Viva prep · Real questions · Student experiences Enroll in Bootcamp

Proctor workspace

Proctor MLP_LEVEL1_VIVA

Share your experience Add your viva experience here
11 Questions
1 Sets
0 Topics
0 Reviews
Tips for this examiner: Prepare well.

Understand your code well.

Learn basic operation on toy datasets.

Prepare your theory well. Be firm on your answers, he can trick you.

Emphasis on theoretical understanding.

Student reviews

No student reviews for this proctor yet.

Approved viva sets

Download all
  1. 1
    Asked to show everything mentioned in the mandatory requirements of L1 viva, one by one.
    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.

  2. 2
    Explain hyperparameter tuning
    Times asked 1
    Official solution

    SimpleCode explain formula: input kya aaya → auth/validation → DB change → response/UI.

    Viva tipFunction signature + 3-4 important lines + edge case.

  3. 3
    Why did you train your final model on entire dataset? (You shouldn't)
    Times asked 1
    Official solution

    Simplemodels.py = har table ki class: columns, PK/FK, relationships. Yeh Model layer hai.

    Suggested flow1) Classes list karo
    2) Har table ka kaam 1 line
    3) Relationships (1-M / M-M)
    4) Constraints (unique, nullable)

    Example

    class Lot(db.Model):
        id = db.Column(db.Integer, primary_key=True)
        name = db.Column(db.String(80), nullable=False)
        spots = db.relationship('Spot', backref='lot', lazy=True)
  4. 4
    How did you ensure data leakage?
    Times asked 1
    Official solution

    SimpleCode explain formula: input kya aaya → auth/validation → DB change → response/UI.

    Viva tipFunction signature + 3-4 important lines + edge case.

  5. 5
    Explain the parameters of XGBoost, Linear regressor.
    Times asked 1
    Official solution

    SimpleCode explain formula: input kya aaya → auth/validation → DB change → response/UI.

    Viva tipFunction signature + 3-4 important lines + edge case.

  6. 6
    Coding questions was to load diabetes dataset
    Times asked 1
    Official solution

    SimpleLive coding: notepad/editor mein chhota Flask route / Jinja loop / model field. Calm raho, run karke dikhao.

  7. 7
    Convert it to the dataframe.
    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.

  8. 8
    Fit a logistic model on it.
    Times asked 1
    Official solution

    Simplemodels.py = har table ki class: columns, PK/FK, relationships. Yeh Model layer hai.

    Suggested flow1) Classes list karo
    2) Har table ka kaam 1 line
    3) Relationships (1-M / M-M)
    4) Constraints (unique, nullable)

    Example

    class Lot(db.Model):
        id = db.Column(db.Integer, primary_key=True)
        name = db.Column(db.String(80), nullable=False)
        spots = db.relationship('Spot', backref='lot', lazy=True)
  9. 9
    Number of features in it.
    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.

  10. 10
    Can you use logistic regression to predict the amount of rainfall. 11 Difference between bagging and boosting, is xgb a bagging model or a boosting model?
    Times asked 1
    Official solution

    Simplemodels.py = har table ki class: columns, PK/FK, relationships. Yeh Model layer hai.

    Suggested flow1) Classes list karo
    2) Har table ka kaam 1 line
    3) Relationships (1-M / M-M)
    4) Constraints (unique, nullable)

    Example

    class Lot(db.Model):
        id = db.Column(db.Integer, primary_key=True)
        name = db.Column(db.String(80), nullable=False)
        spots = db.relationship('Spot', backref='lot', lazy=True)
  11. 11
    How to ensure reproducibility of the results?
    Times asked 1
    Official solution

    SimpleCode explain formula: input kya aaya → auth/validation → DB change → response/UI.

    Viva tipFunction signature + 3-4 important lines + edge case.

Advice: Prepare well. Understand your code well. Learn basic operation on toy datasets. Prepare your theory well. Be firm on your answers, he can trick you. Emphasis on theoretical understanding.
Created for educational purposes only. Questions are based on students' personal experiences and may not reflect actual exam content.