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Directions: Study the following information carefully and answer the question.Many companies have made great strides in collecting and utilising data from their own activities. So far, though, comparatively few have realised the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. Although external-data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what's available requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Analysing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalisation of external data may require updates to the organisation's existing data environment. Companies also need to remain cognisant of privacy concerns and consumer scrutiny when they use some types of external data.These challenges are considerable but surmountable. Companies across industries have begun successfully using external data from a variety of sources. The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered "alternative data" from a variety of licensed and public data sources, many of which draw from the "digital exhaust" of a growing number of technology companies and the public web. There are a number of steps that a company must take to use external data. To get started, organisations should establish a dedicated data-sourcing team. While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, that approach is not necessarily the most effective. It generally leads to a series of time-consuming vendor-by-vendor discussions and negotiations.A more effective strategy involves using data marketplace and aggregation platforms that specialise in building relationships with hundreds of data sources, often in specific data domains—for example, consumer, real-estate, government, or company data. These relationships can give organisations ready access to the broader data ecosystem. Once the team has identified a potential data set, the team's data engineers should work directly with business stakeholders and data scientists to evaluate the data and determine the degree to which the data will improve business outcomes. To do so, data teams establish evaluation criteria, assessing data across a variety of factors to determine whether the data set has the necessary characteristics for delivering valuable insights.Data assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case. For example, a transaction's data provider may claim to have hundreds of millions of transactions that help illuminate consumer trends. However, if the data include only transactions made by millennial consumers, the data set will not be useful to a company seeking to understand broader, generation-agnostic consumer trends. Modifications should be designed to ensure that the data architecture is flexible enough to support the integration of a continuous "conveyor belt" of incoming data from a variety of data sources. The final step is ensuring an appropriate and consistent level of quality by constantly monitoring the data used.Q. Which of the following correctly highlights the purpose of the phrase 'conveyor belt' mentioned in the last paragraph?a)It is used to highlight the perpetual creation and flow of the data.b)It signifies the short-term nature of external data, hence the opportunity.c)The phrase denotes the 'rapid' flow of the external data in an organisation.d)It highlights the abrupt and spontaneous nature of the external data.Correct answer is option 'A'. Can you explain this answer? for CAT 2024 is part of CAT preparation. The Question and answers have been prepared according to the CAT exam syllabus. Information about Directions: Study the following information carefully and answer the question.Many companies have made great strides in collecting and utilising data from their own activities. So far, though, comparatively few have realised the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. Although external-data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what's available requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Analysing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalisation of external data may require updates to the organisation's existing data environment. Companies also need to remain cognisant of privacy concerns and consumer scrutiny when they use some types of external data.These challenges are considerable but surmountable. Companies across industries have begun successfully using external data from a variety of sources. The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered "alternative data" from a variety of licensed and public data sources, many of which draw from the "digital exhaust" of a growing number of technology companies and the public web. There are a number of steps that a company must take to use external data. To get started, organisations should establish a dedicated data-sourcing team. While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, that approach is not necessarily the most effective. It generally leads to a series of time-consuming vendor-by-vendor discussions and negotiations.A more effective strategy involves using data marketplace and aggregation platforms that specialise in building relationships with hundreds of data sources, often in specific data domains—for example, consumer, real-estate, government, or company data. These relationships can give organisations ready access to the broader data ecosystem. Once the team has identified a potential data set, the team's data engineers should work directly with business stakeholders and data scientists to evaluate the data and determine the degree to which the data will improve business outcomes. To do so, data teams establish evaluation criteria, assessing data across a variety of factors to determine whether the data set has the necessary characteristics for delivering valuable insights.Data assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case. For example, a transaction's data provider may claim to have hundreds of millions of transactions that help illuminate consumer trends. However, if the data include only transactions made by millennial consumers, the data set will not be useful to a company seeking to understand broader, generation-agnostic consumer trends. Modifications should be designed to ensure that the data architecture is flexible enough to support the integration of a continuous "conveyor belt" of incoming data from a variety of data sources. The final step is ensuring an appropriate and consistent level of quality by constantly monitoring the data used.Q. Which of the following correctly highlights the purpose of the phrase 'conveyor belt' mentioned in the last paragraph?a)It is used to highlight the perpetual creation and flow of the data.b)It signifies the short-term nature of external data, hence the opportunity.c)The phrase denotes the 'rapid' flow of the external data in an organisation.d)It highlights the abrupt and spontaneous nature of the external data.Correct answer is option 'A'. Can you explain this answer? covers all topics & solutions for CAT 2024 Exam. Find important definitions, questions, meanings, examples, exercises and tests below for Directions: Study the following information carefully and answer the question.Many companies have made great strides in collecting and utilising data from their own activities. So far, though, comparatively few have realised the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. Although external-data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what's available requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Analysing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalisation of external data may require updates to the organisation's existing data environment. Companies also need to remain cognisant of privacy concerns and consumer scrutiny when they use some types of external data.These challenges are considerable but surmountable. Companies across industries have begun successfully using external data from a variety of sources. The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered "alternative data" from a variety of licensed and public data sources, many of which draw from the "digital exhaust" of a growing number of technology companies and the public web. There are a number of steps that a company must take to use external data. To get started, organisations should establish a dedicated data-sourcing team. While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, that approach is not necessarily the most effective. It generally leads to a series of time-consuming vendor-by-vendor discussions and negotiations.A more effective strategy involves using data marketplace and aggregation platforms that specialise in building relationships with hundreds of data sources, often in specific data domains—for example, consumer, real-estate, government, or company data. These relationships can give organisations ready access to the broader data ecosystem. Once the team has identified a potential data set, the team's data engineers should work directly with business stakeholders and data scientists to evaluate the data and determine the degree to which the data will improve business outcomes. To do so, data teams establish evaluation criteria, assessing data across a variety of factors to determine whether the data set has the necessary characteristics for delivering valuable insights.Data assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case. For example, a transaction's data provider may claim to have hundreds of millions of transactions that help illuminate consumer trends. However, if the data include only transactions made by millennial consumers, the data set will not be useful to a company seeking to understand broader, generation-agnostic consumer trends. Modifications should be designed to ensure that the data architecture is flexible enough to support the integration of a continuous "conveyor belt" of incoming data from a variety of data sources. The final step is ensuring an appropriate and consistent level of quality by constantly monitoring the data used.Q. Which of the following correctly highlights the purpose of the phrase 'conveyor belt' mentioned in the last paragraph?a)It is used to highlight the perpetual creation and flow of the data.b)It signifies the short-term nature of external data, hence the opportunity.c)The phrase denotes the 'rapid' flow of the external data in an organisation.d)It highlights the abrupt and spontaneous nature of the external data.Correct answer is option 'A'. Can you explain this answer?.

Solutions for Directions: Study the following information carefully and answer the question.Many companies have made great strides in collecting and utilising data from their own activities. So far, though, comparatively few have realised the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. Although external-data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what's available requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Analysing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalisation of external data may require updates to the organisation's existing data environment. Companies also need to remain cognisant of privacy concerns and consumer scrutiny when they use some types of external data.These challenges are considerable but surmountable. Companies across industries have begun successfully using external data from a variety of sources. The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered "alternative data" from a variety of licensed and public data sources, many of which draw from the "digital exhaust" of a growing number of technology companies and the public web. There are a number of steps that a company must take to use external data. To get started, organisations should establish a dedicated data-sourcing team. While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, that approach is not necessarily the most effective. It generally leads to a series of time-consuming vendor-by-vendor discussions and negotiations.A more effective strategy involves using data marketplace and aggregation platforms that specialise in building relationships with hundreds of data sources, often in specific data domains—for example, consumer, real-estate, government, or company data. These relationships can give organisations ready access to the broader data ecosystem. Once the team has identified a potential data set, the team's data engineers should work directly with business stakeholders and data scientists to evaluate the data and determine the degree to which the data will improve business outcomes. To do so, data teams establish evaluation criteria, assessing data across a variety of factors to determine whether the data set has the necessary characteristics for delivering valuable insights.Data assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case. For example, a transaction's data provider may claim to have hundreds of millions of transactions that help illuminate consumer trends. However, if the data include only transactions made by millennial consumers, the data set will not be useful to a company seeking to understand broader, generation-agnostic consumer trends. Modifications should be designed to ensure that the data architecture is flexible enough to support the integration of a continuous "conveyor belt" of incoming data from a variety of data sources. The final step is ensuring an appropriate and consistent level of quality by constantly monitoring the data used.Q. Which of the following correctly highlights the purpose of the phrase 'conveyor belt' mentioned in the last paragraph?a)It is used to highlight the perpetual creation and flow of the data.b)It signifies the short-term nature of external data, hence the opportunity.c)The phrase denotes the 'rapid' flow of the external data in an organisation.d)It highlights the abrupt and spontaneous nature of the external data.Correct answer is option 'A'. Can you explain this answer? in English & in Hindi are available as part of our courses for CAT. Download more important topics, notes, lectures and mock test series for CAT Exam by signing up for free.

Here you can find the meaning of Directions: Study the following information carefully and answer the question.Many companies have made great strides in collecting and utilising data from their own activities. So far, though, comparatively few have realised the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. Although external-data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what's available requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Analysing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalisation of external data may require updates to the organisation's existing data environment. Companies also need to remain cognisant of privacy concerns and consumer scrutiny when they use some types of external data.These challenges are considerable but surmountable. Companies across industries have begun successfully using external data from a variety of sources. The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered "alternative data" from a variety of licensed and public data sources, many of which draw from the "digital exhaust" of a growing number of technology companies and the public web. There are a number of steps that a company must take to use external data. To get started, organisations should establish a dedicated data-sourcing team. While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, that approach is not necessarily the most effective. It generally leads to a series of time-consuming vendor-by-vendor discussions and negotiations.A more effective strategy involves using data marketplace and aggregation platforms that specialise in building relationships with hundreds of data sources, often in specific data domains—for example, consumer, real-estate, government, or company data. These relationships can give organisations ready access to the broader data ecosystem. Once the team has identified a potential data set, the team's data engineers should work directly with business stakeholders and data scientists to evaluate the data and determine the degree to which the data will improve business outcomes. To do so, data teams establish evaluation criteria, assessing data across a variety of factors to determine whether the data set has the necessary characteristics for delivering valuable insights.Data assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case. For example, a transaction's data provider may claim to have hundreds of millions of transactions that help illuminate consumer trends. However, if the data include only transactions made by millennial consumers, the data set will not be useful to a company seeking to understand broader, generation-agnostic consumer trends. Modifications should be designed to ensure that the data architecture is flexible enough to support the integration of a continuous "conveyor belt" of incoming data from a variety of data sources. The final step is ensuring an appropriate and consistent level of quality by constantly monitoring the data used.Q. Which of the following correctly highlights the purpose of the phrase 'conveyor belt' mentioned in the last paragraph?a)It is used to highlight the perpetual creation and flow of the data.b)It signifies the short-term nature of external data, hence the opportunity.c)The phrase denotes the 'rapid' flow of the external data in an organisation.d)It highlights the abrupt and spontaneous nature of the external data.Correct answer is option 'A'. Can you explain this answer? defined & explained in the simplest way possible. Besides giving the explanation of Directions: Study the following information carefully and answer the question.Many companies have made great strides in collecting and utilising data from their own activities. So far, though, comparatively few have realised the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. Although external-data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what's available requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Analysing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalisation of external data may require updates to the organisation's existing data environment. Companies also need to remain cognisant of privacy concerns and consumer scrutiny when they use some types of external data.These challenges are considerable but surmountable. Companies across industries have begun successfully using external data from a variety of sources. The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered "alternative data" from a variety of licensed and public data sources, many of which draw from the "digital exhaust" of a growing number of technology companies and the public web. There are a number of steps that a company must take to use external data. To get started, organisations should establish a dedicated data-sourcing team. While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, that approach is not necessarily the most effective. It generally leads to a series of time-consuming vendor-by-vendor discussions and negotiations.A more effective strategy involves using data marketplace and aggregation platforms that specialise in building relationships with hundreds of data sources, often in specific data domains—for example, consumer, real-estate, government, or company data. These relationships can give organisations ready access to the broader data ecosystem. Once the team has identified a potential data set, the team's data engineers should work directly with business stakeholders and data scientists to evaluate the data and determine the degree to which the data will improve business outcomes. To do so, data teams establish evaluation criteria, assessing data across a variety of factors to determine whether the data set has the necessary characteristics for delivering valuable insights.Data assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case. For example, a transaction's data provider may claim to have hundreds of millions of transactions that help illuminate consumer trends. However, if the data include only transactions made by millennial consumers, the data set will not be useful to a company seeking to understand broader, generation-agnostic consumer trends. Modifications should be designed to ensure that the data architecture is flexible enough to support the integration of a continuous "conveyor belt" of incoming data from a variety of data sources. The final step is ensuring an appropriate and consistent level of quality by constantly monitoring the data used.Q. Which of the following correctly highlights the purpose of the phrase 'conveyor belt' mentioned in the last paragraph?a)It is used to highlight the perpetual creation and flow of the data.b)It signifies the short-term nature of external data, hence the opportunity.c)The phrase denotes the 'rapid' flow of the external data in an organisation.d)It highlights the abrupt and spontaneous nature of the external data.Correct answer is option 'A'. Can you explain this answer?, a detailed solution for Directions: Study the following information carefully and answer the question.Many companies have made great strides in collecting and utilising data from their own activities. So far, though, comparatively few have realised the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. Although external-data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what's available requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Analysing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalisation of external data may require updates to the organisation's existing data environment. Companies also need to remain cognisant of privacy concerns and consumer scrutiny when they use some types of external data.These challenges are considerable but surmountable. Companies across industries have begun successfully using external data from a variety of sources. The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered "alternative data" from a variety of licensed and public data sources, many of which draw from the "digital exhaust" of a growing number of technology companies and the public web. There are a number of steps that a company must take to use external data. To get started, organisations should establish a dedicated data-sourcing team. While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, that approach is not necessarily the most effective. It generally leads to a series of time-consuming vendor-by-vendor discussions and negotiations.A more effective strategy involves using data marketplace and aggregation platforms that specialise in building relationships with hundreds of data sources, often in specific data domains—for example, consumer, real-estate, government, or company data. These relationships can give organisations ready access to the broader data ecosystem. Once the team has identified a potential data set, the team's data engineers should work directly with business stakeholders and data scientists to evaluate the data and determine the degree to which the data will improve business outcomes. To do so, data teams establish evaluation criteria, assessing data across a variety of factors to determine whether the data set has the necessary characteristics for delivering valuable insights.Data assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case. For example, a transaction's data provider may claim to have hundreds of millions of transactions that help illuminate consumer trends. However, if the data include only transactions made by millennial consumers, the data set will not be useful to a company seeking to understand broader, generation-agnostic consumer trends. Modifications should be designed to ensure that the data architecture is flexible enough to support the integration of a continuous "conveyor belt" of incoming data from a variety of data sources. The final step is ensuring an appropriate and consistent level of quality by constantly monitoring the data used.Q. Which of the following correctly highlights the purpose of the phrase 'conveyor belt' mentioned in the last paragraph?a)It is used to highlight the perpetual creation and flow of the data.b)It signifies the short-term nature of external data, hence the opportunity.c)The phrase denotes the 'rapid' flow of the external data in an organisation.d)It highlights the abrupt and spontaneous nature of the external data.Correct answer is option 'A'. Can you explain this answer? has been provided alongside types of Directions: Study the following information carefully and answer the question.Many companies have made great strides in collecting and utilising data from their own activities. So far, though, comparatively few have realised the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. Although external-data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what's available requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Analysing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalisation of external data may require updates to the organisation's existing data environment. Companies also need to remain cognisant of privacy concerns and consumer scrutiny when they use some types of external data.These challenges are considerable but surmountable. Companies across industries have begun successfully using external data from a variety of sources. The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered "alternative data" from a variety of licensed and public data sources, many of which draw from the "digital exhaust" of a growing number of technology companies and the public web. There are a number of steps that a company must take to use external data. To get started, organisations should establish a dedicated data-sourcing team. While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, that approach is not necessarily the most effective. It generally leads to a series of time-consuming vendor-by-vendor discussions and negotiations.A more effective strategy involves using data marketplace and aggregation platforms that specialise in building relationships with hundreds of data sources, often in specific data domains—for example, consumer, real-estate, government, or company data. These relationships can give organisations ready access to the broader data ecosystem. Once the team has identified a potential data set, the team's data engineers should work directly with business stakeholders and data scientists to evaluate the data and determine the degree to which the data will improve business outcomes. To do so, data teams establish evaluation criteria, assessing data across a variety of factors to determine whether the data set has the necessary characteristics for delivering valuable insights.Data assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case. For example, a transaction's data provider may claim to have hundreds of millions of transactions that help illuminate consumer trends. However, if the data include only transactions made by millennial consumers, the data set will not be useful to a company seeking to understand broader, generation-agnostic consumer trends. Modifications should be designed to ensure that the data architecture is flexible enough to support the integration of a continuous "conveyor belt" of incoming data from a variety of data sources. The final step is ensuring an appropriate and consistent level of quality by constantly monitoring the data used.Q. Which of the following correctly highlights the purpose of the phrase 'conveyor belt' mentioned in the last paragraph?a)It is used to highlight the perpetual creation and flow of the data.b)It signifies the short-term nature of external data, hence the opportunity.c)The phrase denotes the 'rapid' flow of the external data in an organisation.d)It highlights the abrupt and spontaneous nature of the external data.Correct answer is option 'A'. Can you explain this answer? theory, EduRev gives you an ample number of questions to practice Directions: Study the following information carefully and answer the question.Many companies have made great strides in collecting and utilising data from their own activities. So far, though, comparatively few have realised the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. Although external-data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what's available requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Analysing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalisation of external data may require updates to the organisation's existing data environment. Companies also need to remain cognisant of privacy concerns and consumer scrutiny when they use some types of external data.These challenges are considerable but surmountable. Companies across industries have begun successfully using external data from a variety of sources. The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered "alternative data" from a variety of licensed and public data sources, many of which draw from the "digital exhaust" of a growing number of technology companies and the public web. There are a number of steps that a company must take to use external data. To get started, organisations should establish a dedicated data-sourcing team. While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, that approach is not necessarily the most effective. It generally leads to a series of time-consuming vendor-by-vendor discussions and negotiations.A more effective strategy involves using data marketplace and aggregation platforms that specialise in building relationships with hundreds of data sources, often in specific data domains—for example, consumer, real-estate, government, or company data. These relationships can give organisations ready access to the broader data ecosystem. Once the team has identified a potential data set, the team's data engineers should work directly with business stakeholders and data scientists to evaluate the data and determine the degree to which the data will improve business outcomes. To do so, data teams establish evaluation criteria, assessing data across a variety of factors to determine whether the data set has the necessary characteristics for delivering valuable insights.Data assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case. For example, a transaction's data provider may claim to have hundreds of millions of transactions that help illuminate consumer trends. However, if the data include only transactions made by millennial consumers, the data set will not be useful to a company seeking to understand broader, generation-agnostic consumer trends. Modifications should be designed to ensure that the data architecture is flexible enough to support the integration of a continuous "conveyor belt" of incoming data from a variety of data sources. The final step is ensuring an appropriate and consistent level of quality by constantly monitoring the data used.Q. Which of the following correctly highlights the purpose of the phrase 'conveyor belt' mentioned in the last paragraph?a)It is used to highlight the perpetual creation and flow of the data.b)It signifies the short-term nature of external data, hence the opportunity.c)The phrase denotes the 'rapid' flow of the external data in an organisation.d)It highlights the abrupt and spontaneous nature of the external data.Correct answer is option 'A'. Can you explain this answer? tests, examples and also practice CAT tests.