Free Practice Questions for Scikit-learn Associate Practitioner Certification Certification
Study with 400 exam-style practice questions designed to help you prepare for the Scikit-learn Associate Practitioner Certification.
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Exam Details
Key information about Scikit-learn Associate Practitioner Certification
- Multiple choice
Junior data scientists
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Machine Learning Concepts
Subdomain 1.1: Types of ML: supervised, unsupervised, semi-supervised
Types of ML: supervised, unsupervised, semi-supervised
Subdomain 1.2: Model families: tree-based, linear, ensemble, neighbors
Model families: tree-based, linear, ensemble, neighbors
Subdomain 1.3: Key concepts: features, labels, training/test sets
Key concepts: features, labels, training/test sets
Subdomain 1.4: Overfitting and underfitting
Overfitting and underfitting
Subdomain 1.5: Bias/variance trade-off
Bias/variance trade-off
Domain 2: Model Building and Evaluation
Subdomain 2.1: Splitting with train_test_split
Splitting with train_test_split
Subdomain 2.2: Training with fit()
Training with fit()
Subdomain 2.3: Prediction with predict()
Prediction with predict()
Subdomain 2.4: Metrics: accuracy, precision, recall, F1, MSE, R²
Metrics: accuracy, precision, recall, F1, MSE, R²
Subdomain 2.5: Baseline comparison
Baseline comparison
Domain 3: Interpretation and Communication
Subdomain 3.1: Matplotlib and seaborn visualization
Matplotlib and seaborn visualization
Subdomain 3.2: Confusion matrix and ROC curve reading
Confusion matrix and ROC curve reading
Subdomain 3.3: Non-technical stakeholder communication
Non-technical stakeholder communication
Subdomain 3.4: Uncertainty reporting
Uncertainty reporting
Domain 4: Data Preprocessing
Subdomain 4.1: Loading parquet datasets
Loading parquet datasets
Subdomain 4.2: Scatterplots and boxplots for inspection
Scatterplots and boxplots for inspection
Subdomain 4.3: Identifying encoding issues
Identifying encoding issues
Subdomain 4.4: Imputation with SimpleImputer
Imputation with SimpleImputer
Subdomain 4.5: Scaling: StandardScaler, MinMaxScaler
Scaling: StandardScaler, MinMaxScaler
Subdomain 4.6: Encoding: OrdinalEncoder, OneHotEncoder
Encoding: OrdinalEncoder, OneHotEncoder
Subdomain 4.7: ColumnTransformer for combining steps
ColumnTransformer for combining steps
Domain 5: Model Selection and Validation
Subdomain 5.1: Cross-validation: KFold, ShuffleSplit
Cross-validation: KFold, ShuffleSplit
Subdomain 5.2: Learning and validation curves
Learning and validation curves
Subdomain 5.3: Hyperparameter tuning: GridSearchCV, RandomSearchCV
Hyperparameter tuning: GridSearchCV, RandomSearchCV
Subdomain 5.4: Coefficient stability across splits
Coefficient stability across splits
Techniques & products