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Microsoft DP-100 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Manage Azure resources for machine learning (25-30%) | |
| Create an Azure Machine Learning workspace | - create an Azure Machine Learning workspace - configure workspace settings - manage a workspace by using Azure Machine Learning studio |
| Manage data in an Azure Machine Learning workspace | - select Azure storage resources - register and maintain datastores - create and manage datasets |
| Manage compute for experiments in Azure Machine Learning | - determine the appropriate compute specifications for a training workload - create compute targets for experiments and training - configure Attached Compute resources including Azure Databricks - monitor compute utilization |
| Implement security and access control in Azure Machine Learning | - determine access requirements and map requirements to built-in roles - create custom roles - manage role membership - manage credentials by using Azure Key Vault |
| Set up an Azure Machine Learning development environment | - create compute instances - share compute instances - access Azure Machine Learning workspaces from other development environments |
| Set up an Azure Databricks workspace | - create an Azure Databricks workspace - create an Azure Databricks cluster - create and run notebooks in Azure Databricks - link and Azure Databricks workspace to an Azure Machine Learning workspace |
Run Experiments and Train Models (20-25%) | |
| Create models by using the Azure Machine Learning Designer | - create a training pipeline by using Azure Machine Learning designer - ingest data in a designer pipeline - use designer modules to define a pipeline data flow - use custom code modules in designer |
| Run model training scripts | - create and run an experiment by using the Azure Machine Learning SDK - configure run settings for a script - consume data from a dataset in an experiment by using the Azure Machine Learning SDK - run a training script on Azure Databricks compute - run code to train a model in an Azure Databricks notebook |
| Generate metrics from an experiment run | - log metrics from an experiment run - retrieve and view experiment outputs - use logs to troubleshoot experiment run errors - use MLflow to track experiments - track experiments running in Azure Databricks |
| Use Automated Machine Learning to create optimal models | - use the Automated ML interface in Azure Machine Learning studio - use Automated ML from the Azure Machine Learning SDK - select pre-processing options - select the algorithms to be searched - define a primary metric - get data for an Automated ML run - retrieve the best model |
| Tune hyperparameters with Azure Machine Learning | - select a sampling method - define the search space - define the primary metric - define early termination options - find the model that has optimal hyperparameter values |
Deploy and operationalize machine learning solutions (35-40%) | |
| Select compute for model deployment | - consider security for deployed services - evaluate compute options for deployment |
| Deploy a model as a service | - configure deployment settings - deploy a registered model - deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint - consume a deployed service - troubleshoot deployment container issues |
| Manage models in Azure Machine Learning | - register a trained model - monitor model usage - monitor data drift |
| Create an Azure Machine Learning pipeline for batch inferencing | - configure a ParallelRunStep - configure compute for a batch inferencing pipeline - publish a batch inferencing pipeline - run a batch inferencing pipeline and obtain outputs - obtain outputs from a ParallelRunStep |
| Publish an Azure Machine Learning designer pipeline as a web service | - create a target compute resource - configure an Inference pipeline - consume a deployed endpoint |
| Implement pipelines by using the Azure Machine Learning SDK | - create a pipeline - pass data between steps in a pipeline - run a pipeline - monitor pipeline runs |
| Apply ML Ops practices | - trigger an Azure Machine Learning pipeline from Azure DevOps - automate model retraining based on new data additions or data changes - refactor notebooks into scripts - implement source control for scripts |
Implement Responsible ML (5-10%) | |
| Use model explainers to interpret models | - select a model interpreter - generate feature importance data |
| Describe fairness considerations for models | - evaluate model fairness based on prediction disparity - mitigate model unfairness |
| Describe privacy considerations for data | - describe principles of differential privacy - specify acceptable levels of noise in data and the effects on privacy |
Microsoft DP-100: Requirements
The Microsoft DP-100 exam is created for the individuals who are involved in implementing machine learning techniques. The candidates for this certification test should have an in-depth knowledge of identifying and preparing the development environment, applying scientific data and rigor exploration techniques to acquire actionable insights along with communicating results to the stakeholders. They should also possess the skills in preparing data for modeling and developing models. In addition, they should also be able to apply machine learning techniques to train, deploy and evaluate models to build any AI solutions, which satisfy the business objectives.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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Basic Exam Traits
The Microsoft DP-100 is an associate-level job role-based exam. Its structure is the same as any other exam falling in this category. As per the standard test format, DP-100 exam is likely to contain 40-60 exam questions. As far as question format is concerned, Microsoft doesn’t follow a set pattern. The exam is likely to cover questions based on the MCQ pattern. However, the odds of including items based on other patterns like case studies and best answers are also high. What’s more, there is no exact passing score as there is no fixed number of questions and it might change as per the final number of tasks. Nonetheless, a test-taker must secure 70% passing marks to be called successful in the official exam. Currently, this test can be taken in English, Japanese, Chinese (Simplified), and Korean worldwide. The standard exam fee is $165 and is likely to get changed as per the location of the test-taker.
What Certificate You Will Get by Passing DP-100
DP-100 syllabus includes concepts of machine learning workloads, handling data experiments, optimizing and managing models, and many more. This is an associate-level test that creates a strong base for candidates' future professional development. This Microsoft exam is associated with the Microsoft Certified: Azure Data Scientist Associate certification. This is the only test that one has to ace to become accredited and is considered the best choice as it has no formal prerequisites & allows specialists to validate their proficiency in utilizing Azure Machine Learning Service and many other related solutions.
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Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 2: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Topic 3: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Topic 4: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Topic 5: Design and prepare a machine learning solution | - Manage compute and data assets - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads |
