Free Practice Questions for Scikit-learn Professional Practitioner Certification Certification
- Guide checked for updates:
- 9 Oct 2026
- Question bank created:
- 19 Mar 2026
- Question bank last updated:
- 9 Oct 2026
Study with 399 exam-style practice questions designed to help you prepare for the Scikit-learn Professional Practitioner Certification.
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Exam Details
Key information about Scikit-learn Professional Practitioner Certification
- Multiple choice
- True/False
- Fill in the blank
Professional
mid-level data scientist
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Machine Learning Concepts
1.1: Supervised and unsupervised, regression, classification, clustering, dimensional reduction
Supervised and unsupervised, regression, classification, clustering, dimensional reduction
1.2: Model families: tree-based, linear, ensemble, neighbors
Model families, tree-based, linear, ensemble, neighbors
1.3: Regularization: L1, L2, Elasticnet
Regularization, L1, L2, Elasticnet
1.4: Hard and soft predictions, predict vs predict_proba
Hard and soft predictions, predict vs predict_proba
1.5: Overfitting and underfitting, impact on soft predictions
Overfitting and underfitting, impact on soft predictions
2: Model Building and Evaluation
2.1: Linear models as baselines
Linear models as baselines
2.2: Handling correlation with regularization and feature selection
Handling correlation with regularization and feature selection
2.3: Bagging and boosting, the working ensemble methods
Bagging and boosting, the working ensemble methods
2.4: Choosing metrics for outliers and imbalanced settings
Choosing metrics for outliers and imbalanced settings
3: Interpretation and Communication
3.1: Visualizing results with intermediate matplotlib and seaborn techniques
Visualizing results with intermediate matplotlib and seaborn techniques
3.2: Interpreting model outputs and performance metrics
Interpreting model outputs and performance metrics
3.3: Communicating results to non-technical stakeholders
Communicating results to non-technical stakeholders
4: Data Preprocessing
4.1: Loading parquet datasets
Loading parquet datasets
4.2: Heatmaps and PCA for first look
Heatmaps and PCA for first look
4.3: Identifying strongly correlated features
Identifying strongly correlated features
4.4: Missing values in the target via label propagation
Missing values in the target via label propagation
4.5: Feature engineering with PolynomialFeatures, SplineTransformer
Feature engineering with PolynomialFeatures, SplineTransformer
4.6: Combining features with FeatureUnion
Combining features with FeatureUnion
5: Model Selection and Validation
5.1: Cross-validation with group structure and non i.i.d. data
Cross-validation with group structure and non i.i.d. data
5.2: Hyperparameter tuning: GridSearchCV, RandomSearchCV
Hyperparameter tuning, GridSearchCV, RandomSearchCV
5.3: Stability of optimal hyperparameters via nested cross-validation
Stability of optimal hyperparameters via nested cross-validation
Techniques & products