DP-100 - Microsoft Certified: Azure Data Scientist Associate Study Guide 2026

Key Competencies (3)

• 1

  Create and Manage Resources in an Azure Machine Learning Workspace

  Setting up and managing resources necessary for ML tasks within Azure.

• 2

  Design a Machine Learning Solution

  Creating a blueprint for implementing machine learning solutions within a project.

• 3

  Create and Manage Assets in an Azure Machine Learning Workspace

  Handling data, environments, and assets for ML operations in Azure.

Key Competencies (2)

• 1

  Run Experiments

  Executing and managing trials to train effective machine learning models.

• 2

  Explore Data

  Analyzing and processing data to prepare for ML model development.

Key Competencies (2)

• 1

  Deploy Models

  Facilitating the transition of ML models from development to production.

• 2

  Train Models

  Selecting algorithms and setting up processes for model training.

Key Competencies (4)

• 1

  Prepare for Model Optimization

  Setting up and evaluating language models suitable for optimization.

• 2

  Optimize through Fine-Tuning

  Refining model performance through targeted fine-tuning processes.

• 3

  Optimize through Retrieval Augmented Generation (RAG)

  Enhancing model outputs through RAG techniques.

• 4

  Optimize through Prompt Engineering and Prompt Flow

  Using prompt engineering approaches for refining language model outputs.