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Federal (FBISE) · Chapter test · Free

10th Class Computer Chapter 4 Test4. Data and Analysis

30 marks · 40 minutes · 21 questions. Covers: 4. Data and Analysis.

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10th Class Computer Chapter 4 Test

10th Class · Computer · Federal (FBISE)

Name: ____________Roll No: ________Class/Section: ________Date: ________Total marks: 30Time: 40 minutes

Q1. Multiple choice questions (Attempt all · 10 × 1 = 10)

  1. A company wants to develop a system that categorizes customer feedback into positive, negative, or neutral. Which learning model would be most suitable?
    1. Supervised learning
    2. Unsupervised learning
    3. Reinforcement learning
    4. Deep learning
  2. Which stage ensures data is relevant and accurate?
    1. Problem Definition
    2. Data Collection
    3. Data Cleaning
    4. Model Deployment
  3. Deploying a model means:
    1. Deleting it
    2. Testing it
    3. Putting it into use
    4. Making KPIs
  4. Analytical tools in database are helpful for:
    1. Data storage
    2. Data analysis
    3. Gaming
    4. Sound processing
  5. Data visualization is the process of creating:
    1. Tables
    2. Graphical representation of data
    3. Text reports
    4. None
  6. Realtime data processing is supported by:
    1. Database
    2. Compiler
    3. Editor
    4. Keyboard
  7. Information visualization is often used for:
    1. Abstract data
    2. Graphical games
    3. Sound files
    4. None
  8. APIs are:
    1. pplication Processing Interface
    2. Application Programming Interface
    3. Automatic Program Interaction
    4. Artificial Prediction Input
  9. Maintenance and monitoring occur:
    1. Before problem definition
    2. After deployment
    3. During cleaning
    4. During collection
  10. TensorFlow is a framework used in:
    1. Data Science
    2. Machine Learning and Deep Learning
    3. Communication
    4. Website Design

Q2. Short questions (Attempt any 6 of 8 · 6 × 2 = 12)

  1. What is a recommender system?
  2. Why is model evaluation necessary?
  3. Define KPI with one example.
  4. Define Machine Learning.
  5. Define smart cities.
  6. What is unsupervised learning?
  7. Describe an everyday example that illustrates reinforcement learning.
  8. What is the importance of surveys in data collection?

Q3. Long questions (Attempt any 2 of 3 · 2 × 4 = 8)

  1. Analyze the interrelationship between Data Science, Machine Learning, and Artificial Intelligence.
  2. Discuss its applications in healthcare, sports, finance, business, and other fields with examples.
  3. Discuss the way data visualization can be used to communicate data uncertainty. Provide two specific examples.