English II End Term: 3 April 2022 (January 2022 term)

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

Which of the following statements best summarises the gist of this passage?

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

How did Tukey define “data analysis”?

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

Which of the following statements is/are true about today’s data science?

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

Data science is most frequently talked about in the following fields:

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

The authors give particular examples of three fields of scientific research where data science can be of use. What are they?

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

Which of the following statements is/are TRUE?

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

What is the meaning of the word “unprecedented ”?

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

Match the following scientific disciplines with the adjectives that the authors use to describe these fields:

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

Which of the following words (from the passage given) is the closest opposite of the word “avoid”?

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

Choose the odd one out among the following.

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q11mq.mp3

What does the phrase “being in the thick of something” mean?

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q11mq.mp3

What is the meaning of the word “armageddon” in the context of the audio?

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q11mq.mp3

What does consecutive interpretation mean?

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q11mq.mp3

When was the simultaneous interpretation system developed?

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q11mq.mp3

What is the meaning of “keeping aplomb”?

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q11mq.mp3

Approximately how many years of training does it take fluent bilinguals to become skilled simultaneous interpreters?

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q11mq.mp3

What is the meaning of something becoming “second nature” to someone?

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q11mq.mp3

Which of the following strategies do simultaneous interpreters use to make their job easier?

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q11mq.mp3

Which of the following is true about simultaneous interpreters?

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q11mq.mp3

Which of the following words is closest in meaning to the word “pertinent”?

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q21mq.mp3

The word aggregate is stressed on the_________.

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q21mq.mp3

The word civilization is stressed on the__________.

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q21mq.mp3

Identify the number of syllables in the word suffering.

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q21mq.mp3

The vowel in the first syllable of the word hunter is ______.

Listen to the audio and answer the given subquestions:

885_640653_0_1984128_hs1002fd1q1e1s1q21mq.mp3

Choose the appropriate pause for the following sentence as you hear in the audio:
That's here that's home that's us on it everyone you love everyone you know everyone you ever heard of every human being who ever was lived out their lives.

Read the following conversation and answer the subquestions.
Anisha: Min, it’s so nice to meet you in this setting. Thank you for taking the time to interview for the role of instructional designer at Bridgespan Education. I’m curious to hear more about your interest in the learning design industry. Why are you in this industry?
Min: Nice to meet you too! I’m so excited to be here today and thankful to you for having given me this opportunity. So, I’ve been a curriculum developer in various educational and corporate settings for the past 10 years. I’m very passionate about creating data-driven and aesthetically appealing learning solutions in different formats — be it instructor-led, e-learning, and blended learning. I applied for this role because I watched a news piece on how your CEO Mr. Raul brought in an indoor mapping tool that made hybrid work possible for employees working from anywhere — I thought that was quite amazing, and I’d be thrilled to meet him sometime.
Anisha: We can definitely have you meet him if things work out! It’s great that you’ve been following our company for some time now. I’d like to follow up with another question — why this role and this company, in particular?
Min: Well, there are three reasons I applied for this role in particular — I’d love to dive into them if you’d like to know more about that!
Anisha: nods enthusiastically
Min: The first is that your company is known to put a strong emphasis on innovations and research, and I’d love to be a part of a company that wants to constantly develop their products. Secondly, I’m also huge on teamwork and collaboration, and given that the role and your company’s values emphasise teamwork, it sounded like the perfect fit for me. Thirdly, the position matches my prior experience, but is also challenging enough that I know I’ll have the space to pick up new skills and grow.
Anisha: That’s great, thanks so much!

Which of the following is NOT true about Min?

Read the following conversation and answer the subquestions.
Anisha: Min, it’s so nice to meet you in this setting. Thank you for taking the time to interview for the role of instructional designer at Bridgespan Education. I’m curious to hear more about your interest in the learning design industry. Why are you in this industry?
Min: Nice to meet you too! I’m so excited to be here today and thankful to you for having given me this opportunity. So, I’ve been a curriculum developer in various educational and corporate settings for the past 10 years. I’m very passionate about creating data-driven and aesthetically appealing learning solutions in different formats — be it instructor-led, e-learning, and blended learning. I applied for this role because I watched a news piece on how your CEO Mr. Raul brought in an indoor mapping tool that made hybrid work possible for employees working from anywhere — I thought that was quite amazing, and I’d be thrilled to meet him sometime.
Anisha: We can definitely have you meet him if things work out! It’s great that you’ve been following our company for some time now. I’d like to follow up with another question — why this role and this company, in particular?
Min: Well, there are three reasons I applied for this role in particular — I’d love to dive into them if you’d like to know more about that!
Anisha: nods enthusiastically
Min: The first is that your company is known to put a strong emphasis on innovations and research, and I’d love to be a part of a company that wants to constantly develop their products. Secondly, I’m also huge on teamwork and collaboration, and given that the role and your company’s values emphasise teamwork, it sounded like the perfect fit for me. Thirdly, the position matches my prior experience, but is also challenging enough that I know I’ll have the space to pick up new skills and grow.
Anisha: That’s great, thanks so much!

What effect does Min’s mentioning the CEO of the company have on the interviewer?

Read the following conversation and answer the subquestions.
Anisha: Min, it’s so nice to meet you in this setting. Thank you for taking the time to interview for the role of instructional designer at Bridgespan Education. I’m curious to hear more about your interest in the learning design industry. Why are you in this industry?
Min: Nice to meet you too! I’m so excited to be here today and thankful to you for having given me this opportunity. So, I’ve been a curriculum developer in various educational and corporate settings for the past 10 years. I’m very passionate about creating data-driven and aesthetically appealing learning solutions in different formats — be it instructor-led, e-learning, and blended learning. I applied for this role because I watched a news piece on how your CEO Mr. Raul brought in an indoor mapping tool that made hybrid work possible for employees working from anywhere — I thought that was quite amazing, and I’d be thrilled to meet him sometime.
Anisha: We can definitely have you meet him if things work out! It’s great that you’ve been following our company for some time now. I’d like to follow up with another question — why this role and this company, in particular?
Min: Well, there are three reasons I applied for this role in particular — I’d love to dive into them if you’d like to know more about that!
Anisha: nods enthusiastically
Min: The first is that your company is known to put a strong emphasis on innovations and research, and I’d love to be a part of a company that wants to constantly develop their products. Secondly, I’m also huge on teamwork and collaboration, and given that the role and your company’s values emphasise teamwork, it sounded like the perfect fit for me. Thirdly, the position matches my prior experience, but is also challenging enough that I know I’ll have the space to pick up new skills and grow.
Anisha: That’s great, thanks so much!

Which of the following sentences is/are incorrect?

Read the following conversation and answer the subquestions.
Anisha: Min, it’s so nice to meet you in this setting. Thank you for taking the time to interview for the role of instructional designer at Bridgespan Education. I’m curious to hear more about your interest in the learning design industry. Why are you in this industry?
Min: Nice to meet you too! I’m so excited to be here today and thankful to you for having given me this opportunity. So, I’ve been a curriculum developer in various educational and corporate settings for the past 10 years. I’m very passionate about creating data-driven and aesthetically appealing learning solutions in different formats — be it instructor-led, e-learning, and blended learning. I applied for this role because I watched a news piece on how your CEO Mr. Raul brought in an indoor mapping tool that made hybrid work possible for employees working from anywhere — I thought that was quite amazing, and I’d be thrilled to meet him sometime.
Anisha: We can definitely have you meet him if things work out! It’s great that you’ve been following our company for some time now. I’d like to follow up with another question — why this role and this company, in particular?
Min: Well, there are three reasons I applied for this role in particular — I’d love to dive into them if you’d like to know more about that!
Anisha: nods enthusiastically
Min: The first is that your company is known to put a strong emphasis on innovations and research, and I’d love to be a part of a company that wants to constantly develop their products. Secondly, I’m also huge on teamwork and collaboration, and given that the role and your company’s values emphasise teamwork, it sounded like the perfect fit for me. Thirdly, the position matches my prior experience, but is also challenging enough that I know I’ll have the space to pick up new skills and grow.
Anisha: That’s great, thanks so much!

What is the meaning of the phrase “follow up”?

Read the following conversation and answer the subquestions.
Anisha: Min, it’s so nice to meet you in this setting. Thank you for taking the time to interview for the role of instructional designer at Bridgespan Education. I’m curious to hear more about your interest in the learning design industry. Why are you in this industry?
Min: Nice to meet you too! I’m so excited to be here today and thankful to you for having given me this opportunity. So, I’ve been a curriculum developer in various educational and corporate settings for the past 10 years. I’m very passionate about creating data-driven and aesthetically appealing learning solutions in different formats — be it instructor-led, e-learning, and blended learning. I applied for this role because I watched a news piece on how your CEO Mr. Raul brought in an indoor mapping tool that made hybrid work possible for employees working from anywhere — I thought that was quite amazing, and I’d be thrilled to meet him sometime.
Anisha: We can definitely have you meet him if things work out! It’s great that you’ve been following our company for some time now. I’d like to follow up with another question — why this role and this company, in particular?
Min: Well, there are three reasons I applied for this role in particular — I’d love to dive into them if you’d like to know more about that!
Anisha: nods enthusiastically
Min: The first is that your company is known to put a strong emphasis on innovations and research, and I’d love to be a part of a company that wants to constantly develop their products. Secondly, I’m also huge on teamwork and collaboration, and given that the role and your company’s values emphasise teamwork, it sounded like the perfect fit for me. Thirdly, the position matches my prior experience, but is also challenging enough that I know I’ll have the space to pick up new skills and grow.
Anisha: That’s great, thanks so much!

Which of the following sentences uses the word “quite” correctly?

Context :At a job fair, an interviewer to an interviewee.
A question about her hobbies.

Context :At a job fair, an interviewer to an interviewee.
About her professional qualifications.

He won the battle against the pesticide company in court. The meaning of this sentence is _____.

Identify the independent clause in the following sentence.
Last weekend we went to several antique shops, though we didn’t make any large purchases.

Identify the number of suffixes in the following sentence.
This new device is a fabulous piece of technical craftsmanship and its performance is supremely impressive.

Identify the number of prefixes in the following sentence.
That movie is touted as the first desi superhero movie to be made at this scale.

Imagine that you are in a formal meeting with a client. In such a context, you are asking her whether she would like to review a file. From the options below, which would be the most appropriate way to ask this question?

Convert the following sentence to active voice.
The defender was tackled by Ronaldo.

These people are so slow, __________?

How many relative clauses does the following sentence have?
The college examinations which had been cancelled were rescheduled for later by a government official who was a former teacher.

For the following sentence, choose whether the type of sentence is simple, compound, or complex.
The child is hungry, therefore it is crying.

Identify the adjectival clauses in the following sentence.
Our teacher told us the reason why we should study grammar.

Identify the verb tense and aspect in the following sentence.
Poonguzhali was rowing the boat.

My dog, who is very friendly, loves to play around with me is a compound sentence.

Identify the adverb in the following sentence.
The lazy dog ran into a lovely wolf.

In the following sentence, fill in the blank with the most appropriate modal verb.
If she owned a car, she ___________ have travelled around the world: although she is quite a reckless driver.

In the following sentence, fill in the blank with the most appropriate modal verb.
She __________ try putting on some formal clothes. (suggestion)

Identify the adjective(s) in the following sentence.
Ayurveda is an ancient system of medicine.

Identify the indirect object in the following sentence.
Our boss sent the document to the client by email.

Mumbai is always full of people, _________?

“Asoka was a great emperor.” The interrogative form of this sentence is _____________.

For the direct question “When does the registration begin?”, identify a possible embedded question from the options given below.

Will they inform us ______ or not the convocation will take place?

For the matrix clause “He teaches”, the direct object is the clause “that there is virtue in sharing”, and the indirect object is “them”. Identify the correct order of the sentence.

From the options given below, identify a transitive verb.

‘All of the cake ___ been eaten by Shyam!’

Pick the odd one out
In theory, technically speaking, morally speaking, in political terms

He rejected their proposals despite of their wonderful marketing. This sentence is grammatically correct.

Choose an appropriate salutation for a professional email.

He liked her very much. The negative of this sentence is: He ___ like her very much.