Study guide for Exam DP-100: Designing and Implementing a Data Science Solution on 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 this document should help you focus your studies as you prepare for the exam.
| Useful links | Description |
|---|---|
| Review the skills measured as of April 15, 2024 | 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 April 15, 2024 | Study this list of skills if you take your exam PRIOR to the date provided. |
| Change log | You can go directly to the change log if you want to see the changes that will be made on the date provided. |
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Updates to the exam
Our exams are updated periodically to reflect skills that are required to perform a role. We have included 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 other languages, and those are updated approximately eight weeks after the English version is updated. While Microsoft makes every effort to update localized versions as noted, there may be times when the localized versions of an exam are not updated on this schedule. Other available languages are listed in the Schedule Exam section of the Exam Details webpage. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.
Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.
Skills measured as of April 15, 2024
Audience profile
As a candidate for this exam, you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure.
Your responsibilities for this role include:
Designing and creating a suitable working environment for data science workloads.
Exploring data.
Training machine learning models.
Implementing pipelines.
Running jobs to prepare for production.
Managing, deploying, and monitoring scalable machine learning solutions.
As a candidate for this exam, you should have knowledge and experience in data science by using:
Azure Machine Learning
MLflow
Skills at a glance
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
Create and manage registries
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
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
Track model training by using MLflow
Evaluate a model
Train a model by using Python SDK v2
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 metric
Define 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 principles
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 self-study 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 Docs |
| Get 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 |
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 April 15, 2024 | Skill area as of April 15, 2024 | Change |
|---|---|---|
| Audience profile | Minor | |
| Design and prepare a machine learning solution | Design and prepare a machine learning 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 in Azure Machine Learning | Manage compute for experiments in Azure Machine Learning | No change |
| Explore data and train models | Explore data and train models | No % change |
| Explore data by using data assets and data stores | Explore data by using data assets and data stores | No change |
| Create models by using the Azure Machine Learning designer | Create models by using the Azure Machine Learning designer | No change |
| Use automated machine learning to explore optimal models | Use automated machine learning to explore optimal models | No change |
| Use notebooks for custom model training | Use notebooks for custom model training | 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 | Minor |
| Deploy and retrain a model | Deploy and retrain a model | No % change |
| Deploy a model | Deploy a model | No change |
| Apply machine learning operations (MLOps) practices | Apply machine learning operations (MLOps) practices | No change |
Skills measured prior to April 15, 2024
Audience profile
As a candidate for this exam, you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure.
Your responsibilities for this role include:
Designing and creating a suitable working environment for data science workloads.
Exploring data.
Training machine learning models.
Implementing pipelines.
Running jobs to prepare for production.
Managing, deploying, and monitoring scalable machine learning solutions.
As a candidate for this exam, you should have knowledge and experience in data science by using:
Azure Machine Learning
MLflow
Skills at a glance
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
Create and manage registries
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
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
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 metric
Define 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 guidelines
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