Free Practice Questions for Scikit-learn Expert Practitioner Certification Certification

🔄 Last checked for updates July 6th, 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

Official study guide

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Question formats CertSafari offers

Multiple choice
Ordering

level:

Expert Practitioner

target audience:

Senior data scientists

Exam Topics & Skills Assessed

Skills measured (from the official study guide)

Domain 1: Machine Learning Concepts

Subdomain 1.1: Supervised and unsupervised learning, model families

Supervised and unsupervised learning, model families

Subdomain 1.2: Loss functions and splitting criteria

Loss functions and splitting criteria

Subdomain 1.3: Feature selection methods

Feature selection methods

Subdomain 1.4: Calibration vs. ranking power differentiation

Calibration vs. ranking power differentiation

Domain 2: Model Building and Evaluation

Subdomain 2.1: Custom estimators following the sklearn API

Custom estimators following the sklearn API

Subdomain 2.2: Metadata routing

Metadata routing

Subdomain 2.3: Calibration plotting (reliability diagrams)

Calibration plotting (reliability diagrams)

Subdomain 2.4: Post-calibration techniques: isotonic, Platt scaling

Post-calibration techniques: isotonic, Platt scaling

Domain 3: Interpretation and Communication

Subdomain 3.1: Partial dependence plots

Partial dependence plots

Subdomain 3.2: Permutation importance

Permutation importance

Subdomain 3.3: Pipeline diagnosis and feature selection pitfalls

Pipeline diagnosis and feature selection pitfalls

Subdomain 3.4: Reading and explaining others’ code

Reading and explaining others’ code

Domain 4: Data Preprocessing

Subdomain 4.1: Loading and joining parquet datasets

Loading and joining parquet datasets

Subdomain 4.2: Plot interpretation for model selection

Plot interpretation for model selection

Subdomain 4.3: Multi-source data combining

Multi-source data combining

Subdomain 4.4: Feature engineering including lagged features

Feature engineering including lagged features

Domain 5: Model Selection and Validation

Subdomain 5.1: Proper scoring rules for probabilistic outputs

Proper scoring rules for probabilistic outputs

Subdomain 5.2: Calibration-focused metrics in GridSearchCV

Calibration-focused metrics in GridSearchCV

Subdomain 5.3: Custom scorers with make_scorer

Custom scorers with make_scorer

Domain 6: Model Deployment

Subdomain 6.1: Serialization with joblib and pickle

Serialization with joblib and pickle

Subdomain 6.2: Secure serialization with skops

Secure serialization with skops

Subdomain 6.3: Trade-offs between formats

Trade-offs between formats

Techniques & products

scikit-learn

Supervised learning

Unsupervised learning

Regression

Classification

Clustering

Dimensional reduction

Tree-based models

Linear models

Ensemble models

Neighbors models

Loss functions

Surrogate loss

Decision Trees

Splitting criteria

Feature selection

Filter methods

Wrapper methods

Embedded methods

Calibration

Expected calibration error

Ranking power

ROC AUC

GINI

Estimators

NearestCentroid

Recommender systems

Transformers

Metadata routing

CalibrationDisplay

CalibratedClassifierCV

Explainability

Interpretability

Partial dependence plots

Permutation importance

Model diagnostics

Debugging

Pipelines

Parquet datasets

Data wrangling

Hyperparameter tuning

joblib

pickle

skops

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