男生护肤品推荐痘痘肌肤干皮 Study guide for Exam DP

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.

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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 ailability (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 he subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. Additionally, you should he 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 he 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

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 DatabricksAzure Machine LearningAzure Synapse AnalyticsMLflow and Azure Machine Learning Ask a question Microsoft Q&A | Microsoft Docs Get community support AI - Machine Learning - Microsoft Tech CommunityAI - 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