deep learning doesn't work for all companies in India. One example is Climate-Connect, a Delhi-based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. <br><br> But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn't worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. <br><br> Another problem for AI firms today is finding the right people. NITI Aayog's report has bleak news: only about 50 Indian scientists carry out 'serious research' and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation.
Which of the following is/are synonym/s of the word bleak? <br> I. Depressing<br> II. Dismal<br> III. Congenial<br> IV. Stark
Explanation: Bleak means barren/depressing. <br> I, II and IV are synonyms. <br> Congenial means friendly and is the antonym. <br> Hence, I, II and IV are correct. <br> Option C is correct.
Direction: Study the following information carefully and answer the question given below. <br><br> In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors - agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It's an idea with great potential: India has 30 million farmers with smartphones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. <br><br> However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a data-guzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like 'deep learning', attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. <br><br> In fact, the lack of data means that deep learning doesn't work for all companies in India. One example is Climate-Connect, a Delhi-based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. <br><br> But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn't worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. <br><br> Another problem for AI firms today is finding the right people. NITI Aayog's report has bleak news: only about 50 Indian scientists carry out 'serious research' and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation.
Which of the following is/are antonym/s of the word sparse? <br> I. Scant<br> II. Few<br> III. Sporadic<br> IV. Abundant
Explanation: Sparse means scanty/ in short supply. <br> I, II and III are synonyms and incorrect. <br> Only IV- abundant- is the antonym here and means plenty. <br> Hence, option B is correct.
Direction: Study the following information carefully and answer the question given below. <br><br> In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors - agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It's an idea with great potential: India has 30 million farmers with smartphones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. <br><br> However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a data-guzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like 'deep learning', attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. <br><br> In fact, the lack of data means that deep learning doesn't work for all companies in India. One example is Climate-Connect, a Delhi-based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. <br><br> But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn't worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. <br><br> Another problem for AI firms today is finding the right people. NITI Aayog's report has bleak news: only about 50 Indian scientists carry out 'serious research' and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation.
What can be some steps that can be taken by India to improve its AI capabilities? <br> I. The government must collect and digitize data it has access to due to running numerous schemes. <br> II. Set up institutes to churn out more skilled people in this field. <br> III. There should be adequate funding and also fixed deadlines to gauge performance.
Explanation: All of the statements are correct as all state valid ways of improving India's AI capabilities. <br> Hence, option E is correct.
Direction: Study the following information carefully and answer the question given below. <br><br> In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors - agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It's an idea with great potential: India has 30 million farmers with smartphones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. <br><br> However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a data-guzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like 'deep learning', attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. <br><br> In fact, the lack of data means that deep learning doesn't work for all companies in India. One example is Climate-Connect, a Delhi-based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. <br><br> But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn't worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. <br><br> Another problem for AI firms today is finding the right people. NITI Aayog's report has bleak news: only about 50 Indian scientists carry out 'serious research' and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation.
Which of the following weakens the argument for using more of AI powered tools in the future in India? <br> I. The AI sector uses a tremendous amount of electricity so as to process huge amounts of data which is not sustainable. <br> II. It is tough to collect, validate, standardize, correlate, archive and distribute AI-relevant data and make it accessible to organizations, people and systems. <br> III. Although AI will create more jobs than it would destroy.
Explanation: Statement II is incorrect as it may be tough but not impossible. With proper planning and a scientific approach, this issue can be resolved. <br> Statement III is incorrect as it strengthens the argument for AI. <br> Statement I is correct. It weakens the argument as using a huge amount of electricity is not sustainable in the long run. <br> Hence, option A is correct.
Direction: Study the following information carefully and answer the question given below. <br><br> In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse. AI is the use of computers to make decisions that are normally made by humans. Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors - agriculture, healthcare, education, smart cities and infrastructure, and transport. In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations. It's an idea with great potential: India has 30 million farmers with smartphones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution. <br><br> However, there are formidable obstacles. AI start-ups already offer some solutions, but the challenge lies in scaling these to cover the entire value chain, as NITI Aayog envisions. The first problem is data. Machine learning, the set of technologies used to create AI, is a data-guzzling monster. It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like 'deep learning', attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning. Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today. <br><br> In fact, the lack of data means that deep learning doesn't work for all companies in India. One example is Climate-Connect, a Delhi-based firm, which uses AI to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such AI will play a central role in power planning. <br><br> But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation. Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%. While deep learning can boost accuracy for operations such as Climate-Connect, it hasn't worked very well in the Indian scenario, says Nitin Tanwar, cofounder of the firm. <br><br> Another problem for AI firms today is finding the right people. NITI Aayog's report has bleak news: only about 50 Indian scientists carry out 'serious research' and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning. A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians. How does this skill gap impact companies? To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help. This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation.
Which of the following statements weakens the argument about using 'Open Libraries' of machine learning code?
Explanation: Options A and B talk about advantages of open libraries and are incorrect. <br> Option C is incorrect as it simply states that it is possible to understand machine learning without needing mathematics. <br> Only option D fits in. If true, this weakens the point of using Open Libraries. <br> Hence, option D is correct.
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