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| Section | Weight | Objectives |
|---|---|---|
| Develop multi-agent solutions in Azure | 30-35% | - Build and integrate tool ecosystems
|
| Architect multi-agent solutions | 15-20% | - Design logical architecture for multi-agent solutions
|
| Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Implement observability and monitoring
|
| Secure, govern, and deploy multi-agent solutions | 20-25% | - Design and implement security for multi-agent solutions
|
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NEW QUESTION # 10
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: A
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 # 11
You have a Microsoft Foundry helpdesk triage agent. Employees sign in to the agent by using Microsoft Entra. The agent can invoke the following tools:
* A ticket search tool that enforces the existing per employee authorization model
* A knowledge article tool that writes to a separate production article repository You need to recommend an identity-based access configuration for the following execution contexts:
* Ensure that interactive ticket searches enforce per employee authorization.
* Constrain approved article updates to the production article repository The solution must meet the following requirements:
* Prevent the use of embedded secrets.
* Follow the principle of least privilege
Which access configurations should you recommend? To answer, drag the appropriate configurations to the correct execution contexts. Each configuration may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Interactive ticket lookup: Delegated permissions from the signed-in employee; Approved article updates: A managed identity scoped to write production article records.
Ticket search must preserve the existing per-employee authorization model, so the downstream operation should execute in the signed-in employee ' s delegated identity context. That allows the ticket service to enforce the same user-level permissions it already uses. The knowledge-article update is an application- controlled write to one production repository, so a managed workload identity with only the required write permission is the least-privilege choice. Microsoft identity guidance distinguishes delegated/on-behalf-of access for user-context operations from managed or agent identities for service-to-service work. Both approaches also eliminate embedded secrets when configured with Microsoft Entra authentication. A broad Contributor assignment or shared API key would unnecessarily expand the blast radius and weaken audit attribution. Therefore the mixed model in the answer is intentional: delegated permissions for per-user reads, and a narrowly scoped managed identity for controlled production writes. 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 agent identity
NEW QUESTION # 12
You have a Microsoft Foundry ticket-triage solution that uses connected agents. Each subagent prompt includes instructions for allowed tools and a JSON handoff.
You need to add automated prompt evaluations. The solution must identify changes that cause the subagents to do the following:
* Skip mandated evidence gathering.
* Return payloads that downstream agents cannot process.
* Handle work outside their assigned responsibilities.
How should you configure the evaluation suite? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Mandated evidence/tool behavior: Replay cases and assert tool-call and citation presence; Agent responsibility boundaries: Test in-scope responses and out-of-scope refusals; Workflow handoff contract: Replay fixtures and validate schema-conforming payloads.
The three regressions target different interfaces and should be evaluated with tests that directly observe those interfaces. Required evidence gathering is a process behavior, so replayed cases should assert that mandated tool calls and citations occur. Responsibility boundaries are best tested with both positive and negative prompts: in-scope cases must be handled, while out-of-scope work should be refused or redirected. The JSON handoff is an interface contract, so replay fixtures should be validated against the expected schema to catch missing fields, renamed properties, and type changes before downstream agents fail. Microsoft Foundry ' s agent evaluators and evaluation datasets support process-level tool checks, task-adherence checks, and structured regression testing. The supplied mappings therefore correctly align each evaluation technique with the failure it is intended to detect. For operational use, the measurement should be captured in a repeatable dataset, trace, or automated gate so that the same criterion can be compared across versions. That is more useful than a one-off manual observation and makes regressions visible before they become production incidents.
Official Microsoft reference: Microsoft Foundry - built-in evaluators and evaluation datasets
NEW QUESTION # 13
You need to recommend a memory retrieval strategy to support the planned changes for claim Approval.
What should you recommend? To answer, drag the appropriate resources to the correct requirements. Each resource may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Prior claims: memory-store-490; Contact preferences: memory-store-490.
Both requirements describe information that should persist across conversations for the same customer:
previous claim information and stable contact preferences. Microsoft Foundry Memory is intended for durable, cross-session facts and summaries that agents can retrieve later, while a knowledge base is for shared reference content rather than user-specific history. The refund-processing and customer-refund tools are operational interfaces, not persistence layers. Using the existing memory store for both categories therefore matches the scenario ' s design objective. In production, the memory search tool should also be scoped to the end user so one customer ' s memory cannot be retrieved for another customer. Microsoft documents scope- based isolation for memory stores and supports a user-derived scope such as `{{$userId}}`. The key distinction is that the same memory store can hold multiple kinds of durable customer memory as long as retrieval is correctly scoped; there is no need to create separate tools or knowledge indexes for these two user- specific memory categories.
Official Microsoft reference: Create and use memory in Foundry Agent Service
NEW QUESTION # 14
You have a Microsoft Foundry multi-agent solution. The agents contain the CI/CD evaluation gates shown in the following table.
You call one of the agents by using the request context in the evaluation process as shown in the following table.
The agent receives the following results for the tool.
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:
SchemaGate: Yes; RelevanceGate: Yes; CompletenessGate: No.
The supplied tool result satisfies the structural requirements of SchemaGate: it includes a valid-looking UUID result identifier, a string sourceTool value, an items array, and the required fields on the item. RelevanceGate also passes because the request category is VPN, the returned item category is VPN, and its confidence value of 0.87 is above the required 0.80 threshold. CompletenessGate fails because the rule requires at least two items while the response contains only one. This is a deterministic gate evaluation rather than an LLM-judge interpretation, so the values should be checked directly against the declared criteria. Microsoft Foundry evaluation datasets and CI/CD quality gates are designed to make exactly these regression checks repeatable before release. Therefore the correct sequence is Yes, Yes, No. 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 - evaluation datasets
NEW QUESTION # 15
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