AI-102 Implement an agentic solution Practice Test 4 — 15 Questions

Question 1

You are implementing an agentic solution using Azure AI Agent Service with multiple agents that need to collaborate. Each agent has access to different knowledge bases. You want to ensure that the agents can share context and hand off tasks to each other seamlessly. Which architecture should you use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use a supervisor agent that delegates to specialized agents, with a shared context store in Azure Cosmos DB. The supervisor agent pattern with a shared context store (e.g., Azure Cosmos DB) enables multiple agents to maintain a consistent conversation state and hand off tasks seamlessly. The supervisor orchestrates specialized agents, each with its own knowledge base, while the shared store ensures context is preserved across agent boundaries, which is essential for collaborative agentic workflows in Azure AI Agent Service.

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Create a single monolithic agent that includes all knowledge bases

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Deploy each agent independently and configure them to call each other via HTTP

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Chain the agents sequentially, passing output from one to the next

Question 2

You are using Microsoft Copilot Studio to create an agent that handles customer support. The agent needs to understand the user's intent from free-text input. Which feature should you use to map user utterances to specific topics?

Accepted Answer

Define trigger phrases for each topic. In Microsoft Copilot Studio, trigger phrases are the primary mechanism for mapping user utterances to specific topics. When a user types a free-text input, the agent's natural language understanding (NLU) engine compares the input against the defined trigger phrases for each topic. The topic with the highest confidence score based on semantic similarity is triggered, enabling intent recognition without requiring exact keyword matches.

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Configure variables to capture user input

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Add actions to process the input

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Create custom entities to extract key phrases

Question 3

You are designing an agentic solution using Azure AI Agent Service. The agent needs to perform actions on behalf of users, such as sending emails and updating databases. The solution must use managed identities for authentication to Azure resources. Which TWO configurations are required?

Accepted Answer

Use DefaultAzureCredential in the agent's code to authenticate to Azure services. Option E is correct because a system-assigned managed identity must first be enabled on the Azure resource hosting the agent (for example, the Azure AI Foundry/Azure AI Services resource or the compute running the agent code) so that Microsoft Entra ID can issue tokens for that resource without storing secrets. Option C is correct because DefaultAzureCredential is the recommended credential chain in the Azure Identity SDK; it automatically picks up the managed identity (via ManagedIdentityCredential/EnvironmentCredential) when running in Azure, allowing the agent code to authenticate to Azure services such as Azure SQL, Storage, or Microsoft Graph without embedding credentials. Option A is not required and contradicts the managed-identity requirement, since connection strings/secrets in Key Vault are a secret-based pattern rather than identity-based authentication. Option B is not required because managed identities are service principals managed by the platform; you do not manually create a service principal in Microsoft Entra ID, and RBAC role assignments are made to the managed identity's principal, not to a separately created app registration. Option D is incorrect because API keys are static secrets and the scenario explicitly mandates managed identities for authentication to Azure resources.

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Store connection strings in Azure Key Vault and reference them in the agent's configuration

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Create a service principal in Microsoft Entra ID and assign RBAC roles to the agent's resource

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Configure the agent to use an API key for each external service

Question 4

You are building an agentic solution using Microsoft Semantic Kernel. The agent uses a planner to orchestrate multiple functions. You want to improve the planner's ability to handle complex user requests that involve multiple steps. Which THREE strategies should you implement?

Accepted Answer

Enable the planner to ask the user for clarification when the request is ambiguous. Option B is correct because allowing the planner to ask the user for clarification when a request is ambiguous prevents it from guessing at missing parameters or intent, which is essential for correctly decomposing complex multi-step requests. Option C is correct because composite functions encapsulate common multi-step sub-tasks into a single callable unit, reducing the planner's reasoning burden and making orchestration of complex workflows more reliable. Option E is correct because few-shot examples of multi-step workflows in the planner prompt demonstrate the expected decomposition and sequencing pattern, improving the planner's ability to generate correct multi-step plans. Option A is not appropriate because arbitrarily limiting available functions removes capabilities the agent needs for complex requests rather than improving planning quality. Option D is not appropriate because a simple, generic prompt provides no guidance on multi-step decomposition and would degrade, not improve, planning performance for complex tasks.

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Limit the number of available functions to reduce planning overhead

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Use a simple, generic prompt to avoid overfitting

Question 5

You run the PowerShell script shown to audit your Azure AI Agent Service agents. The script outputs that several agents have no tools configured. What is the impact on those agents?

Accepted Answer

The agents can only respond to queries using the model's built-in knowledge, without ability to perform actions. Azure AI Agent Service agents without tools configured rely solely on the model's built-in knowledge (e.g., GPT-4o's training data) to generate responses. They cannot execute external actions like calling APIs, querying databases, or running code, which are enabled only when tools (e.g., code interpreter, function calling, or Azure Functions) are explicitly attached. This is by design: tools extend the agent's capabilities beyond the model's static knowledge.

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The agents cannot be deployed until tools are added

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The agents cannot start conversations with users

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The agents will use default tools provided by Azure

Question 6

You are troubleshooting an agent built with Microsoft Copilot Studio. The agent uses a custom topic to check inventory levels. The topic calls a Power Automate flow that returns JSON with 'inStock' boolean. The agent sometimes says 'Item is in stock' even when the flow returns false. What is the most likely cause?

Accepted Answer

The topic's condition is using a variable that is not being updated with the flow output.. The most likely cause is that the topic's condition is referencing a variable that does not get updated with the flow's output. In Microsoft Copilot Studio, when a Power Automate flow returns data, the output must be explicitly assigned to a topic variable. If the condition checks a different variable (e.g., a default or uninitialized one), it will not reflect the actual 'inStock' value from the flow, leading to incorrect responses like 'Item is in stock' even when the flow returns false.

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The Power Automate flow has a timeout and returns default true.

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The agent's response is based on a different variable that defaults to true.

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The agent's topic is not parsing the JSON output correctly.

Question 7

You are designing an agentic solution that uses Microsoft Copilot Studio and Azure AI Search. The agent needs to answer questions based on confidential documents. Which security measure should you implement to ensure the agent only accesses documents the user has permission to read?

Accepted Answer

Implement document-level security using security filters in the search index.. Azure AI Search supports document-level security through security filters, which allow you to restrict search results based on the user's identity. By storing security identifiers (e.g., group memberships or user IDs) as a field in the index and applying an OData filter at query time, the agent can ensure users only see documents they are permitted to read. This is the standard approach for implementing row-level security in Azure AI Search.

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Disable public network access on the Azure AI Search service.

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Use a managed identity for the agent to access the search index.

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Require multi-factor authentication for all users.

Question 8

You are building an agent using Azure AI Agent Service that must execute code in a sandbox environment. The code should be able to install Python packages. Which action type should you use?

Accepted Answer

code_interpreter. The code_interpreter action type in Azure AI Agent Service provides a sandboxed environment where code can be executed, and it supports installing Python packages via pip. This allows the agent to run code that requires additional libraries not pre-installed.

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function

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openApi

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httpRequest

Question 9

You are designing an agent that uses Azure AI Search as a knowledge store. The agent must handle multiple languages. Which feature should you configure in Azure AI Search to ensure the agent retrieves relevant results for queries in different languages?

Accepted Answer

Language analyzers. Language analyzers in Azure AI Search are specifically designed to handle linguistic variations across different languages, such as stemming, stop word removal, and tokenization rules. By configuring the appropriate language analyzer (e.g., 'en.microsoft' for English or 'fr.microsoft' for French) on a searchable field, the agent can retrieve relevant results for queries in multiple languages because the analyzer processes both the indexed content and the query string using the same language-specific rules.

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Scoring profiles

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Semantic search

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Synonym maps

Question 10

You are building an agentic solution using Azure AI Agent Service. The agent needs to send an email via Microsoft Graph API. Which authentication method should you use for the action?

Accepted Answer

OAuth 2.0. Microsoft Graph API requires OAuth 2.0 for authentication because it uses delegated or application permissions to access user data securely. Azure AI Agent Service can use OAuth 2.0 with a managed identity or service principal to obtain an access token for the Graph API, ensuring proper authorization and compliance with Microsoft's security model.

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Client Certificate

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API Key

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Basic Authentication

Question 11

You are deploying an agentic solution using Azure AI Agent Service. The agent needs to be invoked from a custom application using REST API calls. Which endpoint should you use to send a message to the agent?

Accepted Answer

POST /threads/{thread_id}/messages. To send a message to an existing conversation thread in Azure AI Agent Service, you must use the POST /threads/{thread_id}/messages endpoint. This adds the user's message to the specified thread, which the agent can then process in a subsequent run. The REST API requires the thread to already exist, and messages are posted directly to that thread's resource.

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POST /threads/{thread_id}/runs

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POST /threads

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GET /agents

Question 12

You are troubleshooting an agentic solution where the agent is not returning responses within acceptable time limits. You suspect the agent is making too many sequential calls to external tools. Which strategy should you recommend to reduce latency?

Accepted Answer

Enable parallel tool execution. Enabling parallel tool execution allows the agent to invoke multiple external tools simultaneously rather than sequentially, directly reducing the total latency caused by serial tool calls. This is a core optimization in agentic frameworks like Semantic Kernel or AutoGen, where tool calls are independent and can be dispatched concurrently.

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Increase the max token limit

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Add more tools to distribute the load

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Reduce the thread history length

Question 13

You are developing an agentic solution that uses Azure AI Agent Service with a custom function calling tool. The agent needs to call a function that requires authentication to an external API. How should you securely pass the API key to the function?

Accepted Answer

Use Azure Key Vault to store the API key and reference it in the function. Azure Key Vault provides a secure, centralized service for storing and managing secrets like API keys. In Azure AI Agent Service, you can configure the function to retrieve the API key at runtime from Key Vault using managed identities, ensuring the key is never exposed in code, configuration, or prompts. This follows the principle of least privilege and aligns with Azure's security best practices for agentic solutions.

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Hardcode the API key in the function code

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Store the API key in an environment variable

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Pass the API key as part of the agent's system prompt

Question 14

Refer to the exhibit. You have created an assistant with the above configuration. When you send a message 'What is the weather in Seattle?', the assistant responds without calling the function. What is the most likely cause?

Accepted Answer

The function is missing the 'description' property. The function definition lacks a 'description' property. In the Assistants API, the 'description' field is critical for the model to understand when and why to invoke a function. Without it, the model may not recognize that the function is relevant to the user's query about weather, causing it to respond directly instead of calling the function.

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The 'instructions' are not being followed

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The 'strict' parameter is set to true

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The 'tool_resources' code_interpreter has empty file_ids

Question 15

You are building an agent using Microsoft Copilot Studio to handle customer returns. The agent must collect the order ID, reason for return, and then provide a return shipping label. The process requires the user to provide information step-by-step. Which type of conversation flow should you implement?

Accepted Answer

Use multiple question nodes in a sequential flow.. Microsoft Copilot Studio uses 'Question' nodes to collect user input one piece at a time in a sequential flow, which matches the step-by-step requirement for order ID, reason, and shipping label. This approach ensures each piece of data is validated before moving to the next, maintaining a guided conversation.

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Use an adaptive card to collect all inputs in one step.

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Use a single question node to collect all information at once.

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Use a generative answers node to parse the user's intent.