DP-100 Exam Study Guide (Microsoft Azure Data Science Solution)

Preparing for DP-100 Designing and Implementing a Data Science Solution on the Azure Certificate exam? Don’t know where to start? This post is the DP-100 Certificate Study Guide (with links to each exam objective).

I have curated a list of articles from Microsoft documentation for each objective of the DP-100 exam. I hope this article will help you to prepare for the DP-100 Certification exam. Also, please share the post within your circles so it helps them to prepare for the exam.

DP-100 Azure Data Science Course (Online)

Pluralsight (Learning Path)	Microsoft Azure Data Scientist Course [Free trial]
LinkedIn Learning [Free Trial]	Azure Machine Learning Development
Udemy	A-Z Machine Learning using Azure Machine Learning

DP-100 Azure Data Science Practice Test & Lab

Udemy Practice Test	70+ Practice Exam Questions
Guided Projects on AML	Learn by doing: Azure Machine Learning Studio
Amazon e-book (pdf)	Cloud Data Science with Azure Machine Learning

Other Data Science Learning Programs

Coursera [Specialization]	Data Science by John Hopkins University
Udacity	Data Scientist Nanodegree Program

To view other Azure Certificate Study Guides, click here

Full Disclosure: Some of the links in this post are affiliate links. I receive a commission when you purchase through them.

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Set up an Azure Machine Learning workspace (30-35%)

Create an Azure Machine Learning workspace

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace

Configure workspace settings

https://docs.microsoft.com/en-us/cli/azure/security/workspace-setting?view=azure-cli-latest

Manage a workspace by using Azure Machine Learning Studio

https://docs.microsoft.com/en-us/azure/machine-learning/studio/manage-workspace

Manage data objects in an Azure Machine Learning workspace

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data#create-and-register-datastores

Create and manage datasets

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets#create-datasets

Manage experiment compute contexts

Create a compute instance

https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-instance

Determine appropriate compute specifications for a training workload

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets#compute-targets-for-training

Create compute targets for experiments and training

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets

Run experiments and train models (25-30%)

Create models by using Azure Machine Learning Designer

Create a training pipeline by using Azure Machine Learning designer

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-retrain-designer

Ingest data in a designer pipeline

https://docs.microsoft.com/en-us/azure/machine-learning/concept-data-ingestion

Use designer modules to define a pipeline data flow

https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score

Use custom code modules in designer

https://docs.microsoft.com/en-us/azure/machine-learning/studio/custom-r-modules

Run training scripts in an Azure Machine Learning workspace

Create and run an experiment by using the Azure Machine Learning SDK

https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-sdk-setup

Consume data from a data store in an experiment by using the Azure Machine Learning SDK

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data#create-and-register-datastores

Consume data from a dataset in an experiment by using the Azure Machine Learning SDK

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets#create-datasets

Choose an estimator for a training experiment

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models?WT.mc_id=blog-medium-abornst

Generate metrics from an experiment run

Log metrics from an experiment run

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments

Retrieve and view experiment outputs

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments#view-the-experiment-in-your-workspace-in-azure-machine-learning-studio

Use logs to troubleshoot experiment run errors

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines-application-insights

Amazon link (affiliate)

Automate the model training process

Create a pipeline by using the SDK

https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-auto-train-models

Pass data between steps in a pipeline

https://docs.microsoft.com/en-us/learn/modules/create-pipelines-in-aml/3-pipeline-data

Run a pipeline

https://docs.microsoft.com/en-us/learn/modules/automate-model-selection-with-azure-automl/4-automl-experiments

Monitor pipeline runs

https://docs.microsoft.com/en-us/azure/machine-learning/monitor-azure-machine-learning

Optimize and manage models (20-25%)

Use Automated ML to create optimal models

Use the Automated ML interface in Azure Machine Learning studio

https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-automated-ml-forecast

Use Automated ML from the Azure Machine Learning SDK

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train

Select scaling functions and pre-processing options

https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml#preprocessing

Determine algorithms to be searched

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-define-task-type

Define a primary metric

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train#primary-metric

Get data for an Automated ML run

https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-auto-train-models#explore-the-results

Retrieve the best model

https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-auto-train-models#retrieve-the-best-model

Use Hyperdrive to tune hyperparameters

Select a sampling method

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters#sampling-the-hyperparameter-space

Define the search space

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters#define-search-space

Define the primary metric

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters#specify-primary-metric

Define early termination options

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters#specify-early-termination-policy

Find the model that has optimal hyperparameter values

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters#find-the-best-model

Use model explainers to interpret models

Select a model interpreter

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability#interpretability-with-azure-machine-learning

Generate feature importance data

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml#generate-feature-importance-value-on-your-personal-machine

Manage models

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where#registermodel

Monitor model history

https://docs.microsoft.com/en-us/azure/machine-learning/monitor-azure-machine-learning#monitoring-data-from-azure-machine-learning

Monitor data drift

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets

Data Drift Monitoring for Azure ML Datasets

Amazon link (affiliate)

Deploy and consume models (20-25%)

Create production compute targets

Consider security for deployed services

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-secure-web-service

Evaluate compute options for deployment

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where#deploy-to-target

Deploy a model as a service

Configure deployment settings

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where

Consume a deployed service

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service

Troubleshoot deployment container issues

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

Create a pipeline for batch Inferencing

Publish a batch inferencing pipeline

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer

Run a batch inferencing pipeline and obtain outputs

https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-batch-scoring-classification

Publish a designer pipeline as a web service

Create a target compute resource

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where#deploy-to-target

Configure an Inference pipeline

https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

Consume a deployed endpoint

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where#consume-web-services

This brings us to the end of DP-100 Study Guide

What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!

In case you are looking for other Azure certification exams check out this page

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DP-100 Exam Study Guide (Implementing a Data Science Solution on Azure)