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Subjects

Artificial Intelligence

Data Interpretation and basic visualization

In this Class 9 Artificial Intelligence topic from Data Literacy, students learn to read and interpret simple datasets presented in tables, charts, and graphs. They identify values, compare categories, notice patterns and trends, and draw evidence-based conclusions without confusing observation with assumption. The topic also introduces basic data visualization by helping students choose suitable formats, use clear titles, labels, scales, and legends, and recognize how poor presentation can mislead an audience.

Medium · Level 1 · 13 questions

TOPIC PRACTICE

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13 questions

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  1. The audience can distinguish facts from interpretation
  2. Interpretation becomes fact
  3. The report becomes automatically correct
  4. The voice becomes more effective
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  1. The credibility of the data can be checked
  2. The chart becomes louder
  3. All values become equal
  4. Colours are no longer needed
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  1. The background will change
  2. Values may be read from the wrong row
  3. The title will become larger
  4. The table will become smaller
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  1. A truncated scale exaggerates the difference
  2. Bar colours always change the values
  3. The title doubles the data
  4. The horizontal axis reduces all values
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  1. A conflict between the visual cue and the data
  2. A font selection error
  3. Excessive white space
  4. Image proportion error
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  1. Approximately four times
  2. Approximately two times
  3. Approximately half
  4. Unchanged
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  1. Bar labels disappear
  2. Data changes automatically
  3. All values become zero
  4. Width may also falsely suggest magnitude
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  1. To show quantitative differences quickly
  2. To show actual physical size
  3. To make all data appear equal
  4. To eliminate the need for text
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  1. Identify problem patterns and set priorities
  2. Hide every complaint
  3. Delete data without reading
  4. Use colours only
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  1. The student's name
  2. Percentage of students present at the end of the day
  3. The identification number
  4. Arrival time
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  1. The data size is small
  2. Access control and privacy are weak
  3. The marks are in digital form
  4. A printer was not used
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  1. Treating all areas equally without examining data
  2. Viewing only old images
  3. Mapping data about affected locations, population, and needs
  4. Sending resources based on an unverified message
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  1. User training
  2. Digital payment
  3. Error in data timeliness and presentation
  4. Physical device repair

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