Free Practice Questions for Scikit-learn Associate 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 400 exam-style practice questions designed to help you prepare for the Scikit-learn Associate Practitioner Certification.
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Key information about Scikit-learn Associate Practitioner Certification
- Multiple choice
Junior data scientists
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Machine learning concepts
1.1: Types of ML, supervised, unsupervised, semi-supervised
Types of ML, supervised, unsupervised, semi-supervised
1.2: Model families, tree-based, linear, ensemble, neighbors
Model families, tree-based, linear, ensemble, neighbors
1.3: Key concepts, features, labels, training and test sets
Key concepts, features, labels, training and test sets
1.4: Overfitting and underfitting
Overfitting and underfitting
1.5: The bias / variance trade-off
The bias / variance trade-off
2: Model building and evaluation
2.1: Splitting datasets with train_test_split
Splitting datasets with train_test_split
2.2: Training models with fit()
Training models with fit()
2.3: Predicting with predict()
Predicting with predict()
2.4: Evaluating with accuracy, precision, recall, F1, MSE, R squared
Evaluating with accuracy, precision, recall, F1, MSE, R squared
2.5: Interpreting score against a dummy baseline
Interpreting score against a dummy baseline
3: Interpretation and communication
3.1: Visualizing results with matplotlib and seaborn
Visualizing results with matplotlib and seaborn
3.2: Reading a confusion matrix and an ROC curve
Reading a confusion matrix and an ROC curve
3.3: Explaining performance to non-technical stakeholders
Explaining performance to non-technical stakeholders
3.4: Reporting uncertainty without hand-waving
Reporting uncertainty without hand-waving
4: Data preprocessing
4.1: Loading parquet datasets
Loading parquet datasets
4.2: Scatterplots and boxplots for first look
Scatterplots and boxplots for first look
4.3: Spotting wrongly-encoded columns (float as string, etc.)
Spotting wrongly-encoded columns (float as string, etc.)
4.4: Imputation with SimpleImputer
Imputation with SimpleImputer
4.5: Feature scaling, StandardScaler, MinMaxScaler
Feature scaling, StandardScaler, MinMaxScaler
4.6: Encoding with OrdinalEncoder, OneHotEncoder
Encoding with OrdinalEncoder, OneHotEncoder
4.7: Combining steps with ColumnTransformer
Combining steps with ColumnTransformer
5: Model selection and validation
5.1: Cross-validation, KFold, ShuffleSplit, and friends
Cross-validation, KFold, ShuffleSplit, and friends
5.2: Reading learning and validation curves
Reading learning and validation curves
5.3: Hyperparameter tuning with GridSearchCV, RandomSearchCV
Hyperparameter tuning with GridSearchCV, RandomSearchCV
5.4: Stability of learned coefficients across splits
Stability of learned coefficients across splits
Techniques & products