Microsoft Azure Data Scientist Associate (DP-100) Cert Prep

1h 43mIntermediate2025-06-18

Authors

Noah Gift

MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO

Course details

In this comprehensive course, MLOps expert Noah Gift helps you prepare for the Microsoft Azure Data Scientist Associate (DP-100) Certification exam. Learn how to design and prepare a machine learning solution as you create an Azure Machine Learning workspace. Explore data and run experiments through developing code with a compute instance, training a model with Python SDK, and much more. Step through the full process of training and deploying models. Plus, dive into techniques you can use to optimize language models for AI applications.

Skills covered

Cloud DevelopmentMachine LearningCloud AdministrationAzureCert PrepArtificial Intelligence (AI)Cloud ComputingMicrosoft

Concepts

0. Introduction

01 - Overview
02 - Prerequisite technology

Domain 1 - Design and Prepare a Machine Learning Solution

03 - Determine the appropriate compute specifications for a training workload
04 - Create an Azure Machine Learning workspace
05 - Manage a workspace by using developer tools for workspace interaction
06 - Create and manage data assets
07 - Create compute targets for experiments and training
08 - Monitor compute utilization

Domain 2 - Explore Data and Train Models

09 - Load and transform data
10 - Analyze data by using Azure Data Explorer
11 - Demo - Azure Data Explorer
12 - Consume data assets from the designer
13 - Use automated machine learning for tabular data
14 - Develop code by using a compute instance
15 - Consume data in a notebook
16 - Train a model by using Python SDK
17 - Techniques for dealing with hyperparameter optimization

Domain 3 - Prepare a Model for Deployment

18 - Configure compute for a job run
19 - Consume data from a data asset in a job
20 - Run a script as a job by using Azure Machine Learning
21 - Use MLflow to log metrics from a job run
22 - Describe MLflow model output
23 - Identify an appropriate framework to package a model
24 - Describe MLflow model workflow in Databricks

Domain 4 - Deploy and Retrain a Model

25 - Configure compute for a batch deployment
26 - Deploy a model to a batch endpoint
27 - Test a real-time deployed service
28 - Apply machine learning operations (MLOps) practices
29 - Trigger an Azure Machine Learning pipeline, including from Azure DevOps or GitHub
30 - Conclusion