Designing and Implementing a Data Science Solution on Azure Study Guide

How the Exam Is Structured

Designing and Implementing a Data Science Solution on Azure (DP-100) validates design and prepare a machine learning solution, explore data and run experiments, train and evaluate models, deploy and operationalize machine learning solutions. The ExamPal practice bank includes 175 premium questions and 40 free questions mapped across the official blueprint.

25% of exam

Domain 2: Explore data and run experiments

Covers data ingestion, preparation, splitting, training, tuning, and experiment tracking. This domain focuses on the practical workflow of preparing data, running models, and comparing results in Azure Machine Learning.

20% of exam

Domain 4: Deploy and operationalize machine learning solutions

Covers preparing models for deployment, serving real-time and batch inference, managing inference environments, and integrating deployed models with applications. This domain emphasizes operational readiness, endpoint configuration, and deployment lifecycle management.

Key Terms to Know

These terms are loaded from the shared terminology pack and appear across the question explanations.

Azure ML Python SDK v2

The version 2 Python software development kit used to interact programmatically with Azure Machine Learning resources.

Azure ML component

A reusable, versionable unit of work in Azure ML pipelines that encapsulates code, environment, inputs, and outputs.

Azure ML workspace

The central Azure Machine Learning resource that stores assets, runs, compute targets, and configuration for ML projects.

Azure Machine Learning Designer

A visual interface in Azure ML used to build, configure, and run machine learning pipelines without extensive coding.

Conda configuration file

A YAML file that specifies Conda packages and dependencies required for an ML environment.

Environment

An Azure ML SDK v2 class used to define the software environment, including dependencies, for training or inference.

Import Data

A Designer component used to bring external data, such as a CSV file from a website, into a pipeline.

MLOps

A set of practices for automating, managing, deploying, monitoring, and retraining machine learning systems.

MLTable

An Azure Machine Learning data asset format used to define tabular or file-based datasets for ML workflows.

MLflow

An open-source platform for tracking experiments, logging metrics and artifacts, and managing ML lifecycle tasks.

ParallelRunStep

An Azure ML pipeline step used for scalable parallel batch inference over large datasets.

YAML

A human-readable configuration format commonly used to define Azure ML components and pipeline settings.

artifact

A file or folder produced or used by an ML run, such as images, models, or output datasets.

autoscaling

The ability of a compute resource to automatically increase or decrease the number of nodes based on workload demand.

binary classification

A supervised learning task in which a model predicts one of two possible classes.

compute cluster

An Azure ML compute target made up of multiple nodes that can run training or batch workloads.

conda_file

A parameter used when creating an Azure ML environment from a Conda YAML specification.

config.json

A workspace configuration file that stores connection details needed for SDK code to connect to Azure Machine Learning.