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Purpose of this document
This study guide provides a summary of the topics covered in the assessment lab, along with additional resources to help you prepare. Note that the learning path on the credential details page may include more modules than the "Tasks performed" to provide a cohesive learning experience.
Tasks at a glance
Prepare the AI hub
Create an Azure AI hub
Create a new Azure AI project
Add project users and assign roles
Supporting module (s):
Configure connected resources
Configure an Azure AI services or Azure OpenAI Service connection
Configure an Azure AI Search connection
Configure a storage connection
Supporting module (s):
Deploy and test a model
Select an appropriate model and version from the Azure AI model catalog and collections
Configure deployment settings
Deploy a model
Edit a deployed model
Test a deployed model
Supporting module (s):
Create a copilot by using prompt flow
Create a chat flow
Configure a language model node, including parameters and prompts
Configure a Python node
Configure a prompt node
Define inputs and outputs
Run and test the chat flow
Supporting module (s):
Implement a Retrieval Augmented Generation (RAG) pattern
Create a data component
Create an index
Integrate the data component and the index into the flow
Test the index
Run the chat application
Supporting module (s):
Configure responsible AI
Create a custom content filter
Create an input filter that includes a blocklist
Create an output filter with protected material
Apply a custom content filter to an existing deployment
Supporting module (s):
Assess and compare copilot performance
Assess a copilot by using built-in performance and quality metrics
Assess a copilot by using built-in risk and safety metrics
Assess a copilot by using manual evaluations
Supporting module (s):