Free Practice Questions for Scikit-learn Expert Practitioner Certification Certification
- Guide checked for updates:
- 18 Sep 2026
- Question bank created:
- 19 Mar 2026
- Question bank last updated:
- 9 Oct 2026
Study with 510 exam-style practice questions designed to help you prepare for the Scikit-learn Expert Practitioner Certification.
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Exam Details
Key information about Scikit-learn Expert Practitioner Certification
- Multiple choice
- Ordering
Expert Practitioner
Senior data scientists
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Machine Learning Concepts
1.1: Supervised and unsupervised learning, model families
Supervised and unsupervised learning, model families
1.2: Loss functions and splitting criteria
Loss functions and splitting criteria
1.3: Feature selection methods
Feature selection methods
1.4: Calibration vs. ranking power differentiation
Calibration vs. ranking power differentiation
2: Model Building and Evaluation
2.1: Custom estimators following the sklearn API
Custom estimators following the sklearn API
2.2: Metadata routing
Metadata routing
2.3: Calibration plotting (reliability diagrams)
Calibration plotting (reliability diagrams)
2.4: Post-calibration techniques: isotonic, Platt scaling
Post-calibration techniques: isotonic, Platt scaling
3: Interpretation and Communication
3.1: Partial dependence plots
Partial dependence plots
3.2: Permutation importance
Permutation importance
3.3: Pipeline diagnosis and feature selection pitfalls
Pipeline diagnosis and feature selection pitfalls
3.4: Reading and explaining others’ code
Reading and explaining others’ code
4: Data Preprocessing
4.1: Loading and joining parquet datasets
Loading and joining parquet datasets
4.2: Plot interpretation for model selection
Plot interpretation for model selection
4.3: Multi-source data combining
Multi-source data combining
4.4: Feature engineering including lagged features
Feature engineering including lagged features
5: Model Selection and Validation
5.1: Proper scoring rules for probabilistic outputs
Proper scoring rules for probabilistic outputs
5.2: Calibration-focused metrics in GridSearchCV
Calibration-focused metrics in GridSearchCV
5.3: Custom scorers with make_scorer
Custom scorers with make_scorer
6: Model Deployment
6.1: Serialization with joblib and pickle
Serialization with joblib and pickle
6.2: Secure serialization with skops
Secure serialization with skops
6.3: Trade-offs between formats
Trade-offs between formats
Techniques & products