DP-100 Study Guide

Exam Quick Facts

Study Resources

Skills at a Glance

Who is this exam for?

This Microsoft Data certification covers data concepts and Azure data services. It tests your ability to work with relational and non-relational databases, analytics workloads, and data platforms on Azure. This is an associate-level exam that expects hands-on experience. You should have practical knowledge of the technologies covered.

This exam is retiring on 2026-06-01. The replacement exam is AI-300. If you’re planning to take this exam, schedule it before the retirement date.

Skills Measured

Design and prepare a machine learning solution (20–25%)

This domain covers the skills needed to work with the topics described below. Study each objective carefully and use the linked resources to deepen your understanding.

Design a machine learning solution

• Identify the structure and format for datasets
• Determine the compute specifications for machine learning workload
• Select the development approach to train a model

Create and manage resources in an Azure Machine Learning workspace

• Create and manage a workspace
• Create and manage datastores
• Create and manage compute targets
• Set up Git integration for source control

Create and manage assets in an Azure Machine Learning workspace

• Create and manage data assets
• Create and manage environments
• Share assets across workspaces by using registries

Explore data, and run experiments (20–25%)

This domain covers the skills needed to work with the topics described below. Study each objective carefully and use the linked resources to deepen your understanding.

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

• Use the terminal to configure a compute instance
• Access and wrangle data in notebooks
• Wrangle data interactively with attached Synapse Spark pools and serverless Spark compute
• Retrieve features from a feature store to train a model
• Track model training by using MLflow
• Evaluate a model, including responsible AI guidelines

Automate hyperparameter tuning

• Select a sampling method
• Define the search space
• Define the primary metric
• Define early termination options

Train and deploy models (25–30%)

This domain covers the skills needed to work with the topics described below. Study each objective carefully and use the linked resources to deepen your understanding.

Run model training scripts

• Consume data in a job
• Configure compute for a job run
• Configure an environment for a job run
• Track model training with MLflow in a job run
• Define parameters for a job
• Run a script as a job
• Use logs to troubleshoot job run errors

Implement training pipelines

• Create custom components
• Create a pipeline
• Pass data between steps in a pipeline
• Run and schedule a pipeline
• Monitor and troubleshoot pipeline runs

Manage models

• Define the signature in the MLmodel file
• Package a feature retrieval specification with the model artifact
• Register an MLflow model
• Assess a model by using responsible AI principles

Deploy a model

• Configure settings for online deployment
• Deploy a model to an online endpoint
• Test an online deployed service
• Configure compute for a batch deployment
• Deploy a model to a batch endpoint
• Invoke the batch endpoint to start a batch scoring job

Optimize language models for AI applications (25–30%)

This domain covers the skills needed to work with the topics described below. Study each objective carefully and use the linked resources to deepen your understanding.

Prepare for model optimization

• Select and deploy a language model from the model catalog
• Compare language models using benchmarks
• Test a deployed language model in the playground
• Select an optimization approach

Optimize through prompt engineering and prompt flow

• Test prompts with manual evaluation
• Define and track prompt variants
• Create prompt templates
• Define chaining logic with the prompt flow SDK
• Use tracing to evaluate your flow

Optimize through Retrieval Augmented Generation (RAG)

• Prepare data for RAG, including cleaning, chunking, and embedding
• Configure a vector store
• Configure an Azure AI Search-based index store
• Evaluate your RAG solution

Optimize through fine-tuning

• Prepare data for fine-tuning
• Select an appropriate base model
• Run a fine-tuning job
• Evaluate your fine-tuned model

What to Study Next

Based on this exam, here are related certifications to consider:

• DP-900: Microsoft Azure Data Fundamentals — Fundamentals
• AI-900: Microsoft Azure AI Fundamentals — Fundamentals ⚠️ Retiring
• AI-300: Machine Learning Operations (MLOps) Engineer Associate — Associate 🧪 Beta

Quick Links

• Official Exam Page
• Microsoft Study Guide
• Practice Assessment