Microsoft DP-100 : Designing and Implementing a Data Science Solution on Azure

Exam Code: DP-100
Exam Name: Designing and Implementing a Data Science Solution on Azure
Updated: Aug 13, 2026
Q & A: 528 Questions and Answers

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2. Train Models & Run Experiments (25-30%):

Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.
Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.
Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.
Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.

Microsoft DP-100 Exam Syllabus Topics:

Topic	Details
Topic 1	Design and prepare a machine learning solution: This topic equips Microsoft Data Scientists with skills to design comprehensive machine learning solutions tailored to business needs. It delves into managing resources and assets in the Azure Machine Learning workspace, offering insights into organizing computational and storage resources effectively.
Topic 2	Train and deploy models: In this topic, Microsoft Data Scientists develop the expertise to execute training scripts and implement efficient training pipelines. Participants learn to manage models effectively and deploy them using Azure services. The focus is on assessing proficiency in training processes, deployment strategies, and lifecycle management of machine learning models to ensure production-ready solutions that align with organizational goals.
Topic 3	Optimize language models for AI applications: This advanced topic teaches Microsoft Data Scientists to enhance AI applications by optimizing language models. Skills are developed in preparing models for optimization, prompt engineering, and employing techniques like Prompt Flow and Retrieval Augmented Generation (RAG). Additionally, fine-tuning strategies are explored to maximize model performance.
Topic 4	Explore data, and run experiments: This section focuses on enabling Microsoft Data Scientists to explore datasets and experiment with model configurations effectively. By leveraging automated machine learning, participants explore optimal models and streamline custom model training with notebooks. It evaluates skills in automating hyperparameter tuning to refine models for improved performance.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

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