This exam measures your ability to accomplish the following technical tasks: design and prepare a machine learning solution; explore data and train models; prepare a model for deployment; and deploy and retrain a model.
As a candidate for this exam, you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure.
Your responsibilities for this role include:
◉ Designing and creating a suitable working environment for data science workloads.
◉ Exploring data.
◉ Training machine learning models.
◉ Implementing pipelines.
◉ Running jobs to prepare for production.
◉ Managing, deploying, and monitoring scalable machine learning solutions.
As a candidate for this exam, you should have knowledge and experience in data science by using:
◉ Azure Machine Learning
◉ MLflow
Microsoft Designing and Implementing a Data Science Solution on Azure Exam Summary:
| Exam Name | Microsoft Certified - Azure Data Scientist Associate |
| Exam Code | DP-100 |
| Exam Price | $165 (USD) |
| Exam Price | 120 mins |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Books / Training | DP-100T01-A: Designing and Implementing a Data Science Solution on Azure |
| Sample Questions | Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions |
| Practice Exam | Microsoft DP-100 Certification Practice Exam |
Microsoft DP-100 Exam Syllabus Topics:
| Topic | Details |
| 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 - Create and manage registries |
| Manage data in an Azure Machine Learning workspace | - Select Azure Storage resources - Register and maintain datastores - 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 Apache Spark pools - Monitor compute utilization |
| Explore data and train models (35-40%) | |
| Explore data by using data assets and data stores | - Access and wrangle data during interactive development - Wrangle interactive data with Apache Spark |
| Create models by using the Azure Machine Learning designer | - Create a training pipeline - Consume data assets from the designer - Use custom code components in 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 - 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 - Track model training by using MLflow - Evaluate a model - Train a model by using Python SDKv2 - 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 online deployment - Configure compute for a batch deployment - Deploy a model to an online endpoint - Deploy a model to a batch endpoint - Test an online deployed service - Invoke the batch endpoint to start a batch scoring job |
| Apply machine learning operations (MLOps) practices | - Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub - Automate model retraining based on new data additions or data changes - Define event-based retraining triggers |
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