Free Practice Questions for Scikit-learn Associate Practitioner Certification Certification
Study with 368 exam-style practice questions designed to help you prepare for the Scikit-learn Associate Practitioner Certification.
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Exam Information
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 Machine Learning : Supervised, Unsupervised, and Semi-supervised learning.
Types of Machine Learning : Supervised, Unsupervised, and Semi-supervised learning.
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 and test sets
Key concepts : features, labels, training and test sets
Subdomain 1.4: Model overfitting and underfitting
Model overfitting and underfitting
Subdomain 1.5: Bias/variance trade-off
Bias/variance trade-off
Domain 2: Model building and evaluation
Subdomain 2.1: Splitting datasets into training and testing sets using train_test_split
Splitting datasets into training and testing sets using train_test_split
Subdomain 2.2: Training ML models using the fit() method
Training ML models using the fit() method
Subdomain 2.3: Making predictions using the predict() method
Making predictions using the predict() method
Subdomain 2.4: Evaluating model performance with most common metrics (accuracy, precision, recall, F1 score, confusion matrix, mean squared error, R-squared)
Evaluating model performance with most common metrics (accuracy, precision, recall, F1 score, confusion matrix, mean squared error, R-squared)
Subdomain 2.5: Interpreting score with respect to dummy models
Interpreting score with respect to dummy models
Domain 3: Interpretation of results & communication
Subdomain 3.1: Visualizing model results using basic plotting techniques (matplotlib, seaborn)
Visualizing model results using basic plotting techniques (matplotlib, seaborn)
Subdomain 3.2: Interpreting and communicating model outputs and performance metrics to non-technical stakeholders
Interpreting and communicating model outputs and performance metrics to non-technical stakeholders
Domain 4: Data preprocessing
Subdomain 4.1: Loading parquet datasets
Loading parquet datasets
Subdomain 4.2: Visualizing data with basic plotting techniques (scatterplot, boxplot)
Visualizing data with basic plotting techniques (scatterplot, boxplot)
Subdomain 4.3: Identify wrongly encoded predictive columns (e.g. float encoded as string)
Identify wrongly encoded predictive columns (e.g. float encoded as string)
Subdomain 4.4: Handling missing values using imputation SimpleImputer
Handling missing values using imputation SimpleImputer
Subdomain 4.5: Correct choice of feature scaling using StandardScaler , MinMaxScaler , etc
Correct choice of feature scaling using StandardScaler , MinMaxScaler , etc
Subdomain 4.6: Encoding categorical data using OrdinalEncoder and OneHotEncoder
Encoding categorical data using OrdinalEncoder and OneHotEncoder
Subdomain 4.7: Combining preprocessing steps with ColumnTransformer
Combining preprocessing steps with ColumnTransformer
Domain 5: Model selection and validation
Subdomain 5.1: Understanding and implementing cross-validation techniques (KFold, ShuffleSplit, etc)
Understanding and implementing cross-validation techniques (KFold, ShuffleSplit, etc)
Subdomain 5.2: Learning and validation curves
Learning and validation curves
Subdomain 5.3: Performing hyperparameter tuning using GridSearchCV, RandomSearchCV
Performing hyperparameter tuning using GridSearchCV, RandomSearchCV
Subdomain 5.4: Stability of learned coefficients across splits
Stability of learned coefficients across splits
Techniques & products