Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Study guide for Exam DP-100: Designingand Implementing a Data Science Solutionon Azure
Purpose of this document
This study guide should help you understand what to expect on the exam and includes a summary of
the topics the exam might cover and links to additional resources. The information and materials in thisdocument should help you focus your studies as you prepare for the exam.

Useful links

Description

Review the skills measured
as of March 14, 2023

This list represents the skills measured AFTER the date provided.
Study this list if you plan to take the exam AFTER that date.

Review the skills measured
prior to March 14, 2023

Study this list of skills if you take your exam PRIOR to the dateprovided.

Change log

You can go directly to the change log if you want to see thechanges that will be made on the date provided.

How to earn thecertification

Some certifications only require passing one exam, while others
require passing multiple exams.

Certification renewal

Microsoft associate, expert, and specialty certifications expire
annually. You can renew by passing a free online assessment onMicrosoft Learn.

Your Microsoft Learn
profileConnecting your certification profile to Learn allows you to scheduleand renew exams and share and print certificates.

Passing score

A score of 700 or greater is required to pass.

Exam sandbox

You can explore the exam environment by visiting our exam
sandbox.

1

Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Useful links

Description

Request accommodations

If you use assistive devices, require extra time, or need modification
to any part of the exam experience, you can request anaccommodation.

Take a practice test

Are you ready to take the exam or do you need to study a bit more?

Updates to the exam
Our exams are updated periodically to reflect skills that are required to perform a role. We haveincluded two versions of the Skills Measured objectives depending on when you are taking the exam.
We always update the English language version of the exam first. Some exams are localized into otherlanguages, and those are updated approximately eight weeks after the English version is updated.
Other available languages are listed in the Schedule Exam section of the Exam Details webpage. If theexam isn't available in your preferred language, you can request an additional 30 minutes to complete
the exam.

NoteThe bullets that follow each of the skills measured are intended to illustrate how we are assessing thatskill. Related topics may be covered in the exam.

Note
Most questions cover features that are general availability (GA). The exam may contain questions onPreview features if those features are commonly used.

Skills measured as of March 14, 2023
Audience profile
Candidates for the Azure Data Scientist Associate certification should have subject matter expertise inapplying data science and machine learning to implement and run machine learning workloads onAzure.
Responsibilities for this role include designing and creating a suitable working environment for data
science workloads; exploring data; training machine learning models; implementing pipelines; runningjobs to prepare for production; and managing, deploying, and monitoring scalable machine learning
solutions.
A candidate for this certification should have knowledge and experience in data science by using Azure
Machine Learning and MLflow.
••

Design and prepare a machine learning solution (20–25%)
Explore data and train models (35–40%)

2

Exam DP-100: Designing and Implementing a Data Science Solution on Azure•

Prepare a model for deployment (20–25%)•Deploy and retrain a model (10–15%)

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

Determine the appropriate compute specifications for a training workload•

Describe model deployment requirements
Select which development approach to use to build or train a model•Manage an Azure Machine Learning workspace•

Create an Azure Machine Learning workspace•

Manage a workspace by using developer tools for workspace interaction
Set up Git integration for source control•

Manage data in an Azure Machine Learning workspace•

Select Azure Storage resources
Register and maintain datastores•

Create and manage data assets•

Manage compute for experiments in Azure Machine Learning•

Create compute targets for experiments and training•

Select an environment for a machine learning use case•Configure attached compute resources, including Apache Spark pools•

Monitor compute utilization

Explore data and train models (35–40%)
Explore data by using data assets and data stores•

Access and wrangle data during interactive development•

Wrangle interactive data with Apache Spark

Create models by using the Azure Machine Learning designer•

Create a training pipeline•

Consume data assets from the designer•

Use custom code components in designer•

Evaluate the model, including responsible AI guidelines

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 (NLP)•Select and understand training options, including preprocessing and algorithms•3Exam DP-100: Designing and Implementing a Data Science Solution on Azure•

Evaluate an automated machine learning run, including responsible AI guidelines

Use notebooks for custom model training•

Develop code by using a compute instance•

Track model training by using MLflow•

Evaluate a model•

Train a model by using Python SDK
Use the terminal to configure a compute instance•

Tune hyperparameters with Azure Machine Learning•

Select a sampling method•

Define the search space•

Define the primary metricDefine early termination options•Prepare a model for deployment (20–25%)
Run model training scripts•

Configure job run settings for a script
Configure compute for a job run•

Consume data from a data asset in a job•

Run a script as a job by using Azure Machine Learning•

Use MLflow to log metrics from a job run••

Use logs to troubleshoot job run errors
Configure an environment for a job run•

Define parameters for a job•

Implement training pipelines•

Create a pipeline•

Pass data between steps in a pipeline
Run and schedule a pipeline••

Monitor pipeline runs
Create custom components•

Use component-based pipelines•

Manage models in Azure Machine Learning•

Describe MLflow model output•

Identify an appropriate framework to package a model•

Assess a model by using responsible AI guidelines4

Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Deploy and retrain a model (10–15%)
Deploy a model•

Configure settings for online deployment
Configure compute for a batch deployment•

Deploy a model to an online endpoint•

Deploy a model to a batch endpoint•

Test an online deployed service
Invoke the batch endpoint to start a batch scoring job••

Apply machine learning operations (MLOps) practices•

Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub•

Automate model retraining based on new data additions or data changes•

Define event-based retraining triggers

Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer selfstudy options and classroom training as well as links to documentation, community sites, and videos.

Study resources

Links to learning and documentation

Get trained

Choose from self-paced learning paths and modules or take an
instructor led course

Find documentation

Azure Databricks
Azure Machine Learning
Azure Synapse Analytics
MLflow and Azure Machine Learning

Ask a question

Microsoft Q&A | Microsoft DocsGet community support

AI - Machine Learning - Microsoft Tech Community
AI - Machine Learning Blog - Microsoft Tech Community

Follow Microsoft Learn

Microsoft Learn - Microsoft Tech Community

Find a video

Microsoft Learn Shows

5

Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Change log
Key to understanding the table: The topic groups (also known as functional groups) are in bold typeface
followed by the objectives within each group. The table is a comparison between the two versions of
the exam skills measured and the third column describes the extent of the changes.

Skill area prior to March 14, 2023

Skill area as of March 14, 2023

ChangeDesign and prepare a machinelearning solution

Design and prepare a machinelearning solution

No % change

Design a machine learning solution

Design a machine learning solution

No change

Manage an Azure Machine Learning
workspace

Manage an Azure Machine Learning
workspace

No change

Manage data in an Azure Machine
Learning workspace

Manage data in an Azure Machine
Learning workspace

No change

Manage compute for experiments inAzure Machine Learning

Manage compute for experiments inAzure Machine Learning

Minor

Explore data, and train models

Explore data and train models

No % change

Explore data by using data assets anddata stores

Explore data by using data assets anddata stores

Major

Create models by using the Azure
Machine Learning designer

Create models by using the Azure
Machine Learning designer

Minor

Use automated machine learning to
explore optimal models

Use automated machine learning to
explore optimal models

No change

Use notebooks for custom modeltraining

Use notebooks for custom modeltraining

Minor

Tune hyperparameters with Azure
Machine Learning

Tune hyperparameters with Azure
Machine Learning

No change

Prepare a model for deployment

Prepare a model for deployment

No % change

Run model training scripts

Run model training scripts

No change

Implement training pipelines

Implement training pipelines

No change

Manage models in Azure Machine
Learning

Manage models in Azure Machine
Learning

No change

Deploy and retrain a model

Deploy and retrain a model

No % change

6Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Skill area prior to March 14, 2023

Skill area as of March 14, 2023

ChangeDeploy a model

Deploy a model

Minor

Apply machine learning operations(MLOps) practices

Apply machine learning operations(MLOps) practices

MinorSkills measured prior to March 14, 2023
Audience profile
Candidates for the Azure Data Scientist Associate certification should have subject matter expertise inapplying data science and machine learning to implement and run machine learning workloads onAzure.
Responsibilities for this role include designing and creating a suitable working environment for datascience workloads; exploring data; training machine learning models; implementing pipelines; runningjobs to prepare for production; and managing, deploying, and monitoring scalable machine learningsolutions.
A candidate for this certification should have knowledge and experience in data science by using Azure
Machine Learning and MLflow.
•Design and prepare a machine learning solution (20–25%)•Explore data, and train models (35–40%)•Prepare a model for deployment (20–25%)•Deploy and retrain a model (10–15%)

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

Determine the appropriate compute specifications for a training workload
Describe model deployment requirements•

Select which development approach to use to build or train a model•Manage an Azure Machine Learning workspace•

Create an Azure Machine Learning workspace•

Manage a workspace by using developer tools for workspace interaction•

Set up Git integration for source control

Manage data in an Azure Machine Learning workspace•

Select Azure Storage resources•

Register and maintain datastores•

Create and manage data assets

7

Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Manage compute for experiments in Azure Machine Learning•

Create compute targets for experiments and training••

Select an environment for a machine learning use caseConfigure attached compute resources, including Azure Databricks and Azure Synapse Analytics•

Monitor compute utilization

Explore data, and train models (35–40%)
Explore data by using data assets and data stores•

Load and transform data•

Analyze data by using Azure Data Explorer•

Use differential privacy

Create models by using the Azure Machine Learning designer••

Create a training pipeline
Consume data assets from the designer•

Use designer components to define a pipeline data flow
Use custom code components in designer•

Evaluate the model, including responsible AI guidelines•

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 (NLP)•

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•

Develop code by using a compute instance•

Consume data in a notebook••

Track model training by using MLflow
Evaluate a model•

Train a model by using Python SDKv2•

Use the terminal to configure a compute instance

Tune hyperparameters with Azure Machine Learning•

Select a sampling method•

Define the search space•

Define the primary metricDefine early termination options•

8

Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Prepare a model for deployment (20–25%)
Run model training scripts•Configure job run settings for a script
Configure compute for a job run•

Consume data from a data asset in a job•

Run a script as a job by using Azure Machine Learning••

Use MLflow to log metrics from a job run
Use logs to troubleshoot job run errors•

Configure an environment for a job run•

Define parameters for a job•

Implement training pipelines•

Create a pipeline
Pass data between steps in a pipeline•

Run and schedule a pipeline•

Monitor pipeline runs•

Create custom components•

Use component-based pipelines•

Manage models in Azure Machine Learning•

Describe MLflow model output•

Identify an appropriate framework to package a model•

Assess a model by using responsible AI guidelines

Deploy and retrain a model (10–15%)
Deploy a model•

Configure settings for real-time deployment•

Configure compute for a batch deployment•

Deploy a model to a real-time endpoint•

Deploy a model to a batch endpoint•

Test a real-time deployed service•

Invoke the batch endpoint to start a batch scoring job

Apply machine learning operations (MLOps) practices•

Trigger an Azure Machine Learning pipeline, including from Azure DevOps or GitHub•Automate model retraining based on new data additions or data changes•Define event-based retraining triggers

9