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Proctor level1_viva8

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69 Questions
2 Sets
0 Topics
0 Reviews
Tips for this examiner: He is very sweet and patient. He gave me a lot of time to implement. No pressure.

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  1. 1
    Show Github Collaborator
    Times asked 1
    Official solution

    SimpleGitHub repo open karo: commits, collaborators, README. Examiner check karta hai tumne khud kaam kiya.

    Example

    Settings → Collaborators
    Commits: regular messages, last-day dump nahi.

    Viva tipPrivate repo access pehle de do. Main branch submitted ZIP se match honi chahiye.

  2. 2
    Download from the portal
    Times asked 1
    Official solution

    SimpleExaminer portal wala submitted ZIP chahta hai. Local extra changes mat dikhana.

    Suggested flow1) Portal ZIP download
    2) Extract
    3) venv + pip install -r requirements.txt
    4) flask run / python app.py

  3. 3
    Run the program and showcase the features
    Times asked 1
    Official solution

    SimpleDemo script pehle se practice: 5–7 min, Admin + User roles, saare mandatory features.

    Suggested flow1) Register/login user
    2) Admin login — CRUD
    3) Approval/blacklist
    4) Booking/apply + edge case
    5) Search
    6) Logout

    Viva tipBolte-bolte dikhao. Code tab kholna jab poochhein.

  4. 4
    Asked basic flow questions
    Times asked 1
    Official solution

    SimplePehle 1-line definition, phir apne MAD1 project mein file dikhao, phir chhota example.

    Suggested flow1) Concept kya hai?
    2) Mere code mein kahan?
    3) Example / demo
    4) Edge case

    Example

    # related file: models.py / routes / template
    # formula: request → check/auth → DB → render/redirect

    Viva tipExact line yaad nahi to honestly related part dikhao. Bluff mat karo.

  5. 5
    Asked if certain features are implemented (like profile edit after creation. I said no)
    Times asked 1
    Official solution

    SimplePehle 1-line definition, phir apne MAD1 project mein file dikhao, phir chhota example.

    Suggested flow1) Concept kya hai?
    2) Mere code mein kahan?
    3) Example / demo
    4) Edge case

    Example

    # related file: models.py / routes / template
    # formula: request → check/auth → DB → render/redirect

    Viva tipExact line yaad nahi to honestly related part dikhao. Bluff mat karo.

  6. 6
    Asked very basic edge case questions (like what happens if a company with an already listed job drive gets banned)
    Times asked 1
    Official solution

    SimpleEdge cases: duplicate booking, full capacity, wrong role URL, empty search, invalid id.

    Example

    if Spot.query.get_or_404(spot_id).status != 'available':
        flash('Already taken')
        return redirect(...)
  7. 7
    Implementations:
    Times asked 1
    Official solution

    SimpleViva wrap-up hai. Short thanks + confirm submission ZIP latest hai.

  8. 8
    In the student dashboard, show the Student ID taken as input during registration
    Times asked 1
    Official solution

    SimpleCamera pe clearly college/IITM ID dikhao — naam aur photo match hone chahiye.

    Viva tipPehle se ID haath mein rakho, glare mat aane do. Iske baad GitHub + project run.

  9. 9
    In the student dashboard, have a button which displays the current account student id (I created a new route and html file with jinja template)
    Times asked 1
    Official solution

    SimpleCamera pe clearly college/IITM ID dikhao — naam aur photo match hone chahiye.

    Viva tipPehle se ID haath mein rakho, glare mat aane do. Iske baad GitHub + project run.

  10. 10
    What more would you want to implement
    Times asked 1
    Official solution

    SimplePehle 1-line definition, phir apne MAD1 project mein file dikhao, phir chhota example.

    Suggested flow1) Concept kya hai?
    2) Mere code mein kahan?
    3) Example / demo
    4) Edge case

    Example

    # related file: models.py / routes / template
    # formula: request → check/auth → DB → render/redirect

    Viva tipExact line yaad nahi to honestly related part dikhao. Bluff mat karo.

Advice: He is very sweet and patient. He gave me a lot of time to implement. No pressure.
  1. 1
    What are the parameters of Logistic Regression?
    Times asked 1
  2. 2
    Can Logistic Regression be used to predict a continuous value (e.g., rainfall)? Why or why not?
    Times asked 1
  3. 3
    What are the parameters of Random Forest?
    Times asked 1
  4. 4
    What is the loss function of Random Forest?
    Times asked 1
  5. 5
    Difference between parameters and hyperparameters.
    Times asked 2
  6. 6
    What are the important XGBoost parameters?
    Times asked 1
  7. 7
    If XGBoost is overfitting, should you increase or decrease n_estimators? Why?
    Times asked 1
  8. 8
    If XGBoost is overfitting, should you increase or decrease learning_rate? Why?
    Times asked 1
  9. 9
    What does colsample_bytree / colsample_bylevel / colsample_bynode (referred to as col_by_sample) do?
    Times asked 1
  10. 10
    What is n_iter in RandomizedSearchCV?
    Times asked 1
  11. 11
    What does verbose mean?
    Times asked 1
  12. 12
    What does n_jobs do?
    Times asked 1
  13. 13
    What is the mathematical range of R² score?
    Times asked 1
  14. 14
    Why can R² be negative?
    Times asked 1
  15. 15
    What is data leakage?
    Times asked 1
  16. 16
    How did you ensure data leakage was avoided?
    Times asked 1
  17. 17
    Why should you not train the final model on the entire dataset before evaluation?
    Times asked 1
  18. 18
    How do you ensure reproducibility of results?
    Times asked 1
  19. 19
    What is autocorrelation?
    Times asked 1
  20. 20
    Explain the parameters of TF-IDF.
    Times asked 1
  21. 21
    Create a preprocessing Pipeline using: SimpleImputer RobustScaler KNNImputer OneHotEncoder
    Times asked 1
  22. 22
    Add RobustScaler to an existing preprocessing pipeline.
    Times asked 1
  23. 23
    Explain what changes after adding RobustScaler.
    Times asked 1
  24. 24
    Load the Diabetes dataset.
    Times asked 1
  25. 25
    Convert it into a Pandas DataFrame.
    Times asked 1
  26. 26
    Print the number of features.
    Times asked 1
  27. 27
    Fit a Logistic Regression model on it.
    Times asked 1
  28. 28
    Explain EDA overall, not line by line.
    Times asked 1
  29. 29
    Mention one important observation from the dataset.
    Times asked 1
  30. 30
    Show all model score comparisons.
    Times asked 1
  31. 31
    How many models were trained and how were they compared?
    Times asked 1
  32. 32
    Explain the mandatory Level-1 notebook sections one by one.
    Times asked 1
  33. 33
    Were any custom/user-defined functions used?
    Times asked 1
  34. 34
    Were any LLMs used while building the notebook?
    Times asked 1
  35. 35
    Parameters of Logistic Regression
    Times asked 1
  36. 36
    Parameters of Random Forest
    Times asked 1
  37. 37
    Loss function of Random Forest
    Times asked 1
  38. 38
    Difference between Parameters and Hyperparameters
    Times asked 1
  39. 39
    Important XGBoost parameters
    Times asked 1
  40. 40
    Effect of n_estimators on overfitting
    Times asked 1
  41. 41
    Effect of learning_rate on overfitting
    Times asked 1
  42. 42
    colsample_* parameter in XGBoost
    Times asked 1
  43. 43
    verbose
    Times asked 1
  44. 44
    n_jobs
    Times asked 1
  45. 45
    Mathematical range of R²
    Times asked 1
  46. 46
    Why R² can be negative
    Times asked 1
  47. 47
    Data Leakage
    Times asked 1
  48. 48
    How to prevent Data Leakage
    Times asked 1
  49. 49
    Why not train the final model on the full dataset before evaluation
    Times asked 1
  50. 50
    Reproducibility of results
    Times asked 1
  51. 51
    Autocorrelation
    Times asked 1
  52. 52
    TF-IDF parameters
    Times asked 1
  53. 53
    Can Logistic Regression predict rainfall? Why/Why not?
    Times asked 1
  54. 54
    Create/Edit preprocessing pipeline with RobustScaler, KNNImputer, OHE
    Times asked 1
  55. 55
    Load Diabetes dataset → DataFrame → Number of features → Fit Logistic Regression
    Times asked 1
  56. 56
    Explain one key insight from EDA
    Times asked 1
  57. 57
    Compare all trained models
    Times asked 1
  58. 58
    User-defined functions
    Times asked 1
  59. 59
    LLM usage in the notebook
    Times asked 1
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