Machine Learning in Azure Online Test

The Machine Learning in Azure Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

• Understanding the basics of Azure Machine Learning
• Data preprocessing and cleaning techniques using Azure ML
• Feature engineering and selection with Azure ML
• Building and evaluating machine learning models in Azure ML
• Working with various Azure ML algorithms for classification and regression
• Deploying and managing models in Azure ML
• Creating and executing Azure ML pipelines
• Tuning hyperparameters in Azure ML
• Utilizing Azure AutoML for automated model selection and tuning
• Designing machine learning workflows using Azure ML Designer

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

• 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.

• Full list of covered topics

  The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Machine Learning in Azure Test to be based on.

  Azure ML workspaces

  Azure ML Studio

  Azure ML datasets

  Azure ML experiments

  Data preprocessing

  Feature engineering

  Feature selection

  Exploratory data analysis

  Data visualization

  Model building

  Model evaluation

  Cross-validation

  Evaluation metrics

  Azure ML algorithms

  Supervised learning

  Unsupervised learning

  Classification algorithms

  Regression algorithms

  Clustering algorithms

  Dimensionality reduction

  Azure ML model deployment

  Web service deployment

  Model versioning

  Model monitoring

  Azure ML Pipelines

  Pipeline creation

  Pipeline scheduling

  Pipeline monitoring

  Hyperparameter tuning

  Grid search

  Random search

  Bayesian optimization

  Azure AutoML

  Automated model selection

  Automated hyperparameter tuning

  Automated feature engineering

  Azure ML Designer

  Drag-and-drop model development

  Custom module creation

  Pipeline execution

  Azure ML Interpretability

  Feature importance

  Model explanations

  Local interpretability

  Global interpretability

  Azure ML Model Explainability

  Interpretable machine learning

  Explainable AI

  Counterfactual explanations

  Azure ML Model Deployment

  Azure Kubernetes Service (AKS)

  Azure Container Instances (ACI)

  Application insights

  Azure ML Model Management

  Model versioning

  Model deployment slots

  Model scaling and performance

  Continuous integration and deployment (CI/CD)

• Machine Learning Engineer
• Data Scientist
• Data Analyst
• AI Engineer
• Software Engineer
• Data Engineer
• Business Analyst
• Research Scientist
• AI Consultant
• AI Researcher

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

• Interpreting and explaining models using Azure ML Interpretability
• Deploying models for production with Azure ML Model Deployment
• Understanding Azure Databricks and its integration with Azure ML
• Utilizing Azure ML for Natural Language Processing (NLP) tasks
• Implementing computer vision solutions using Azure ML
• Working with time series data in Azure ML
• Handling distributed computing with Azure ML
• Utilizing Azure ML for reinforcement learning
• Applying anomaly detection techniques in Azure ML