This is the Vocab24 daily quiz of 11 December 2025, the same 26 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.
Haphazard
Out of the given alternatives select the alternative which best expresses the meaning of given word.
Monotony
Out of the given alternatives select the alternative which best expresses the meaning of given word.
Condescend
Out of the given alternatives select the alternative which best expresses the meaning of given word.
Retribution
Out of the given alternatives select the word opposite in meaning to the given word.
Inequity
Out of the given alternatives select the word opposite in meaning to the given word.
Validate
Out of the given alternatives select the word opposite in meaning to the given word.
Conformity
Out of the given alternatives select the word opposite in meaning to the given word.
Reckless
Out of given alternatives, choose the word which can be substituted for the given words/ sentence.
Lacking any obvious principle of organization; done in a random, careless, or disorganized way.
Out of given alternatives, choose the word which can be substituted for the given words/ sentence.
Punishment inflicted on someone as vengeance for a wrong or criminal act.
Out of given alternatives, choose the word which can be substituted for the given words/ sentence.
To check or prove the accuracy, truth, or legitimacy of something.
Out of given alternatives, choose the word which can be substituted for the given words/ sentence.
Highly skilled, competent, or expert in doing something.
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.
Have you put ____ salt in the soup?
Explanation: ‘Have you put salt in the soup?’ is a complete sentence and does not require any article.
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.
The car ____ leaking oil.
Explanation: 'was' and 'were' are in the past tense, but they are used differently. <br> 'Was' is used in the first person singular (I) and the third person singular (he, she, it). <br> 'Were' is used in the second person singular and plural (you, your, yours) and first and third person plural (we, they).
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.
Plural of thief ____ thieves.
Explanation: When deciding whether to use 'is' or 'are', look at whether the noun is plural or singular. If the noun is singular, use 'is'. If it is plural or there is more than one noun, use 'are'.
Out of given alternatives select the option which best expresses the meaning of given idiom/ phrase.
A fool's errand
Explanation: A fool's errand: a task or activity that has no hope of success.
Out of given alternatives select the option which best expresses the meaning of given idiom/ phrase.
To see red
Explanation: To see red: to become very angry.
Out of given alternatives select the option which best expresses the meaning of given idiom/ phrase.
Pin-money
Explanation: Pin-money: a small sum of money for spending on inessentials.
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.
I have just the memory (a)/ of my (b)/ father now (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.
The company a ling time producer of planes (a)/ has engaged a public relation firm to inform (b)/ the public of their antipollution programme (c)/ No error (d)
Explanation: (c) the public of its antipollution programme
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.
How I wish it had been me (a)/ whom you had frightened (b)/ and not my daughter (c)/ No error (d)
Direction: Study the following information carefully and answer the question given below<br><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> 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> 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> 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> 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><br> I. Depressing<br> II. Dismal<br> III. Congenial<br> IV. Stark<br>
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><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> 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> 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> 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> 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><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><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> 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> 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> 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> 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><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><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> 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> 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> 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> 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><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><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> 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> 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> 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> 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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