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

DP-100 Designing and Implementing a Data Science Solution on Azure Exam Certification study guide

DP-100 Preparation Details

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.

Exam Voucher for DP-100 with 1 Retake

Get 40% OFF with the combo

DP-100 Azure Data Science Online Course

Pluralsight Microsoft Azure Data Scientist Course
LinkedIn Learning (Free trial) Azure Machine Learning Development
Udemy A-Z Machine Learning with Azure ML

DP-100 Azure Data Science Practice Test & Lab

Whizlabs Exam Questions Microsoft Azure Exam Certification Prep
Udemy Practice Tests 170+ Azure Data Practice Exam Questions
Amazon e-book (PDF) Data Science with Azure Machine Learning

Other Data Science Learning Programs

Coursera Data Scientist Associate test prep
Udacity (Nanodegree) Become an Azure Machine Learning Engineer
DatacampData Science for all [Interactive course]

Not Sure Which Exam Is Right for You?

Confused between AI-102 and DP-100? You are not alone. Read the AI-102 vs DP-100 blog post and choose the one that’s right for you!

DP-100 Sample Practice Exam Questions

DP-100 Design & Implement a Data Science Solution on Azure Exam certification Practice Test

Looking for DP-100 Dumps? Read This!

Using DP-100 exam dumps can get you permanently banned from taking any future Microsoft certificate exam. Read the FAQ page for more information. However, I strongly suggest you validate your understanding with practice questions.

Check out all the other Azure certificate study guides

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

Manage Azure Resources for Machine Learning (25-30%)

Create an Azure Machine Learning Workspace

Create an Azure Machine Learning workspace

Create & manage Azure Machine Learning workspaces

Configure workspace settings

Azure security workspace setting

Manage a workspace by using Azure Machine Learning Studio

Manage an Azure Machine Learning Studio workspace

Manage Data in an Azure Machine Learning Workspace

Select Azure storage resources

Connect to storage services on Azure

Register and maintain datastores

Create & register datastores

Create and manage datasets

Create datasets

Manage Compute for Experiments in Azure Machine Learning

Determine appropriate compute specifications for a training workload

Configure & submit training runs

Create compute targets for experiments and training

What are compute targets in Azure Machine Learning?

Set up compute targets for model training & deployment

Configure Attached Compute resources including Azure Databricks

Attach Azure Databricks compute resources to AML workspace

Monitor compute utilization

Monitor Azure Machine Learning

Master Azure Machine Learning DP-100

Amazon link (affiliate)

Implement Security and Access Control in Azure Machine Learning

Determine access requirements and map requirements to built-in roles

Default roles in the workspace

Manage workspace access

Create custom roles

Create custom role

Manage role membership

Manage roles in your workspace

Manage credentials by using Azure Key Vault

Use authentication credentials in AML

Set up an Azure Machine Learning Development Environment

Create compute instances

What is an AML compute instance?

Create & manage an AML compute instance

Share compute instances

Share compute instance with other users

Access Azure Machine Learning workspaces from other development environments

Organize and set up Azure Machine Learning environments

Use software environments in AML

Set up an Azure Databricks Workspace

Create an Azure Databricks workspace

Quickstart – Create an Azure Databricks workspace

Create an Azure Databricks cluster

Create a cluster in Azure Databricks

Create and run notebooks in Azure Databricks

Manage notebooks in Azure Databricks

Run a Databricks Notebook

Link an Azure Databricks workspace to an Azure Machine Learning workspace

Develop with AML & Azure Databricks

Run Experiments and Train Models (20-25%)

Create Models by Using the Azure Machine Learning Designer

Create a training pipeline by using Azure Machine Learning designer

Retrain models with Azure Machine Learning designer

Ingest data in a designer pipeline

Data ingestion options for Azure Machine Learning workflows

Use designer modules to define a pipeline data flow

Predict automobile price with the designer

Use custom code modules in designer

Define custom R modules for Machine Learning Studio (classic)

Run Model Training Scripts

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

Azure Machine Learning in Jupyter Notebooks

Configure run settings for a script

Configure and submit training runs

Script Run Config class

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

Create and register datastores

Run a training script on Azure Databricks compute

Develop with AutoML & Azure Databricks

Run code to train a model in an Azure Databricks notebook

Example of building ML models on Azure Databricks

Azure certification Frequently Asked Questions

Generate Metrics from an Experiment Run

Log metrics from an experiment run

Enable logging in Azure ML training runs

Retrieve and view experiment outputs

Evaluate machine learning experiment results

Set input & output directories

Use logs to troubleshoot experiment run errors

Collect ML pipeline log files in Application Insights for alerts & debugging

Use MLflow to track experiments

MLflow tracking for Azure Databricks ML experiments

MLflow tracking for ML experiments

Track experiments running in Azure Databricks

Track Databricks experiments in Azure Machine Learning

Use Automated Machine Learning to Create Optimal Models

Use the Automated ML interface in Azure Machine Learning studio

Forecast demand with automated machine learning

Use Automated ML from the Azure Machine Learning SDK

Configure automated ML experiments in Python

Select pre-processing options

Preprocessing Data in Azure Machine Learning Studio

What is automated machine learning (AutoML)?

Select the algorithms to be searched

How to select algorithms for Azure Machine Learning

Define a primary metric

The primary metric

Get data for an Automated ML run

Explore the results

Retrieve the best model

Retrieve the best model

DP-100 Exam details and tips

Tune Hyperparameters with Azure Machine Learning

Select a sampling method

Sampling the hyperparameter space

Define the search space

Tune hyperparameters for your model with Azure ML

Define the primary metric

Primary metric in Azure Machine Learning

Specify primary metric

Choosing a primary metric to handle imbalanced data

Define early termination options

Specify early termination policy

Find the model that has optimal hyperparameter values

Find the best model

Deploy and Operationalize Machine Learning Solutions (35-40%)

Select Compute for Model Deployment

Consider security for deployed services

Use TLS to secure a web service through Azure ML

Evaluate compute options for the deployment

Deploy models with Azure Machine Learning

Deploy a Model as a Service

Configure deployment settings

Deploy models with Azure Machine Learning

Deploy a registered model

Deploy machine learning models

Deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint

Deploy models from Azure Databricks onto Azure ML

Consume a deployed service

Consume an Azure ML model deployed as a web service

Troubleshoot deployment container issues

Troubleshoot docker deployment of models with AKS & ACI

Manage Models in Azure Machine Learning

Register a trained model

Register your model (optional)

Monitor model usage

Monitor Azure Machine Learning

Monitoring data from Azure Machine Learning

Monitor data drift

Detect data drift on datasets

Monitor data drift with Azure Machine Learning

Create an Azure Machine Learning Pipeline for Batch Inferencing

Configure a ParallelRunStep

ParallelRunStep class

Configure compute for a batch inferencing pipeline

Create & attach the remote compute target

Publish a batch inferencing pipeline

Run batch predictions using Azure ML designer

Run a batch inferencing pipeline and obtain outputs

Build an Azure ML pipeline for batch scoring

Obtain outputs from a ParallelRunStep

ParallelRunStep class

Publish an Azure Machine Learning Designer Pipeline as a Web Service

Create a target compute resource

Deploy models with Azure Machine Learning

Configure an Inference pipeline

Deploy a machine learning model with the designer

Consume a deployed endpoint

Deploy models with Azure Machine Learning

Implement Pipelines By Using the Azure Machine Learning SDK

Create a pipeline

Use automated machine learning to predict taxi fares

Pass data between steps in a pipeline

Pass data between pipeline steps

Run a pipeline

Running automated machine learning experiments

Monitor pipeline runs

Monitoring Azure Machine Learning

Apply ML Ops Practices

Trigger an Azure Machine Learning pipeline from Azure DevOps

Trigger Azure Machine Learning pipelines

AML pipeline using CI/CD with Azure DevOps

Enable CI/CD for ML project with Azure Pipelines

Automate model retraining based on new data additions or data changes

Retrain your model on new data

Retraining & updating AML models

Refactor notebooks into scripts

Convert notebook code into Python scripts

Implement source control for scripts

Git integration for Azure Machine Learning

Using Azure Machine Learning from GitHub Actions

Implement Responsible Machine Learning (5-10%)

Use Model Explainers to Interpret Models

Select a model interpreter

Model interpretability in Azure Machine Learning

Explainability in automated ML

Interpret & explain ML models in Python

Generate feature importance data

Generate feature importance values

Permutation Feature Importance

Describe Fairness Considerations for Models

Evaluate model fairness based on prediction disparity

Machine learning fairness

Assess ML models’ fairness in Python

Mitigate model unfairness

Detect & mitigate unfairness with AML models

Describe Privacy Considerations for Data

Describe principles of differential privacy

Differential privacy in machine learning

Specify acceptable levels of noise in data and the effects on privacy

Use differential privacy in Azure ML

This brings us to the end of the DP-100 Designing and Implementing a Data Science Solution on Azure 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 preparing for other Azure certification exams, check out the Azure study guide for those exams.

Follow Me Receive Updates on DP-100 Exam

Want to be notified as soon as I post? Subscribe to the RSS feed / leave your email address in the subscribe section. Share the article to your social networks with the below links so it can benefit others.

Share the DP-100 Study Guide in Your Network

You may also like