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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