Generative vs discriminative models in Prompt Engineering / GenAI - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding model types: Generative vs Discriminative

Which statement correctly describes the main difference between generative and discriminative models?

A Generative models only classify data, whereas discriminative models generate new data samples.

B Discriminative models learn the joint probability of data and labels, while generative models learn the conditional probability of labels given data.

C Generative models learn the joint probability of data and labels, while discriminative models learn the conditional probability of labels given data.

D Generative models always perform better than discriminative models on classification tasks.

💡 Hint

Think about what each model tries to understand about the data and labels.

Generative models learn how data and labels come together (joint probability), enabling them to generate new data. Discriminative models focus on predicting labels from data (conditional probability).

❓ Model Choice

intermediate

Choosing the right model for generating images

You want to create a model that can generate realistic images of cats. Which type of model should you choose?

A Discriminative model like decision trees

B Discriminative model like logistic regression

C Discriminative model like Support Vector Machine (SVM)

D Generative model like a Variational Autoencoder (VAE)

💡 Hint

Think about which model type can create new data samples.

Generative models like VAEs learn to create new data similar to training data, making them suitable for image generation.

❓ Metrics

advanced

Evaluating generative model quality

Which metric is commonly used to evaluate the quality of images generated by a generative model?

A Accuracy

B Inception Score (IS)

C Mean Squared Error (MSE)

D Precision

💡 Hint

Consider metrics designed specifically for generated images.

Inception Score measures how realistic and diverse generated images are, making it popular for generative model evaluation.

🔧 Debug

advanced

Debugging a discriminative model training issue

Given this code snippet training a discriminative model, what is the likely cause of the error?

model = LogisticRegression()
model.fit(X_train, y_train)
preds = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, preds)}")

Error message: ValueError: Found input variables with inconsistent numbers of samples

A X_train and y_train have different numbers of samples

B The model is not suitable for classification

C accuracy_score is not imported

D X_test contains missing values

💡 Hint

Check the shapes of training data and labels.

The error indicates that the features and labels arrays do not have matching sample counts, which is required for training.

❓ Predict Output

expert

Output of a simple generative model code

What is the output of this Python code simulating a simple generative model sampling from a Gaussian distribution?

import numpy as np
np.random.seed(42)
mean = 0
std_dev = 1
samples = np.random.normal(mean, std_dev, 3)
print([round(s, 2) for s in samples])

A [0.5, -0.14, 0.65]

B [0.5, -0.14, 0.45]

C [0.5, -0.14, 0.54]

D [0.5, -0.15, 0.65]

💡 Hint

Run the code or recall numpy's normal distribution output with seed 42.

With seed 42, np.random.normal generates approximately [0.4967, -0.1383, 0.6477], rounded to [0.5, -0.14, 0.65].

Practice

1. Which statement best describes a generative model in machine learning?

easy

A. It only works with labeled data for prediction.

B. It directly learns the boundary between classes for classification.

C. It learns how data is generated and can create new examples.

D. It ignores the data distribution and focuses on accuracy.

Solution

1. Step 1: Understand generative model purpose

   Generative models learn the underlying data distribution to generate new data points similar to the training data.

2. Step 2: Compare with discriminative models

   Discriminative models focus on learning the decision boundary between classes, not on generating data.

3. Final Answer:

   It learns how data is generated and can create new examples. -> Option C

4. Quick Check:

   Generative = create data [OK]

Hint: Generative models create data; discriminative separate classes [OK]

Common Mistakes:

• Confusing generative with discriminative models
• Thinking generative models only classify
• Assuming generative models ignore data distribution

2. Which of the following is the correct way to describe a discriminative model?

easy

A. It models the conditional probability of outputs given inputs.

B. It ignores labels and focuses on data generation.

C. It generates new data points similar to training data.

D. It models the joint probability of inputs and outputs.

Solution

1. Step 1: Define discriminative model behavior

   Discriminative models learn the conditional probability P(output|input), focusing on predicting labels from data.

2. Step 2: Contrast with generative models

   Generative models model the joint probability P(input, output) to generate data, which is not the case here.

3. Final Answer:

   It models the conditional probability of outputs given inputs. -> Option A

4. Quick Check:

   Discriminative = P(output|input) [OK]

Hint: Discriminative models predict labels from inputs [OK]

Common Mistakes:

• Mixing joint and conditional probabilities
• Thinking discriminative models generate data
• Confusing labels with data points

3. Consider the following Python code snippet using scikit-learn:

from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression

X_train = [[1, 2], [2, 3], [3, 4], [4, 5]]
y_train = [0, 0, 1, 1]

model = GaussianNB()
model.fit(X_train, y_train)
predictions = model.predict([[2, 3]])
print(predictions)

What will be the output of this code?

medium

A. [1]

B. [0]

C. [0 1]

D. Error due to wrong model usage

Solution

1. Step 1: Identify model type and training data

   GaussianNB is a generative model that learns class distributions. Training data has two classes: 0 and 1.

2. Step 2: Predict class for input [2, 3]

   Input [2, 3] is closer to training points labeled 0 ([1,2],[2,3]) than to those labeled 1, so prediction is class 0.

3. Final Answer:

   [0] -> Option B

4. Quick Check:

   GaussianNB predicts class 0 for [2,3] [OK]

Hint: GaussianNB predicts class based on closest learned distribution [OK]

Common Mistakes:

• Assuming LogisticRegression is used instead
• Expecting multiple classes in output
• Thinking prediction causes error

4. The following code tries to train a discriminative model but has an error:

from sklearn.linear_model import LogisticRegression

X_train = [[1, 2], [2, 3], [3, 4]]
y_train = [0, 1]

model = LogisticRegression()
model.fit(X_train, y_train)

What is the error and how to fix it?

medium

A. Mismatch in number of samples and labels; fix by matching lengths.

B. LogisticRegression requires numeric labels; convert labels to numbers.

C. X_train must be a numpy array; convert list to array.

D. Model.fit() missing parameter; add sample weights.

Solution

1. Step 1: Check training data shapes

   X_train has 3 samples, but y_train has only 2 labels, causing mismatch error.

2. Step 2: Fix label length

   To fix, ensure y_train has 3 labels matching X_train samples, e.g., y_train = [0, 1, 0].

3. Final Answer:

   Mismatch in number of samples and labels; fix by matching lengths. -> Option A

4. Quick Check:

   Samples and labels count must match [OK]

Hint: Check if data and label counts match before training [OK]

Common Mistakes:

• Ignoring label count mismatch
• Assuming LogisticRegression needs label conversion
• Thinking data type causes error

5. You want to build a model that can both classify images of cats and dogs and also generate new realistic images of cats. Which approach should you choose?

hard

A. Use a clustering algorithm to separate and generate images.

B. Use a generative model like a Generative Adversarial Network (GAN) for both tasks.

C. Use a discriminative model like Logistic Regression for both tasks.

D. Use a discriminative model for classification and a generative model for image creation.

Solution

1. Step 1: Identify tasks and suitable models

   Classification is best done by discriminative models that separate classes well. Image generation requires generative models that learn data distribution.

2. Step 2: Combine models for both tasks

   Use a discriminative model for classifying cats vs dogs, and a generative model like GAN to create new cat images.

3. Final Answer:

   Use a discriminative model for classification and a generative model for image creation. -> Option D

4. Quick Check:

   Classification + generation = discriminative + generative [OK]

Hint: Classify with discriminative, generate with generative models [OK]

Common Mistakes:

• Using one model type for both tasks
• Confusing clustering with generation
• Ignoring model strengths for each task