Feature engineering pipelines in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main purpose of a feature engineering pipeline in MLOps?

Choose the best description of why we use feature engineering pipelines in machine learning operations.

A To collect raw data from various sources.

B To deploy machine learning models to production environments.

C To automate and standardize the process of transforming raw data into features for models.

D To monitor the performance of models after deployment.

💡 Hint

Think about what happens before training a model with raw data.

Feature engineering pipelines automate and standardize how raw data is transformed into features, ensuring consistency and reproducibility.

💻 Command Output

intermediate

Output of a feature pipeline step using scikit-learn's ColumnTransformer

Given the following Python code snippet, what is the output shape of X_transformed?

MLOps

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
import numpy as np

X = np.array([[25, 'red'], [30, 'blue'], [22, 'green']])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', StandardScaler(), [0]),
        ('cat', OneHotEncoder(), [1])
    ])

X_transformed = preprocessor.fit_transform(X)
print(X_transformed.shape)

A (3, 3)

B (3, 4)

C (2, 4)

D (3, 2)

💡 Hint

Count numeric and categorical features after transformation.

The numeric column is scaled to 1 feature, and the categorical column with 3 unique values is one-hot encoded to 3 features, total 4 features.

🔀 Workflow

advanced

Order the steps to build a feature engineering pipeline for a new dataset

Arrange the following steps in the correct order to create a feature engineering pipeline.

A 3,1,2,4

B 2,1,3,4

C 1,3,2,4

D 1,2,3,4

💡 Hint

Think about understanding data first, then defining transformations, then implementation, then testing.

First identify data and types, then define transformations, implement the pipeline, and finally test it.

❓ Troubleshoot

advanced

Why does this feature pipeline raise a ValueError during fit?

Consider this code snippet that raises an error during fit. What is the most likely cause?

MLOps

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
import numpy as np

X = np.array([[1, 2], [np.nan, 3], [7, 6]])

pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

pipeline.fit(X)

A Pipeline steps are in wrong order; imputer should come before scaler.

B SimpleImputer requires categorical data, but numeric data was given.

C StandardScaler cannot handle NaN values before imputation.

D The input array X has inconsistent row lengths.

💡 Hint

Think about which step should handle missing values first.

Imputation must happen before scaling because StandardScaler cannot process NaN values.

✅ Best Practice

expert

Which practice ensures feature engineering pipelines support model reproducibility?

Choose the best practice that helps maintain reproducibility of machine learning models when using feature engineering pipelines.

A Version control the pipeline code and store pipeline artifacts with the model.

B Run the pipeline only on training data and ignore test data transformations.

C Manually apply transformations outside the pipeline for flexibility.

D Use random transformations without fixing seeds to increase data variety.

💡 Hint

Think about how to keep track of changes and ensure the same transformations are applied later.

Version controlling pipeline code and storing artifacts ensures the exact transformations can be reproduced for future use.

Practice

1. What is the main purpose of a feature engineering pipeline in MLOps?

easy

A. To automate and standardize data preparation steps

B. To deploy machine learning models to production

C. To monitor model performance after deployment

D. To collect raw data from external sources

Solution

1. Step 1: Understand the role of feature engineering pipelines

   Feature engineering pipelines automate the process of transforming raw data into features for model training and testing.

2. Step 2: Differentiate from other MLOps tasks

   Deploying models, monitoring, and data collection are separate tasks from feature engineering pipelines.

3. Final Answer:

   To automate and standardize data preparation steps -> Option A

4. Quick Check:

   Feature engineering pipeline = automate data prep [OK]

Hint: Feature pipelines automate data prep, not deployment or monitoring [OK]

Common Mistakes:

• Confusing feature pipelines with model deployment
• Thinking pipelines collect raw data
• Mixing up monitoring with feature engineering

2. Which of the following is the correct way to define a simple feature engineering pipeline step using scikit-learn's Pipeline?

easy

A. pipeline = Pipeline([('scaler', StandardScaler()), ('pca', PCA(n_components=2))])

B. pipeline = Pipeline('scaler', StandardScaler(), 'pca', PCA(n_components=2))

C. pipeline = Pipeline({'scaler': StandardScaler(), 'pca': PCA(n_components=2)})

D. pipeline = Pipeline(StandardScaler(), PCA(n_components=2))

Solution

1. Step 1: Recall scikit-learn Pipeline syntax

   Pipeline expects a list of tuples, each tuple with a name and a transformer object.

2. Step 2: Check each option's syntax

   pipeline = Pipeline([('scaler', StandardScaler()), ('pca', PCA(n_components=2))]) correctly uses a list of tuples. Options B, C, and D use incorrect argument formats.

3. Final Answer:

   pipeline = Pipeline([('scaler', StandardScaler()), ('pca', PCA(n_components=2))]) -> Option A

4. Quick Check:

   Pipeline needs list of (name, transformer) tuples [OK]

Hint: Pipeline needs list of (name, transformer) tuples [OK]

Common Mistakes:

• Passing arguments without list brackets
• Using dict instead of list of tuples
• Omitting step names in pipeline

3. Given the following pipeline code, what will be the output of pipeline.transform([[0, 0], [1, 1]])?

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

pipeline = Pipeline([
  ('scaler', StandardScaler()),
  ('pca', PCA(n_components=1))
])
pipeline.fit([[0, 0], [1, 1]])
result = pipeline.transform([[0, 0], [1, 1]])
print(result)

medium

A. Error: PCA requires more than one sample

B. [[0. 0.]
[1. 1.]]

C. [[0.5]
[0.5]]

D. [[-1.41421356]
[ 1.41421356]]

Solution

1. Step 1: Understand pipeline steps

   First, data is scaled to zero mean and unit variance, then PCA reduces to 1 component.

2. Step 2: Calculate transformed output

   Scaling [[0,0],[1,1]] centers data, PCA finds principal component; output is approximately [[-1.41421356],[1.41421356]].

3. Final Answer:

   [[-1.41421356] [ 1.41421356]] -> Option D

4. Quick Check:

   Scaling + PCA output = [[-1.41421356] [ 1.41421356]] [OK]

Hint: Scaling centers data; PCA output is principal component values [OK]

Common Mistakes:

• Expecting original data as output
• Confusing PCA output shape
• Assuming error due to small data

4. You have this pipeline code but it raises an error: ValueError: Expected 2D array, got 1D array instead. What is the likely cause?

pipeline = Pipeline([
  ('scaler', StandardScaler()),
  ('pca', PCA(n_components=1))
])
pipeline.fit([1, 2, 3, 4])

medium

A. Pipeline steps must be functions, not classes

B. Input to fit should be 2D array, not 1D list

C. StandardScaler requires integer inputs only

D. PCA cannot have n_components=1

Solution

1. Step 1: Analyze error message

   The error says input is 1D but 2D is expected for fit method.

2. Step 2: Check input format

   Input [1, 2, 3, 4] is a 1D list; fit expects 2D array like [[1], [2], [3], [4]].

3. Final Answer:

   Input to fit should be 2D array, not 1D list -> Option B

4. Quick Check:

   fit input shape must be 2D [OK]

Hint: fit() needs 2D array shape, not flat list [OK]

Common Mistakes:

• Passing 1D list instead of 2D array
• Misunderstanding PCA parameter limits
• Thinking StandardScaler restricts input types

5. You want to create a feature engineering pipeline that handles missing values by filling them with the median, then scales features, and finally selects the top 3 features using a model-based selector. Which pipeline setup is correct?

hard

A. Pipeline([('scaler', StandardScaler()), ('imputer', SimpleImputer(strategy='median')), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3))])

B. Pipeline([('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3)), ('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])

C. Pipeline([('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3))])

D. Pipeline([('imputer', SimpleImputer(strategy='mean')), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3)), ('scaler', StandardScaler())])

Solution

1. Step 1: Order pipeline steps logically

   Missing values must be handled first, then scaling, then feature selection.

2. Step 2: Check each option's correctness

   Pipeline([('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3))]) follows correct order and uses median for imputation. Pipeline([('scaler', StandardScaler()), ('imputer', SimpleImputer(strategy='median')), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3))]) swaps imputer and scaler incorrectly. Pipeline([('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3)), ('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]) starts with selector which needs complete data. Pipeline([('imputer', SimpleImputer(strategy='mean')), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3)), ('scaler', StandardScaler())]) uses mean instead of median and wrong order.

3. Final Answer:

   Pipeline([('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3))]) -> Option C

4. Quick Check:

   Impute -> scale -> select features [OK]

Hint: Impute missing -> scale -> select features in pipeline order [OK]

Common Mistakes:

• Placing scaler before imputer
• Selecting features before imputing missing values
• Using mean instead of median when median is required