MicrosoftのAI-500の実際のテストのオンラインバージョンを使用すると非常に便利です。 オンライン版の利便性を実感すれば、多くの問題の解決に役立ちます。 Jpshiken教材のAI-500オンライン版の利便性は、主に次の側面に反映されています。一方で、オンライン版は機器に限定されません。 AI-500テスト準備のオンラインバージョンは、電話、コンピューターなどを含むすべての電子機器に適用されます。 一方、AI-500学習資料のオンライン版を使用することに決めた場合、WLANネットワークがないことを心配する必要はありません。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architect multi-agent solutions | 15-20% | - Design logical architecture for multi-agent solutions
|
| Topic 2: Secure, govern, and deploy multi-agent solutions | 20-25% | - Design and implement security for multi-agent solutions
|
| Topic 3: Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Optimize prompt and model performance
|
| Topic 4: Develop multi-agent solutions in Azure | 30-35% | - Implement agent memory, context management, and knowledge integration
|
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質問 # 49
You have a LangGraph workflow in Microsoft Foundry that is compiled as app by using a checkpointer. Each request includes a value named ticket_id.
You need to instrument the workflow so that each streamed run sends OpenTelemetry traces to Observability in Foundry. The solution must meet the following requirements:
* Correlate graph steps and tool calls for each request by the supplied ticket__id.
* Use the Azure Al OpenTelemetry tracer with the LangGraph invocation.
How should you complete the code? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
` " thread_id " : ticket_id` and ` " callbacks " : [azure_tracer]`.
LangGraph checkpointers use a stable `thread_id` under the configurable invocation context to associate execution and persisted state with the same logical request or conversation. Setting that value to the supplied
`ticket_id` allows graph steps and tool calls to be correlated to the ticket across a streamed run. Microsoft Foundry ' s LangGraph integration also uses `AzureAIOpenTelemetryTracer` through LangChain/LangGraph callbacks. Adding the tracer in the `callbacks` list causes agent, model, and tool spans to be emitted through OpenTelemetry and become visible in Foundry/Application Insights. A random run ID would not provide the stable checkpointer identity needed for state continuity, and fields such as `span_processors` are configured at a different instrumentation layer. Therefore the two code completions shown in the answer are correct. A robust evaluation program separates process metrics from final-response metrics. The selected answer measures the layer where the stated failure actually occurs, which is essential for deciding whether to change retrieval, orchestration, prompt behavior, or the final generator.
Official Microsoft reference: Microsoft Foundry - develop LangChain/LangGraph agents
質問 # 50
HOTSPOT -
You have a multi-agent solution in a Microsoft Foundry project. The project is connected to an Application Insights resource.
You have the following code that implements tracing.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
No / No / Yes
The first statement is false because content recording is explicitly disabled, so prompt and generated response text are not stored as GenAI span content attributes even though tracing remains active. The second is false because the OpenAI client is obtained before trace-context propagation is enabled; Microsoft documents that clients created before the instrumentation change are not retroactively configured for that propagation behavior. The third statement is true because the tracing decorator for ordinary functions records supported function parameters as `code.function.parameter. < name > ` attributes. Those parameter attributes are separate from GenAI prompt-content recording, so disabling message content does not suppress them.
Consequently `email` and `loyalty_tier` can appear on the `lookup_customer` span. The correct sequence is No, No, Yes. The evaluation should also preserve correlation identifiers and version information where possible so a failed score can be traced back to the exact agent, model, tool call, or retrieval step that produced it. This turns the metric into an actionable diagnostic rather than only a dashboard number.
Official Microsoft reference: Microsoft Foundry - client-side agent tracing
質問 # 51
You have a Microsoft Foundry multi-agent customer support solution. The solution includes a triage agent that uses Azure Al Search to retrieve customer-supplied HTML articles and Azure Functions to retrieve customer relationship management (CRM) case records. A custom guardrail is assigned directly to the agent.
Some retrieved HTML articles contain hidden instructions that attempt to override the triage agent ' s system message You need to configure guardrails to meet the following requirements:
* Evaluate the complete payload from the supported tools before the content is available to the agent.
* Prevent malicious instructions embedded in externally retrieved content from influencing the agent
* Stop processing when tainted data is detected and inform the security team.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Intervention point: Tool response; Risk: Indirect attacks.
The hidden instructions originate in retrieved HTML returned by supported tools, not in the user ' s direct prompt. Microsoft classifies that pattern as an indirect prompt-injection attack. The Tool response intervention point is specifically designed to inspect the complete payload returned from a tool before the content is stored in agent memory or used for subsequent reasoning. Configuring the Indirect attack control there with a blocking action prevents tainted retrieved data from steering the triage agent. Azure AI Search and Azure Functions are among the supported tools for tool-response moderation, which is important because both appear in the scenario. A Tool call control would inspect arguments sent to a tool rather than hostile content coming back from it. User-input attack detection likewise targets the wrong origin. The correct configuration is therefore Tool response plus Indirect attacks, with the operational response set to stop processing and alert the security workflow when malicious content is detected.
Official Microsoft reference: Microsoft Foundry guardrails - intervention points
質問 # 52
You have a Microsoft Foundry project that processes customer requests through several stages: A routing agent receives investigation requests, delegates calculations to a data analysis agent that can use Code Interpreter, and delegates source-grounded summaries to a literature review agent.
You discover the following issues:
* Tasks are sometimes routed to the incorrect agent.
* The format of the final response is inconsistent.
You need to ensure that compound requests are routed consistently, and the final response is in a consistent format. The solution must meet the following requirements:
* Minimize changes to the application code.
* Apply to every future conversation handled by the agents.
* Clarify the expected behavior for representative compound inputs.
Which prompt design should you implement?
正解：B
解説：
The requirements ask for behavior that applies to every future conversation, improves routing for representative compound inputs, and standardizes final output with minimal application-code change. Few- shot instruction examples satisfy all three by demonstrating both the desired routing decision and the expected schema-compliant response for representative cases. Repository-wide constraints list rules but do not demonstrate how ambiguous compound requests should be handled. Per-request prompt cues are not durable across future conversations and would require application logic to inject them repeatedly. System role instructions define domains and objectives but provide less behavioral specificity than examples. Microsoft AI-500 prompt-engineering objectives explicitly include examples and dynamic prompt techniques for shaping complex agent behavior. Therefore C is the strongest design. The implementation should also preserve clear inputs and outputs around this step so that later agents receive only the information they require. This improves debuggability and keeps token, permission, and state growth under control as the workflow becomes more complex.
Official Microsoft reference: AI-500 Study Guide - advanced prompt engineering
質問 # 53
You have a multi-agent solution in a Microsoft Foundry project. The project connects to an Azure Storage account named stgaudit.
You plan to enable a storage-backed tool for the agent The tool will read and write blobs to stgaudit.
You need to create a role assignment for the agent. The solution must follow the principle of least privilege.
Which role should you use?
正解：B
質問 # 54
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もう既にMicrosoftのAI-500認定試験を申し込みましたか。「もうすぐ試験の時間なのに、まだ試験に合格する自信を持っていないですが、どうしたらいいでしょうか。何か試験に合格するショートカットがあるのですか。試験参考書を読み終わる時間も足りないですから・・・」いまこのような気持ちがありますか。そうしても焦らないでくださいよ。試験を目前に控えても、ちゃんと試験に準備するチャンスもあります。何のチャンスですかと聞きたいでしょう。それはJpshikenのAI-500問題集です。これは効果的な資料で、あなたを短時間で試験に十分に準備させることができます。この問題集の的中率がとても高いですから、問題集に出るすべての問題と回答を覚える限り、AI-500認定試験に合格することができます。
AI-500日本語版参考資料: https://www.jpshiken.com/AI-500_shiken.html