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Last Updated: Aug 09, 2026
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What is the duration of the DP-100 Exam
- Passing Score: 700/1000
- Format: Multiple choices, multiple answers
- Number of Questions: 45-60
- Length of Examination: 150 minutes
Ways to Getting DP-100 Exam-Ready
The kind of extensive subject matter for the Microsoft DP-100 can be only obtained once the aspirant has access to quality study resources that are in general endorsed by the vendor itself. Overall, Microsoft offers both free and paid training courses to gain demanded expertise for DP-100. When it comes to the gratis learning path, it contains the following self-paced classes:
- Create Machine Learning Model - This is an intermediate-level free training course with 5 modules that details every single fact you should know about ML and its functions, features, and tools. To enroll for this option, it’s advisable that you have some related experience with the Python programming language.
- Create No-Code Predictive Models with Azure Machine Learning - In all, it’s beginner training that is composed of 4 concise modules that accentuate such technologies as data science, ML, Azure, and AI engineering. To be entitled to access this learning path and entirely apprehend all the details covered here, you are expected to have some skills in navigating the Azure portal.
- Build AI Solutions with Machine Learning - This is the mid-tier class offered for those who want to get themselves familiarized with Azure ML Python SDK that is used for creating organizational-ready AI solutions. Consisting of 14 full-length parts, the training requires you to possess some experience dealing with Python, PyTorch, ML, Scikit-Learn, and Tensorflow.
As far as you can see, all these are the best options to consider when free learning resources for the Microsoft DP-100 exam are concerned. Still, there is one paid instructor-led training course as well. It covers the concepts related to Designing & Implementing a Data Science Solution on Azure and is known as Course DP-100T01-A, which is a 3-day long class explaining every bit of the exam domains. In particular, the course imparts in applicants the intermediate knowledge of ML solutions, Python to monitor data preparation & ingestion, and ML frameworks. During the whole training, you’ll get exposed to as many as 10 comprehensive modules that spin around the test objectives. What makes these lessons significant is that they contain labs in the curriculum unlike free self-paced learning, which allows you to develop practical skills that are generally necessary for job-oriented tasks. So, visit the vendor’s website, check whether you comply with the course prerequisites and if you do, enroll in it, and be entirely geared up for the Microsoft DP-100 exam.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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 |
Step 4: Training with Practice Tests
Practicing more and more before taking the exam can help you score better. Using practice tests, you can also significantly improve your time management skills. You can easily find the relevant practice questions on different online learning platforms.
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