Training data preparation in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why is data normalization important before training a model?

Imagine you have a dataset with features measured in very different units, like height in centimeters and income in dollars. Why should you normalize this data before training a machine learning model?

A Normalization increases the size of the dataset to improve model accuracy.

B Normalization removes missing values automatically from the dataset.

C Normalization ensures all features contribute equally by scaling them to a similar range, preventing bias towards features with larger values.

D Normalization converts categorical data into numerical labels.

💡 Hint

Think about how different scales can affect the learning process of a model.

Normalization scales features to a similar range so that no single feature dominates the learning process due to its scale. This helps models learn better and faster.

❓ Predict Output

intermediate

Output of data splitting code

What is the output of the following Python code that splits data into training and testing sets?

Prompt Engineering / GenAI

from sklearn.model_selection import train_test_split
X = list(range(10))
y = list(range(10))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
print(len(X_train), len(X_test))

A 3 7

B 7 3

C 10 0

D 0 10

💡 Hint

Check how the test_size parameter affects the split.

The test_size=0.3 means 30% of data goes to testing, so 7 items remain for training and 3 for testing.

❓ Model Choice

advanced

Best data preparation for text classification

You want to train a model to classify movie reviews as positive or negative. Which data preparation step is most important before training?

A Convert text to lowercase, remove punctuation, and tokenize words.

B Normalize numerical features to zero mean and unit variance.

C Fill missing values with the mean of the column.

D Encode categorical variables using one-hot encoding.

💡 Hint

Think about what you do to raw text before feeding it to a model.

Text data needs cleaning like lowercasing, removing punctuation, and tokenizing to convert it into a form the model can understand.

❓ Metrics

advanced

Evaluating data quality impact on model accuracy

You train two models on the same task. Model A uses raw data with many missing values. Model B uses data where missing values were properly handled. Which metric difference would best show the impact of data preparation?

A Model A has lower accuracy and lower loss than Model B.

B Model A has higher accuracy but higher loss than Model B.

C Both models have the same accuracy but different loss values.

D Model B has higher accuracy and lower loss than Model A.

💡 Hint

Good data preparation usually improves both accuracy and loss.

Handling missing values properly improves data quality, leading to better model accuracy and lower loss.

🔧 Debug

expert

Identify the error in data preprocessing code

What error will this Python code raise when preparing data for training?

Prompt Engineering / GenAI

import numpy as np
from sklearn.preprocessing import StandardScaler
X = np.array([[1, 2], [3, 4], [5, 6]])
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
print(X_scaled[3])

A IndexError: index 3 is out of bounds for axis 0 with size 3

B TypeError: 'StandardScaler' object is not callable

C ValueError: could not convert string to float

D No error, prints scaled values

💡 Hint

Check the size of the array and the index accessed.

The array has only 3 rows (indices 0,1,2). Accessing index 3 causes an IndexError.

Practice

1. What is the main purpose of training data preparation in machine learning?

easy

A. To clean and organize data for better model learning

B. To create the final model architecture

C. To deploy the model to production

D. To write the code for model training

Solution

1. Step 1: Understand the role of training data preparation

   Training data preparation involves cleaning and organizing data so the model can learn effectively.

2. Step 2: Differentiate from other steps in machine learning

   Creating model architecture, deployment, and coding are separate steps after data preparation.

3. Final Answer:

   To clean and organize data for better model learning -> Option A

4. Quick Check:

   Training data preparation = cleaning and organizing data [OK]

Hint: Focus on data cleaning and organizing for training [OK]

Common Mistakes:

• Confusing data preparation with model building
• Thinking deployment is part of data preparation
• Assuming coding is data preparation

2. Which of the following is the correct way to split data into training and testing sets in Python using scikit-learn?

easy

A. split_train_test(data, 0.2)

B. train_test(data, split=0.2)

C. train_test_split(data, test_size=0.2)

D. test_train_split(data, size=0.2)

Solution

1. Step 1: Recall the scikit-learn function for splitting data

   The correct function is train_test_split with parameters like test_size.

2. Step 2: Check the syntax of each option

   Only train_test_split(data, test_size=0.2) uses the correct function name and parameter syntax.

3. Final Answer:

   train_test_split(data, test_size=0.2) -> Option C

4. Quick Check:

   Correct function and parameter = train_test_split(data, test_size=0.2) [OK]

Hint: Remember scikit-learn's train_test_split function name [OK]

Common Mistakes:

• Using wrong function names
• Incorrect parameter names
• Mixing order of parameters

3. Given the code below, what will be the output of print(X_train.shape, X_test.shape)?

from sklearn.model_selection import train_test_split
import numpy as np
X = np.arange(20).reshape(10, 2)
X_train, X_test = train_test_split(X, test_size=0.3, random_state=42)

medium

A. (7, 2) (3, 2)

B. (3, 2) (7, 2)

C. (10, 2) (0, 2)

D. (5, 2) (5, 2)

Solution

1. Step 1: Understand the data shape and split ratio

   The data X has 10 rows and 2 columns. test_size=0.3 means 30% data for testing (3 rows) and 70% for training (7 rows).

2. Step 2: Calculate the shapes of training and testing sets

   Training set shape: (7, 2), Testing set shape: (3, 2).

3. Final Answer:

   (7, 2) (3, 2) -> Option A

4. Quick Check:

   70% train = 7 rows, 30% test = 3 rows [OK]

Hint: Calculate rows by multiplying total by split ratio [OK]

Common Mistakes:

• Swapping train and test sizes
• Ignoring the shape's second dimension
• Misunderstanding test_size meaning

4. Identify the error in the following code snippet for normalizing data using MinMaxScaler:

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
X_scaled = scaler.fit_transform(X)
print(X_scaled)

medium

A. MinMaxScaler cannot handle negative values

B. MinMaxScaler requires data as a numpy array, not list

C. fit_transform should be called on scaler.fit(X).transform(X)

D. No error, code runs correctly

Solution

1. Step 1: Check input data type compatibility

   MinMaxScaler accepts lists or numpy arrays as input, so list input is valid.

2. Step 2: Verify method usage

   Calling scaler.fit_transform(X) is the correct way to fit and transform data in one step.

3. Final Answer:

   No error, code runs correctly -> Option D

4. Quick Check:

   MinMaxScaler works with lists and fit_transform method [OK]

Hint: MinMaxScaler accepts lists and arrays directly [OK]

Common Mistakes:

• Thinking input must be numpy array
• Misusing fit and transform methods
• Assuming scaler rejects negative values

5. You have a dataset with categorical text features and numeric features. Which sequence of steps correctly prepares the data for training a machine learning model?

hard

A. Split data, encode categorical features, normalize numeric features, then clean missing values

B. Clean missing values, encode categorical features, normalize numeric features, then split data

C. Normalize numeric features, clean missing values, split data, then encode categorical features

D. Encode categorical features, split data, clean missing values, then normalize numeric features

Solution

1. Step 1: Clean missing values first

   Cleaning missing data ensures no errors during encoding or normalization.

2. Step 2: Encode categorical features before normalization

   Categorical data must be converted to numbers before normalization.

3. Step 3: Normalize numeric features and then split data

   Normalization scales numeric data; splitting last avoids data leakage.

4. Final Answer:

   Clean missing values, encode categorical features, normalize numeric features, then split data -> Option B

5. Quick Check:

   Proper order: clean -> encode -> normalize -> split [OK]

Hint: Always clean first, encode before normalize, split last [OK]

Common Mistakes:

• Splitting data before cleaning causes leakage
• Normalizing before encoding categorical data
• Encoding after splitting leads to inconsistent categories