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The Microsoft Azure Data Scientist Associate - DP-100
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Microsoft Certified: Azure Data Scientist Associate - DP-100 Syllabus
Design and prepare a machine learning solution (20-25%)
Design a machine learning solution
- Determine the appropriate compute specifications for a training workload
- Describe model deployment requirements
- Select which development approach to use to build or train a model
Manage an Azure Machine Learning workspace
- Create an Azure Machine Learning workspace
- Manage a workspace by using developer tools for workspace interaction
- Set up Git integration for source control
Manage data in an Azure Machine Learning workspace
- Select Azure Storage resources
- Register and maintain data stores
- Create and manage data assets
Manage compute for experiments in Azure Machine Learning
- Create compute targets for experiments and training
- Select an environment for a machine learning use case
- Configure attached compute resources, including Azure Databricks and Azure Synapse Analytics
- Monitor compute utilization
Explore data and train models (35-40%)
Explore data by using data assets and data stores
- Load and transform data
- Analyze data by using Azure Data Explorer
- Use differential privacy
Create models by using the Azure Machine Learning designer
- Create a training pipeline
- Consume data assets from the designer
- Use designer components to define a pipeline data flow
- Use custom code components in the designer
- Evaluate the model, including responsible AI guidelines
Use automated machine learning to explore optimal models
- Use automated machine learning for tabular data
- Use automated machine learning for computer vision
- Use automated machine learning for natural language processing (NLP)
- Select and understand training options, including preprocessing and algorithms
- Evaluate an automated machine learning run, including responsible AI guidelines
Use notebooks for custom model training - Develop code by using a compute instance
- Consume data in a notebook
- Track model training by using MLflow
- Evaluate a model
- Train a model by using Python SDK
- Use the terminal to configure a compute instance
Tune hyperparameters with Azure Machine Learning
- select a sampling method
- define the search space
- define the primary metric
- define early termination options
Prepare a model for deployment (20-25%)
Run model training scripts
- Configure job run settings for a script
- Configure compute for a job run
- Consume data from a data asset in a job
- Run a script as a job by using Azure Machine Learning
- Use MLflow to log metrics from a job run
- Use logs to troubleshoot job run errors
- Configure an environment for a job run
- Define parameters for a job
Implement training pipelines
- Create a pipeline
- Pass data between steps in a pipeline
- Run and schedule a pipeline
- Monitor pipeline runs
- Create custom components
- Use component-based pipelines
Manage models in Azure Machine Learning
- Describe MLflow model output
- Identify an appropriate framework to package a model
- Assess a model by using responsible AI guidelines
Deploy and retrain a model (10-15%)
Deploy a model - Configure settings for real-time deployment
- Configure compute for a batch deployment
- Deploy a model to a real-time endpoint
- Deploy a model to a batch endpoint
- Test a real-time deployed service
- Invoke the batch endpoint to start a batch scoring job
Apply machine learning operations (MLOps) practices
- Trigger an Azure Machine Learning pipeline, including from Azure DevOps or GitHub
- Automate model retraining based on new data additions or data changes
- Define event-based retraining triggers
To ensure success in Microsoft Designing and Implementing a Data Science Solution on Azure certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Designing and Implementing a Data Science Solution on Microsoft Azure (DP-100) exam.
