AI-102 Implement an agentic solution — Timed 10 Questions

Question 1

You are building an agentic solution using Microsoft Semantic Kernel. The agent must autonomously decide when to call an external API to fetch real-time data. You want to minimize token usage and avoid unnecessary API calls. Which planner configuration should you use?

Accepted Answer

Use a SequentialPlanner with a stepwise strategy and explicit function parameter constraints. Option D is correct because a SequentialPlanner combined with a stepwise strategy ensures the agent only invokes the API when the context explicitly requires it, reducing token waste. Option A is incorrect because AutoInvoke with greedy planning may over-call APIs. Option B is incorrect because ManualInvoke requires user intervention, defeating autonomy. Option C is incorrect because ParallelPlanner may invoke multiple APIs concurrently, increasing token usage.

Answer

Use a ManualInvoke kernel with a sequential planner

Answer

Use a ParallelPlanner with a function-calling model

Answer

Use an AutoInvoke kernel with a greedy action planner

Question 2

You are implementing an agentic solution using Azure AI Agent Service. The agent needs to maintain conversation context across multiple turns. You configure the agent with a custom prompt that includes a 'system message' and 'few-shot examples'. However, after a few turns, the agent starts repeating the same responses. What is the most likely cause?

Accepted Answer

The 'max_context_length' is set too low, causing earlier turns to be truncated. Option B is correct because a low maximum context length truncates earlier conversation turns, causing the agent to lose context and repeat responses. Option A is incorrect because the token limit per request affects the length of individual responses, not repetition. Option C is incorrect because the Azure OpenAI API does not recycle context unless explicitly configured. Option D is incorrect because changing temperature affects randomness, not repetition due to context loss.

Answer

The 'max_tokens' parameter is set too low for the response

Answer

The agent is using a 'context recycling' feature that resets after each turn

Answer

The temperature is set too high, causing the model to become deterministic

Question 3

Your organization is deploying an agentic solution using Microsoft Copilot Studio. The agent must be able to escalate to a human agent when it cannot resolve a user's request. You need to ensure that the escalation includes the full conversation history. What should you configure?

Accepted Answer

Add a 'Transfer conversation' action and set it to include the full transcript. Option C is correct because 'Transfer conversation' automatically passes the full transcript to the human agent. Option A is incorrect because 'Create a ticket' does not transfer the conversation. Option B is incorrect because 'End conversation' terminates without escalation. Option D is incorrect because 'Start a new topic' does not include history.

Answer

Add an 'End conversation' action and configure a fallback

Answer

Add a 'Create a ticket' action in the topic

Answer

Add a 'Start a new topic' action with context variables

Question 4

You are designing an agentic solution using Azure AI Agent Service with a custom skill that calls an external REST API. The API has rate limits: 100 requests per minute per client. You need to ensure the agent respects this limit without degrading user experience. Which approach should you take?

Accepted Answer

Use a token bucket rate limiter with a shared counter stored in Azure Cache for Redis. Option D is correct because a token bucket with a shared counter across all agent instances ensures global rate limiting without dropping requests unexpectedly. Option A is incorrect because 'retry-after' headers require the agent to handle 429 responses, which can degrade UX. Option B is incorrect because exponential backoff can cause delays. Option C is incorrect because a fixed delay per request does not adapt to varying traffic.

Answer

Set a fixed delay of 600ms between each API call

Answer

Configure the skill to handle HTTP 429 responses with retry-after logic

Answer

Implement exponential backoff in the skill code

Question 5

You are building an agentic solution using Microsoft Semantic Kernel. The agent needs to orchestrate multiple plugins. One plugin returns a large dataset that exceeds the model's context window. What is the best way to handle this?

Accepted Answer

Use a summarization plugin to condense the data before passing it to the model. Option B is correct because summarizing the data reduces its size while preserving key information. Option A is incorrect because truncation may lose critical data. Option C is incorrect because splitting into multiple calls increases latency and tokens. Option D is incorrect because marking as sensitive does not solve the size issue.

Answer

Split the data into multiple smaller API calls and combine results

Answer

Truncate the data to fit the context window

Answer

Configure the plugin to return only a subset of the data and mark the rest as sensitive

Question 6

You are using Microsoft Copilot Studio to create an agent that helps users reset their passwords. The agent should first verify the user's identity using multi-factor authentication (MFA) before proceeding. Which feature should you configure?

Accepted Answer

Configure Authentication settings to require Microsoft Entra ID authentication with MFA policy. Option C is correct because 'Authentication' in Copilot Studio allows you to require Microsoft Entra ID sign-in with MFA. Option A is incorrect because 'Power Automate flow' can call MFA but is not the primary configuration. Option B is incorrect because 'Variable' does not enforce authentication. Option D is incorrect because 'Topic trigger' initiates the topic but does not enforce authentication.

Answer

Add a variable to store the user's identity status

Answer

Add a Power Automate flow that calls Microsoft Entra ID MFA

Answer

Use a 'Sign in' topic trigger from the customer channel

Question 7

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?

Accepted Answer

Use a supervisor agent that delegates to specialized agents, with a shared context store in Azure Cosmos DB. Option A is correct because a supervisor agent with a shared context store allows coordination and handoff. Option B is incorrect because separate independent agents lack coordination. Option C is incorrect because sequential chaining limits flexibility. Option D is incorrect because a single monolithic agent defeats the purpose of specialization.

Answer

Create a single monolithic agent that includes all knowledge bases

Answer

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

Answer

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

Question 8

You are developing an agentic solution that uses Azure AI Search as a knowledge base. The agent needs to retrieve the most relevant documents based on a user query. You notice that the agent sometimes returns irrelevant results. Which configuration should you adjust?

Accepted Answer

Increase the 'top' parameter to retrieve more candidate documents. Option B is correct because increasing the number of results gives the agent more context to choose from, reducing irrelevant returns. Option A is incorrect because decreasing the search score threshold may include more irrelevant results. Option C is incorrect because semantic ranking improves relevance but may not help if the initial retrieval is poor. Option D is incorrect because changing the analyzer affects tokenization, not relevance directly.

Answer

Change the language analyzer to a different language

Answer

Disable semantic ranking

Answer

Decrease the minimum search score threshold

Question 9

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. Option D is correct because 'Trigger phrases' in Copilot Studio are used to match user utterances to topics. Option A is incorrect because 'Entities' extract specific information, not intent. Option B is incorrect because 'Variables' store data. Option C is incorrect because 'Actions' perform operations.

Answer

Configure variables to capture user input

Answer

Add actions to process the input

Answer

Create custom entities to extract key phrases

Question 10

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. Options B and D are correct. B: The agent must have a managed identity assigned to authenticate to resources like Microsoft Graph and Azure SQL Database. D: The agent's code must use the managed identity to acquire tokens, typically via DefaultAzureCredential. Option A is incorrect because connection strings expose secrets. Option C is incorrect because RBAC permissions are assigned to the managed identity, not the agent's service principal directly. Option E is incorrect because connection strings are not used with managed identities.

Answer

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

Answer

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

Answer

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