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
The mental models of learning algorithms and their failure modes.
- Types of ML: supervised, unsupervised, semi-supervised
Subdomain 1.2: Model families: tree-based, linear, ensemble, neighbors
The mental models of learning algorithms and their failure modes.
- Model families: tree-based, linear, ensemble, neighbors
Subdomain 1.3: Key concepts: features, labels, training/test sets
The mental models of learning algorithms and their failure modes.
- Key concepts: features, labels, training/test sets
Subdomain 1.4: Overfitting and underfitting
The mental models of learning algorithms and their failure modes.
- Overfitting and underfitting
Subdomain 1.5: Bias/variance trade-off
The mental models of learning algorithms and their failure modes.
- Bias/variance trade-off
Domain 2: Model Building and Evaluation
Subdomain 2.1: Splitting with train_test_split
The fit-predict-score workflow and score interpretation.
- Splitting with train_test_split
Subdomain 2.2: Training with fit()
The fit-predict-score workflow and score interpretation.
- Training with fit()
Subdomain 2.3: Prediction with predict()
The fit-predict-score workflow and score interpretation.
- Prediction with predict()
Subdomain 2.4: Metrics: accuracy, precision, recall, F1, MSE, R²
The fit-predict-score workflow and score interpretation.
- Metrics: accuracy, precision, recall, F1, MSE, R²
Subdomain 2.5: Baseline comparison
The fit-predict-score workflow and score interpretation.
- Baseline comparison
Domain 3: Interpretation and Communication
Subdomain 3.1: Matplotlib and seaborn visualization
Visualizing and explaining results to non-technical audiences.
- Matplotlib and seaborn visualization
Subdomain 3.2: Confusion matrix and ROC curve reading
Visualizing and explaining results to non-technical audiences.
- Confusion matrix and ROC curve reading
Subdomain 3.3: Non-technical stakeholder communication
Visualizing and explaining results to non-technical audiences.
- Non-technical stakeholder communication
Subdomain 3.4: Uncertainty reporting
Visualizing and explaining results to non-technical audiences.
- Uncertainty reporting
Domain 4: Data Preprocessing
Subdomain 4.1: Loading parquet datasets
Loading, cleaning, and transforming data for model-ready inputs.
- Loading parquet datasets
Subdomain 4.2: Scatterplots and boxplots for inspection
Loading, cleaning, and transforming data for model-ready inputs.
- Scatterplots and boxplots for inspection
Subdomain 4.3: Identifying encoding issues
Loading, cleaning, and transforming data for model-ready inputs.
- Identifying encoding issues
Subdomain 4.4: Imputation with SimpleImputer
Loading, cleaning, and transforming data for model-ready inputs.
- Imputation with SimpleImputer
Subdomain 4.5: Scaling: StandardScaler, MinMaxScaler
Loading, cleaning, and transforming data for model-ready inputs.
- Scaling: StandardScaler, MinMaxScaler
Subdomain 4.6: Encoding: OrdinalEncoder, OneHotEncoder
Loading, cleaning, and transforming data for model-ready inputs.
- Encoding: OrdinalEncoder, OneHotEncoder
Subdomain 4.7: ColumnTransformer for combining steps
Loading, cleaning, and transforming data for model-ready inputs.
- ColumnTransformer for combining steps
Domain 5: Model Selection and Validation
Subdomain 5.1: Cross-validation: KFold, ShuffleSplit
Choosing, tuning, and validating models with correct splits.
- Cross-validation: KFold, ShuffleSplit
Subdomain 5.2: Learning and validation curves
Choosing, tuning, and validating models with correct splits.
- Learning and validation curves
Subdomain 5.3: Hyperparameter tuning: GridSearchCV, RandomSearchCV
Choosing, tuning, and validating models with correct splits.
- Hyperparameter tuning: GridSearchCV, RandomSearchCV
Subdomain 5.4: Coefficient stability across splits
Choosing, tuning, and validating models with correct splits.
- Coefficient stability across splits
Techniques & products