Free Practice Questions for Scikit-learn Expert Practitioner Certification Certification
Study with 461 exam-style practice questions designed to help you prepare for the Scikit-learn Expert Practitioner Certification.
Start Practicing
All Domains
Practice with randomly mixed questions from all topics
Domain Mode
Practice questions from a specific topic area
Quiz History
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)
Domain 1: Machine learning concepts
Subdomain 1.1: Supervised and unsupervised, regression, classification, clustering, dimensional reduction
Supervised and unsupervised, regression, classification, clustering, dimensional reduction
Subdomain 1.2: Model families, tree-based, linear, ensemble, neighbors
Model families, tree-based, linear, ensemble, neighbors
Subdomain 1.3: Loss functions and surrogate loss
Loss functions and surrogate loss
Subdomain 1.4: Splitting criteria in decision trees
Splitting criteria in decision trees
Subdomain 1.5: Filter, wrapper, and embedded methods for feature selection
Filter, wrapper, and embedded methods for feature selection
Subdomain 1.6: Calibration (expected calibration error) vs ranking power (ROC AUC, GINI)
Calibration (expected calibration error) vs ranking power (ROC AUC, GINI)
Domain 2: Model building and evaluation
Subdomain 2.1: Create your own estimator, NearestCentroid, recommender systems, transformers
Create your own estimator, NearestCentroid, recommender systems, transformers
Subdomain 2.2: Metadata routing across estimators and CV
Metadata routing across estimators and CV
Subdomain 2.3: Calibration plots with CalibrationDisplay, post-calibration with CalibratedClassifierCV
Calibration plots with CalibrationDisplay, post-calibration with CalibratedClassifierCV
Domain 3: Interpretation and communication
Subdomain 3.1: Partial dependence plots, non-linear impact on the target
Partial dependence plots, non-linear impact on the target
Subdomain 3.2: Permutation importance
Permutation importance
Subdomain 3.3: Diagnosing methodology, given a plot, name the failure
Diagnosing methodology, given a plot, name the failure
Subdomain 3.4: Pitfalls (e.g. feature selection inside or outside the pipeline)
Pitfalls (e.g. feature selection inside or outside the pipeline)
Subdomain 3.5: Code comprehension and good practices
Code comprehension and good practices
Domain 4: Data preprocessing
Subdomain 4.1: Loading parquet datasets
Loading parquet datasets
Subdomain 4.2: Reading plots to decide which family of models fits
Reading plots to decide which family of models fits
Subdomain 4.3: Combining data from multiple sources
Combining data from multiple sources
Subdomain 4.4: Adding new features, lagged features for time-based data
Adding new features, lagged features for time-based data
Domain 5: Model selection and validation
Subdomain 5.1: Hyperparameter tuning with proper scoring rules (calibration)
Hyperparameter tuning with proper scoring rules (calibration)
Domain 6: Model deployment
Subdomain 6.1: Saving and loading trained models with joblib, pickle, or skops
Saving and loading trained models with joblib, pickle, or skops
Subdomain 6.2: Trade-offs between serializers, security, and forward compatibility
Trade-offs between serializers, security, and forward compatibility
Techniques & products