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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 |
|---|---|
| How to earn the certification | 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 on Microsoft Learn. |
| Your Microsoft Learn profile | Connecting your certification profile to Microsoft Learn allows you to schedule and renew exams and share and print certificates. |
| Exam scoring and score reports | A score of 700 or greater is required to pass. |
| Exam sandbox | You can explore the exam environment by visiting our exam sandbox. |
| Request accommodations | If you use assistive devices, require extra time, or need modification to any part of the exam experience, you can request an accommodation. |
| Take a free Practice Assessment | Test your skills with practice questions to help you prepare for the exam. |
Updates to 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 11, 2025
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. Additionally, you should have knowledge of optimizing language models for AI applications using Azure AI.
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.
Using language models for building AI applications.
As a candidate for this exam, you should have knowledge and experience in data science by using:
Azure Machine Learning
MLflow
Azure AI services, including Azure AI Search
Azure AI Foundry
Skills at a glance
Design and prepare a machine learning solution (20–25%)
Explore data, and run experiments (20–25%)
Train and deploy models (25–30%)
Optimize language models for AI applications (25–30%)
Design and prepare a machine learning solution (20–25%)
Design a machine learning solution
Identify the structure and format for datasets
Determine the compute specifications for machine learning workload
Select the development approach to train a model
Create and manage resources in an Azure Machine Learning workspace
Create and manage a workspace
Create and manage datastores
Create and manage compute targets
Set up Git integration for source control
Create and manage assets in an Azure Machine Learning workspace
Create and manage data assets
Create and manage environments
Share assets across workspaces by using registries
Explore data, and run experiments (20–25%)
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
Use the terminal to configure a compute instance
Access and wrangle data in notebooks
Wrangle data interactively with attached Synapse Spark pools and serverless Spark compute
Retrieve features from a feature store to train a model
Track model training by using MLflow
Evaluate a model, including responsible AI guidelines
Automate hyperparameter tuning
Select a sampling method
Define the search space
Define the primary metric
Define early termination options
Train and deploy models (25–30%)
Run model training scripts
Consume data in a job
Configure compute for a job run
Configure an environment for a job run
Track model training with MLflow in a job run
Define parameters for a job
Run a script as a job
Use logs to troubleshoot job run errors
Implement training pipelines
Create custom components
Create a pipeline
Pass data between steps in a pipeline
Run and schedule a pipeline
Monitor and troubleshoot pipeline runs
Manage models
Define the signature in the MLmodel file
Package a feature retrieval specification with the model artifact
Register an MLflow model
Assess a model by using responsible AI principles
Deploy a model
Configure settings for online deployment
Deploy a model to an online endpoint
Test an online deployed service
Configure compute for a batch deployment
Deploy a model to a batch endpoint
Invoke the batch endpoint to start a batch scoring job
Optimize language models for AI applications (25–30%)
Prepare for model optimization
Select and deploy a language model from the model catalog
Compare language models using benchmarks
Test a deployed language model in the playground
Select an optimization approach
Optimize through prompt engineering and prompt flow
Test prompts with manual evaluation
Define and track prompt variants
Create prompt templates
Define chaining logic with the prompt flow SDK
Use tracing to evaluate your flow
Optimize through Retrieval Augmented Generation (RAG)
Prepare data for RAG, including cleaning, chunking, and embedding
Configure a vector store
Configure an Azure AI Search-based index store
Evaluate your RAG solution
Optimize through fine-tuning
Prepare data for fine-tuning
Select an appropriate base model
Run a fine-tuning job
Evaluate your fine-tuned model
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
The table below summarizes the changes between the current and previous version of the skills measured. The functional groups are in bold typeface followed by the objectives within each group. The table is a comparison between the previous and current version of the exam skills measured and the third column describes the extent of the changes.
| Skill area prior to January 16, 2025 | Skill area as of January 16, 2025 | Change |
|---|---|---|
| Audience profile | Minor | |
| Optimize language models for AI applications | Optimize language models for AI applications | No % change |
| Optimize through prompt engineering and Prompt flow | Optimize through prompt engineering and prompt flow | Minor |