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| Section | Weight | Objectives |
|---|---|---|
| Develop multi-agent solutions in Azure | 30-35% | - Build and integrate tool ecosystems
|
| Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Evaluate solution quality
|
| Secure, govern, and deploy multi-agent solutions | 20-25% | - Design and implement guardrails
|
| Architect multi-agent solutions | 15-20% | - Design logical architecture for multi-agent solutions
|
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NEW QUESTION # 47
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 # 48
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 # 49
You have a Microsoft Foundry agent that answers questions about products. You evaluate the agent responses and discover the following issues:
* Questions about a product named product1 make up 40 percent of user traffic, and responses are returned in inconsistent formats.
* Questions about a product named product2 appear infrequently in the existing logs but have high escalation rates.
The available chat logs include customer names and contact details, and the labeling budget enables subject matter experts (SMEs) to review only a limited subset of training examples.
You need to design a dataset preparation plan to fine-tune the agent. The solution must meet the following requirements:
* Match usage patterns for the product1 questions.
* Cover the product2 questions.
* Meet General Data Protection Regulation < GDPR) and Health Insurance Portability and Accountability Act (HIPAA) privacy requirements for names and contact details.
* Minimize SME review efforts during labeling.
What should you include in the design? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Data acquisition: Production examples for high-volume product1 and synthetic examples for sparse product2; Curation: De-identify source records; Labeling: Use active learning to prioritize SME review.
Product1 represents a large share of real traffic, so production examples best preserve its true usage distribution. Product2 appears infrequently but has high escalation impact, making synthetic generation appropriate for filling the coverage gap without waiting for more production traffic. Because the available logs contain names and contact information, those records should be de-identified before they are reused for training or labeling. Finally, the SME budget is limited, so active learning should prioritize the examples where expert labels are most informative instead of reviewing a uniform random sample. Microsoft Foundry guidance supports synthetic fine-tuning data when real examples are sparse, and Microsoft healthcare/privacy tooling supports de-identification of sensitive identifiers. This design balances representativeness, rare-case coverage, privacy, and labeling efficiency. At implementation time, the same rule should be expressed through the framework or service configuration rather than left only as a natural-language convention. That makes the behavior repeatable across runs, easier to test, and less sensitive to model variability.
Official Microsoft reference: Microsoft Foundry - synthetic fine-tuning data generation
NEW QUESTION # 50
You have an Azure API Management Premium instance that hosts a REST API named inventoryAPl.
You plan to provide Microsoft Foundry agents with the ability to call API operations by using the Model Context Protocol (MCP). You will use API Management as the gateway without a separate MCP backend.
You need to recommend a solution for the MCP deployment that supports the following:
* Microsoft Entra JSON Web Token (JWT) validation
* Azure Monitor diagnostics
* Request quotas
What should you recommend?
Answer: A
Explanation:
Azure API Management can expose an existing managed REST API directly as a remote MCP server, turning selected REST operations into MCP tools without requiring a separate MCP backend. The API Management gateway continues to apply its policy engine, so Microsoft Entra JWT validation, quotas/rate limits, and Azure Monitor/Application Insights diagnostics can be enforced at the MCP endpoint. Azure API Center catalogs APIs but does not itself create the MCP runtime endpoint. Azure Functions or Logic Apps could host MCP-compatible code, but both would introduce the separate backend that the question explicitly says to avoid. Because the API already resides in an APIM Premium instance, using APIM ' s native MCP exposure capability is the minimal and policy-rich design. Therefore B is correct. 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 API Management - Expose REST API as an MCP server
NEW QUESTION # 51
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?
Answer: A
Explanation:
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
NEW QUESTION # 52
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