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%)

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

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 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

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 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

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

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

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

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

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

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

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

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

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

Prepare data for fine-tuning

Select an appropriate base model

Run a fine-tuning job

Evaluate your fine-tuned model