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| Section | Weight | Objectives |
|---|---|---|
| Develop multi-agent solutions in Azure | 30–35% | - Manage state and memory
|
| Evaluate, optimize, and monitor multi-agent solutions | 20–25% | - Assess performance and reliability
|
| Secure, govern, and deploy multi-agent solutions | 20–25% | - Deploy and maintain solutions
|
| Architect multi-agent solutions | 15–20% | - Design logical architecture for multi-agent systems
|
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NEW QUESTION # 29
You have a Microsoft Foundry multi-agent customer support solution that retrieves grounding data from a shared vector index. The indexed corpus contains product runbooks in Markdown and support articles in HTML Both document types use a consistent hierarchical markup.
You discover that current fixed-size token chunking creates chunks that cross section boundaries.
You need to recommend a chunking approach for the ingestion pipeline. The solution must preserve existing document structure boundaries and minimize custom chunking code.
What should you recommend?
Answer: A
Explanation:
The corpus already contains reliable document hierarchy in Markdown and HTML, so the ingestion pipeline should preserve those author-defined boundaries rather than infer new ones from token counts or topic shifts.
Format-specific header splitters can divide Markdown by heading levels and HTML by structural headers, producing chunks that align with meaningful sections. This directly solves the current problem of fixed-size token chunks crossing section boundaries and requires less custom logic than building a semantic topic-shift chunker. Recursive character splitting can be configured with structure-aware separators, but it remains a more generic fallback when format-specific structure is already available. Microsoft Azure AI Search guidance recommends exploiting document structure such as headings when chunking. Therefore B is the most direct and maintainable approach. In production, add telemetry and regression tests around this behavior so changes to prompts, models, tools, or orchestration do not silently alter the intended contract. The selected approach is the one that best matches the platform ' s native execution semantics.
Official Microsoft reference: Azure AI Search - structure-aware chunking and Markdown indexing
NEW QUESTION # 30
You have a Microsoft Foundry resource that hosts Azure OpenAI model deployments for three projects. Each project is for a different business unit. The projects share the same Foundry resource.
You need to implement a Microsoft Cost Management view that separates the shared model spend by the project The solution must meet the following requirements:
* Use cost data that can be reconciled by using Azure Cost Management.
* Minimize manual tagging.
What should you use?
Answer: B
Explanation:
When multiple projects share one Foundry resource, grouping costs only by Azure resource cannot distinguish project consumption because the resource boundary is shared. Microsoft Foundry integrates with Azure Cost Management by attributing model usage to project metadata/tags, allowing a Cost Analysis view to filter shared resource spending by project. This supports showback or chargeback without requiring administrators to manually tag every usage event. Subscription-level service filters or meter grouping identify the service or billing meter but not which of the three business-unit projects generated the usage. Therefore a Foundry-resource-scoped Cost Analysis view filtered by the project tag is the configuration that both reconciles to Azure Cost Management and minimizes manual tagging. D remains correct. 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 - Manage costs
NEW QUESTION # 31
You have a production-readiness review that includes the following Microsoft Foundry Agent Service Model Context Protocol (MCP) tool configuration, discovered MCP tool metadata, and quality-gate rules.
For each of the following statements, select Yes if the statement is true Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
No / No / Yes
The ADO MCP configuration does not satisfy the stated production gate because it lacks the required allowed- tool restriction and disables approval; its discovered schema also conflicts with the gate ' s schema restrictions. The GitHub configuration likewise cannot be considered schema-compliant based on the shown metadata. The third statement, however, is true: Microsoft Foundry MCP tool configuration supports an
`allowed_tools` allowlist that limits which discovered MCP tools are exposed to the agent. Adding ` " allowed_tools " : [ " get_profile " ]` therefore restricts the GitHub MCP integration to that tool, assuming the name matches the discovered tool exactly. This control is materially stronger tha n relying on prompt instructions because excluded tools are not presented as available choices. The corrected sequence is No, No, Yes. The same configuration should be paired with auditable identity, trace, and evaluation data so reviewers can prove which principal acted, which policy was applied, and why a request was allowed or blocked. That is particularly important for production multi-agent systems with external tools.
Official Microsoft reference: Microsoft Foundry agents - Model Context Protocol tools
NEW QUESTION # 32
You have a Microsoft Foundry multi-agent solution that routes requests from an intake agent to a retrieval agent, and then to a resolution agent. The retrieval agent uses a knowledge search tool.
Security testing reveals the following recurring issues:
* Some users submit jailbreak-style prompts at the start of a conversation.
* Some retrieved documents contain hidden instructions intended to manipulate the downstream agent The legal department at your company requires that final responses be flagged for review if they contain protected text. You need to configure guardrails to resolve the security issues and meet the legal requirements.
What should you configure?
Answer: C
Explanation:
The three risks occur at three different intervention points. Direct jailbreak attempts originate in the user prompt, so User input attacks should be evaluated at the User input boundary and blocked. Malicious instructions embedded in retrieved documents are indirect prompt-injection attacks and belong at the Tool response boundary before the retrieved content can influence downstream reasoning. Protected material in the final response must be flagged for legal review, not necessarily blocked, so an Output control using annotate- only behavior matches the requirement. Options A and B misplace at least one control or block content that the scenario says should only be flagged. Microsoft Foundry guardrails are explicitly designed around these intervention points, making C the configuration that aligns each risk with the stage where it can be detected and acted upon. From a security and governance perspective, the control should be enforced at the narrowest platform boundary that can deterministically block or constrain the action. Relying only on prompt text is weaker because the model can still be induced to behave unexpectedly.
Official Microsoft reference: Microsoft Foundry guardrails - intervention points
NEW QUESTION # 33
HOTSPOT -
You have the following persistence configuration for a Microsoft Foundry multitenant, multi-agent solution.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
No / Yes / Yes
The shared-team cache key uses `teamId` but no tenant identifier, so two tenants that use the same team value can collide; the first statement is therefore false. Microsoft multitenant cache guidance recommends including a tenant dimension in shared cache keys when data must remain isolated. The long-term semantic-memory tier is durable Cosmos DB storage partitioned by `tenantId` and is outside the service-managed Foundry Memory feature, making the second statement true, assuming application authorization enforces the same tenant boundary. The session-state tier uses durable workflow checkpoint storage, so it can restore execution after a process restart even if service-managed conversation history is disabled. Microsoft Agent Framework checkpoints are specifically designed for durable, cross-process workflow resumption. The correct sequence is No, Yes, Yes. The architecture should still be validated with representative end-to-end tests, but the selected component establishes the correct structural boundary first. Microsoft ' s AI-500 blueprint consistently favors explicit scopes, interfaces, and persistence or identity boundaries over prompt-only conventions.
Official Microsoft reference: Azure Architecture Center - multitenant cache and state patterns
NEW QUESTION # 34
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