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6.  Situation in which the problem arises is the ‘When’ block.                               ……….……
            7.  It is important that the data we provide to an AI project is relevant.                   ……….……
            8.  Data is the base for any AI project to be built.                                         ……….……

            9.  Data visualisation makes an effective user interaction.                                  ……….……
           10.  Data visualisation is not important for Data Exploration.                                ……….……
           11.  Data exploration arranges your scattered data into a structured pattern.                 ……….……
           12.  Data visualization tools accelerate decision making based on the data insights.          ……….……
           13.  Machine learning is a subset of deep learning.                                           ……….……
           14.  Learning-based approach is based on the set of rules and facts.                          ……….……

           15.  Evaluation is seen as the end of the Project cycle.                                      ……….……

        D.  Match the following:
            1.  Problem Scoping                         a.  4W Problem Canvas
            2.  Who, What, When, Why                    b.  Final step of Project Cycle
            3.  Evaluation                              c.  Identifying Problem
            4.  Web Scraping                            d.  Collecting data from images
            5.  Cameras                                 e.  Data extracted from a website

            6.  Bar Chart                               f.  visualise location and proportion using circles over geographical
                                                          regions
            7.  Bubble Map                              g.  to track changes over time

            8.  Decision tree                           h.  Large volumes of data
            9.  Deep learning                           i.  Rule-based approach

                                          SECTION B (Subjective Type Questions)

        A.  Short answer type questions:
            1.  What is data exploration in AI project cycle?
          Ans.  Data exploration refers to the techniques and tools used to visualize data through complex statistical methods.

            2.  What do we do in the Evaluation stage of the AI project cycle?
          Ans.  Evaluation stage is the testing of the system, where we check if the model is capable of achieving required goals or not.
            3.  What is the role of iterative process in problem scoping?
          Ans.  The iterative process is an important approach of problem scoping that helps in continually improving a design or
              product using an AI model.
            4.  What is the use of ‘Where’ block in 4W’s Problem Canvas?
          Ans.  In this stage, we check for where does the problem arise, the context of the problem.

            5.  What do you mean by Data Acquisition?
          Ans.  Data acquisition means collecting raw facts, figures or statistics from relevant sources either for reference or for analysis
              needed for AI projects.
            6.  What is the use of data in an AI project?
          Ans.  Data plays an important part of an AI project as it creates the base on which the AI project is built.

            7.  What is a system map?
          Ans.  A system map is a diagrammatic representation of a set of things working together.
            8.  State the ways in which you can collect data.
          Ans.  Surveys, Web scraping, sensors, cameras, Observations, API.
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