Data preparation and feature engineering: Data preparation and feature engineering involve transforming raw data into a format suitable for ML models and creating new features to improve model performance. This skill is measured in the test to evaluate a candidate's proficiency in data preprocessing and feature extraction techniques.
Model building and evaluation: Model building and evaluation focus on creating ML models using different algorithms and techniques, along with assessing their performance and accuracy. This skill is measured in the test to gauge a candidate's ability to construct effective ML models and evaluate their results with appropriate metrics.
Azure ML algorithms: Azure ML algorithms include a range of pre-built ML models and techniques that can be used for various types of data analysis and prediction tasks. This skill is measured in the test to determine a candidate's familiarity with different Azure ML algorithms and their suitability for specific scenarios.
Model deployment and management: Model deployment and management involve the processes of deploying ML models into production environments, monitoring their performance, and making necessary updates and improvements. This skill is measured in the test to assess a candidate's understanding of the end-to-end ML model lifecycle and their ability to implement deployment and management strategies using Azure ML.
Azure ML Pipelines: Azure ML Pipelines enables the creation and orchestration of ML workflows, automating the steps involved in data preparation, model training, and deployment. This skill is measured in the test to evaluate a candidate's proficiency in designing and implementing ML pipelines using Azure ML.
Hyperparameter tuning: Hyperparameter tuning involves finding the optimal values for the hyperparameters of an ML model to maximize its performance and generalization. This skill is measured in the test to assess a candidate's knowledge and expertise in applying techniques for hyperparameter tuning using Azure ML.
Azure AutoML: Azure AutoML is a feature in Azure ML that automates the process of model selection and hyperparameter tuning, enabling the development of high-performing ML models with minimal manual intervention. This skill is measured in the test to gauge a candidate's understanding of Azure AutoML and their ability to utilize its capabilities for efficient ML model development.
Azure ML Designer: Azure ML Designer is a no-code tool in Azure ML that allows users to visually build, train, and deploy ML models using a drag-and-drop interface. This skill is measured in the test to determine a candidate's familiarity with Azure ML Designer and their ability to leverage its functionalities for ML model development.
Azure ML Interpretability: Azure ML Interpretability focuses on understanding and interpreting the factors influencing the predictions made by ML models. This skill is measured in the test to evaluate a candidate's knowledge and skills in analyzing and interpreting the results and behaviors of ML models using Azure ML Interpretability features.
Azure ML Model Explainability: Azure ML Model Explainability deals with providing explanations for the predictions made by ML models, helping to build trust and understanding in their decision-making process. This skill is measured in the test to assess a candidate's proficiency in utilizing Azure ML Model Explainability features to provide transparent and interpretable ML models.
Azure ML Model Deployment: Azure ML Model Deployment involves deploying trained ML models as web services or APIs, enabling real-time predictions and integration with other applications. This skill is measured in the test to gauge a candidate's ability to deploy ML models in production environments using Azure ML deployment techniques.
