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HomeCertificationsAI-102TopicsImplement an agentic solution
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AI-102 Implement an agentic solution Practice Questions

20+ practice questions focused on Implement an agentic solution — one of the most tested topics on the Microsoft Azure AI Engineer Associate AI-102 exam. Each question includes a detailed explanation so you learn why the right answer is correct.

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Sample Implement an agentic solution Questions

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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?

A.Use a SequentialPlanner with a stepwise strategy and explicit function parameter constraints
B.Use a ManualInvoke kernel with a sequential planner
C.Use a ParallelPlanner with a function-calling model
D.Use an AutoInvoke kernel with a greedy action planner

Explanation: 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.

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?

A.The 'max_tokens' parameter is set too low for the response
B.The 'max_context_length' is set too low, causing earlier turns to be truncated
C.The agent is using a 'context recycling' feature that resets after each turn
D.The temperature is set too high, causing the model to become deterministic

Explanation: 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.

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?

A.Add an 'End conversation' action and configure a fallback
B.Add a 'Create a ticket' action in the topic
C.Add a 'Start a new topic' action with context variables
D.Add a 'Transfer conversation' action and set it to include the full transcript

Explanation: 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.

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?

A.Use a token bucket rate limiter with a shared counter stored in Azure Cache for Redis
B.Set a fixed delay of 600ms between each API call
C.Configure the skill to handle HTTP 429 responses with retry-after logic
D.Implement exponential backoff in the skill code

Explanation: 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.

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?

A.Split the data into multiple smaller API calls and combine results
B.Truncate the data to fit the context window
C.Configure the plugin to return only a subset of the data and mark the rest as sensitive
D.Use a summarization plugin to condense the data before passing it to the model

Explanation: 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.

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How to master Implement an agentic solution for AI-102

1. Baseline your knowledge

Start with 10 questions to gauge your current understanding of Implement an agentic solution. This tells you whether you need a concept refresher or just practice.

2. Review every explanation

For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.

3. Focus on exam traps

Implement an agentic solution questions on the AI-102 frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.

4. Reach 80% consistently

Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.

Frequently asked questions

How many AI-102 Implement an agentic solution questions are on the real exam?

The exact number varies per candidate. Implement an agentic solution is tested as part of the Microsoft Azure AI Engineer Associate AI-102 blueprint. Practicing with targeted Implement an agentic solution questions ensures you can handle any format or difficulty that appears.

Are these AI-102 Implement an agentic solution practice questions free?

Yes. Courseiva provides free AI-102 practice questions across all exam topics and domains. The platform includes topic-based practice, mock exams, missed-question review, bookmarked questions, and readiness tracking — no account required.

Is Implement an agentic solution one of the harder AI-102 topics?

Difficulty is subjective, but Implement an agentic solution is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.

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Topic Info

Topic

Implement an agentic solution

Exam

AI-102

Questions available

20+