AB-620, Agentic AI, AI, Azure AI, Generative AI, Microsoft Certification

Configure custom prompts to use the Foundry model catalog (AB-620 Exam Prep)

 
This post is a part of the AB-620: Designing and Building Integrated AI Agent Solutions in Copilot Studio Exam Prep Hub.
This topic falls under these sections:
Integrate and extend agents in Copilot Studio (40–45%)
   --> Integrate agents with Azure
      --> Configure custom prompts to use the Foundry model catalog

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 4 practice tests with 30 questions each available from the hub's main page below the exam topics section.

Overview

As organizations build increasingly sophisticated AI agents in Microsoft Copilot Studio, they often require more control over which large language models (LLMs) are used and how those models generate responses. While Copilot Studio includes powerful built-in generative AI capabilities, many enterprise scenarios benefit from connecting to models hosted in Azure AI Foundry (formerly Azure AI Studio).

Azure AI Foundry provides access to a large catalog of foundation models from Microsoft, OpenAI, Meta, Mistral AI, Cohere, Hugging Face, and many other providers. These models can be deployed within an Azure subscription and securely consumed by applications and AI agents.

One of the key capabilities covered in the AB-620 exam is configuring custom prompts in Copilot Studio that leverage models deployed through the Azure AI Foundry model catalog. This enables organizations to tailor agent behavior, use specialized models, implement reusable prompts, and satisfy governance requirements while maintaining enterprise security.

What is Azure AI Foundry?

Azure AI Foundry is Microsoft’s unified platform for building, evaluating, deploying, securing, and managing AI solutions.

It provides:

  • Model catalog
  • Prompt engineering tools
  • AI evaluation capabilities
  • Safety systems
  • Deployment management
  • Model monitoring
  • Responsible AI controls
  • Agent development tools
  • Integration with Copilot Studio

Rather than relying only on the default Copilot model, organizations can deploy one or more models within Azure AI Foundry and make them available to enterprise applications.

What is the Foundry Model Catalog?

The Model Catalog is a centralized repository containing hundreds of AI models.

Examples include:

  • GPT models
  • Phi models
  • Llama models
  • Mistral models
  • Cohere Command models
  • DeepSeek models (where available)
  • Open-source Hugging Face models
  • Vision models
  • Embedding models
  • Speech models
  • Multimodal models

Each model includes information such as:

  • Provider
  • Version
  • Licensing
  • Supported tasks
  • Context window
  • Token limits
  • Pricing
  • Deployment options
  • Performance benchmarks

Why Use the Model Catalog?

Organizations may choose custom models because they need:

  • Better reasoning
  • Lower latency
  • Lower cost
  • Longer context windows
  • Specialized coding abilities
  • Multilingual support
  • Vision processing
  • Image understanding
  • Document analysis
  • Industry-specific performance

Instead of using one model for every task, different prompts can target different deployed models.

What Are Custom Prompts?

A custom prompt is a reusable prompt template that defines how an LLM should perform a task.

Rather than asking the model a simple question, a custom prompt provides detailed instructions, context, formatting rules, and constraints.

Example:

Instead of:

Summarize this document.

A custom prompt might specify:

You are a financial analyst. Summarize this quarterly earnings report in less than 300 words. Highlight revenue changes, operating margin, risks, opportunities, and executive guidance. Produce the output as a Markdown table followed by three bullet points.

The additional instructions produce far more consistent outputs.

Benefits of Custom Prompts

Advantages include:

  • Consistent responses
  • Reusable instructions
  • Better formatting
  • Reduced hallucinations
  • Improved grounding
  • Easier maintenance
  • Centralized governance
  • Standardized business logic

How Copilot Studio Uses Foundry Models

The high-level workflow is:

  1. Deploy a model in Azure AI Foundry.
  2. Configure the deployment endpoint.
  3. Create or connect Azure AI resources.
  4. Connect Copilot Studio.
  5. Create a custom prompt.
  6. Select the deployed model.
  7. Pass user input into the prompt.
  8. Receive generated output.
  9. Continue the conversation.

The user typically does not know which model produced the response.

Typical Architecture

User
↓
Copilot Studio Agent
↓
Custom Prompt
↓
Azure AI Foundry
↓
Selected Model Deployment
↓
Generated Response
↓
Agent Response

Components Involved

A complete solution typically includes:

  • Copilot Studio
  • Azure AI Foundry
  • Azure AI Foundry Project
  • Model deployment
  • Azure AI Services resource
  • Authentication
  • Prompt template
  • Enterprise data
  • Optional Azure AI Search

Creating a Model Deployment

Before a prompt can use a model, the model must first be deployed.

Typical steps include:

  • Browse the Model Catalog.
  • Select a model.
  • Review licensing.
  • Choose deployment type.
  • Configure capacity.
  • Deploy the endpoint.
  • Test the deployment.
  • Secure the deployment.

The deployment creates an endpoint that applications can call.

Connecting Copilot Studio to Foundry

The connection typically involves:

  • Azure authentication
  • Managed identity or service principal
  • Endpoint configuration
  • Permissions
  • Environment configuration

After configuration, Copilot Studio can invoke deployed models as part of prompt execution.

Prompt Design Best Practices

Good prompts generally include:

Role

Tell the model who it is.

Example:

“You are an HR compliance specialist.”

Goal

Describe the objective.

Example:

“Review employee policies.”

Context

Provide supporting information.

Example:

“The organization operates in healthcare.”

Instructions

Explain exactly what should happen.

Example:

“Identify compliance risks.”

Constraints

Limit undesirable behavior.

Example:

  • Don’t speculate.
  • Use only supplied information.
  • Return JSON.

Output Format

Specify the expected structure.

Example:

Summary
Risks
Recommendations
Confidence Score

Prompt Variables

Custom prompts commonly accept variables.

Examples include:

  • User question
  • Customer name
  • Product
  • Ticket number
  • Region
  • Language
  • Conversation history
  • Retrieved documents

Variables make one prompt reusable for thousands of requests.

Example Prompt

Role:
You are an insurance claims specialist.
Task:
Review the submitted claim.
Context:
Use only supplied documents.
Output:
Return:
• Claim summary
• Fraud indicators
• Missing information
• Recommended next steps
Do not invent facts.

Choosing the Right Model

Different prompts benefit from different models.

Examples:

Customer support

  • Low latency
  • Low cost

Legal analysis

  • High reasoning ability
  • Large context window

Coding

  • Strong code generation

Document summarization

  • Long context support

Translation

  • Strong multilingual capabilities

Model Selection Considerations

Factors include:

  • Cost
  • Latency
  • Accuracy
  • Context length
  • Availability
  • Geographic region
  • Compliance requirements
  • Safety capabilities
  • Throughput
  • Scalability

Responsible AI Considerations

When configuring prompts, organizations should:

  • Avoid biased instructions.
  • Protect confidential information.
  • Minimize unnecessary personal data.
  • Ground responses in enterprise knowledge.
  • Validate generated output.
  • Apply content filtering.
  • Review prompts regularly.
  • Monitor model behavior.

Prompt Evaluation

Azure AI Foundry provides tools for evaluating prompts.

Organizations can measure:

  • Accuracy
  • Relevance
  • Faithfulness
  • Groundedness
  • Helpfulness
  • Safety
  • Toxicity
  • Hallucination rate
  • Latency
  • Cost

Evaluation helps determine whether prompt changes actually improve performance.

Prompt Versioning

As prompts evolve, organizations often maintain multiple versions.

Versioning enables:

  • Rollback
  • Testing
  • Controlled releases
  • A/B testing
  • Governance
  • Documentation
  • Change tracking

Common Enterprise Scenarios

Organizations frequently use Foundry-backed prompts for:

  • Customer support
  • IT help desks
  • HR assistants
  • Financial reporting
  • Contract analysis
  • Healthcare documentation
  • Manufacturing troubleshooting
  • Knowledge management
  • Compliance reviews
  • Executive reporting

Best Practices

  • Keep prompts focused on one objective.
  • Provide explicit instructions.
  • Specify output formats.
  • Use variables instead of hardcoding values.
  • Ground prompts with enterprise knowledge whenever possible.
  • Test prompts using multiple scenarios.
  • Monitor latency and token consumption.
  • Select the smallest model that satisfies business requirements.
  • Evaluate prompts continuously.
  • Version prompts before making production changes.

Common Exam Tips

For the AB-620 exam, remember:

  • Azure AI Foundry hosts deployed AI models.
  • The Model Catalog contains many foundation models from multiple providers.
  • Models must typically be deployed before they can be used.
  • Custom prompts provide reusable instructions for LLM interactions.
  • Prompt variables enable reuse across many conversations.
  • Azure AI Search can be combined with Foundry models for grounded responses.
  • Prompt evaluation measures quality and safety.
  • Responsible AI practices remain essential when using custom prompts.
  • Different prompts may use different deployed models.
  • Prompt engineering significantly affects response quality.

10 Practice Exam Questions

Question 1

An organization wants multiple Copilot Studio agents to use the same standardized instructions when summarizing financial reports. What is the best solution?

A. Create a custom prompt that all agents can reuse.

B. Rewrite the instructions in every topic.

C. Store the instructions inside Adaptive Cards.

D. Add the instructions to every user question.

Answer: A

Explanation: A reusable custom prompt centralizes instructions, promotes consistency, and simplifies maintenance across multiple agents.

Question 2

Which Azure AI Foundry component provides access to available foundation models?

A. AI Hub

B. Model Catalog

C. Prompt Flow

D. Azure Monitor

Answer: B

Explanation: The Model Catalog is the repository for browsing, evaluating, and selecting supported foundation models before deployment.

Question 3

A prompt instructs a model to answer only using retrieved enterprise documentation. What primary benefit does this provide?

A. Faster model deployment

B. Reduced token usage

C. Better grounding and fewer hallucinations

D. Automatic translation

Answer: C

Explanation: Restricting responses to trusted enterprise content improves factual accuracy and reduces unsupported or fabricated responses.

Question 4

Before a Copilot Studio agent can use a model from Azure AI Foundry, what must typically occur?

A. The model must be exported to Dataverse.

B. A Power Automate flow must be created.

C. A custom connector must be installed.

D. The selected model must be deployed.

Answer: D

Explanation: Models in the catalog are not directly consumable until they have been deployed to an endpoint.

Question 5

Which prompt component tells the model how it should behave?

A. Context

B. Output format

C. Role

D. Variables

Answer: C

Explanation: The role establishes the model’s persona or expertise, such as “You are a financial analyst.”

Question 6

Why should prompt variables be used instead of hard-coded values?

A. They improve model licensing.

B. They allow prompts to be reused for different inputs.

C. They reduce Azure subscription costs.

D. They eliminate authentication requirements.

Answer: B

Explanation: Variables enable a single prompt template to process many different user requests without modification.

Question 7

An organization compares several prompts for accuracy, groundedness, latency, and safety before production deployment. Which Azure AI Foundry capability are they using?

A. Deployment scaling

B. Resource monitoring

C. Model catalog browsing

D. Prompt evaluation

Answer: D

Explanation: Prompt evaluation measures prompt quality using metrics such as accuracy, groundedness, safety, and response quality.

Question 8

Which consideration is MOST important when selecting a model for a custom prompt?

A. The icon displayed in the model catalog

B. The browser used by administrators

C. The business requirements, including latency, cost, and reasoning capability

D. The number of Copilot Studio topics

Answer: C

Explanation: Model selection should align with workload requirements, balancing performance, cost, context length, and reasoning ability.

Question 9

A prompt specifies that output must always be returned as JSON with predefined fields. What prompt design principle is being applied?

A. Context injection

B. Output formatting

C. Authentication

D. Content indexing

Answer: B

Explanation: Explicitly defining the output structure increases consistency and simplifies downstream processing.

Question 10

Why should organizations maintain multiple versions of important production prompts?

A. To increase model context length

B. To reduce Azure subscription costs

C. To enable rollback, testing, governance, and controlled deployment of prompt changes

D. To eliminate authentication requirements

Answer: C

Explanation: Prompt versioning supports change management, testing, auditing, rollback, and safer deployment of updates without disrupting production agents.

Go to the AB-620 Exam Prep Hub main page