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Punjab (PTB) · Chapter test · Free

2nd Year Computer Chapter 6 TestDATA AND ANALYSIS

30 marks · 40 minutes · 21 questions. Covers: CHAP 6: DATA AND ANALYSIS.

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2nd Year Computer Chapter 6 Test

2nd Year · Computer · Punjab (PTB)

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

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

  1. On what are predictions in predictive modeling based?

    1. Guaranteed certainty

    2. Probability

    3. Fixed rules without variation

    4. Raw feature counts

  2. Predicting a student's final grade using past marks, attendance, and study hours is an example of what?

    1. Causation

    2. Prediction

    3. Hyperparameter tuning

    4. Model validation

  3. What is defined as the number of correctly predicted negative instances?

    1. True Positives (TP)

    2. True Negatives (TN)

    3. False Positives (FP)

    4. False Negatives (FN)

  4. A commonly used tool for machine learning and data analysis is:

    1. MS Word

    2. Python

    3. PowerPoint

    4. Notepad

  5. What sub-activities are associated with the "Modeling" phase?

    1. Gathering & Processing

    2. Exploring & Cleaning

    3. Training & Testing

    4. Insights & Actions

  6. Which subject in data science is specifically used to work with numbers and calculations?

    1. Computer Science

    2. Domain Knowledge

    3. Mathematics

    4. Communication Skills

  7. Why is unstructured data difficult to analyze directly?

    1. It contains no information

    2. It does not follow a fixed format

    3. It cannot be stored on computers

    4. It is only stored in spreadsheets

  8. What key problem does performing a train-test split prevent?

    1. Overfitting

    2. Under-scaling

    3. Data cleaning

    4. Feature selection

  9. Which method of data collection uses machines and software?

    1. Manual data collection

    2. Automatic data collection

    3. Survey collection

    4. Interview collection

  10. Which type of data gives better accuracy results?

    1. Uncleaned raw data

    2. Unbalanced test data

    3. Balanced data

    4. Missing feature data

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

  1. What is the impact of data-driven decisions on business operations and performance?

  2. How do navigation apps and smart home devices assist users using machine learning?

  3. What is unlabeled data?

  4. Why is Excel recommended for beginners in data analysis?

  5. What is reduced when a model achieves high recall?

  6. How does Python compare to Excel in terms of supporting advanced machine learning and industry usage?

  7. How does a supervised learning model improve its accuracy during training?

  8. Briefly explain the use of machine learning in education.

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

  1. Discuss predictive modeling and causality, and explain the importance of model interpretation in real-world use cases.

  2. Discuss the various data sources and collection methods, detailing the distinction between manual and automatic data collection as well as real-time versus stored data gathering.

  3. Explain the key differences between structured and unstructured data, including their organizational formats, ease of analysis, and common real-world examples.