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| Section | Weight | Objectives |
|---|---|---|
| Develop multi-agent solutions in Azure | 30-35% | - Implement agent memory, context management, and knowledge integration
|
| Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Implement observability and monitoring
|
| Secure, govern, and deploy multi-agent solutions | 20-25% | - Deploy multi-agent solutions to Azure
|
| Architect multi-agent solutions | 15-20% | - Design logical architecture for multi-agent solutions
|
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NEW QUESTION # 60
You have the following tool integration configuration for a Microsoft Foundry claims-processing assistant.
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:
Yes / Yes / No
The first statement is true because the tool-integration contract returns only the explicitly defined `final_text` from FraudReview to ClaimsPlanner; the planner does not automatically receive the specialist ' s entire internal execution history. The second statement is also true because an external REST service such as PremiumRates can be exposed as an OpenAPI-defined tool. The OpenAPI schema describes operations and parameters, while the surrounding agent system retains conversational state; the REST tool itself does not need a developer-managed chat transcript. The third statement is false under the shown configuration because the partner publishes tool descriptors as published while the planner defines which schemas it accepts or rejects. Compatibility handling therefore occurs at the consumer/tool boundary rather than requiring PartnerIntegrations to rewrite every unsupported schema before invocation. The correct sequence is Yes, Yes, No. 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 agents - OpenAPI tools
NEW QUESTION # 61
You are designing a multitenant software as a service (SaaS) platform that uses multiple agents. Users will send latency-sensitive inference requests to the platform by using a shared API.
Initially, there will be 20 tenants, and the platform will expand to 200 tenants.
You need to identify the compute component for a production agent runtime. The solution must meet the following requirements:
Isolate workloads for each tenant by using containerization.
Dynamically scale based on demand.
Minimize administrative effort.
What should you use?
Answer: A
Explanation:
Microsoft Foundry Agent Service is the managed option that best satisfies containerized production agent execution, dynamic scaling, and low administrative overhead. Hosted agents run in Foundry-managed container compute and scale according to workload demand while the platform manages much of the runtime, endpoint, identity, and operational plumbing. AKS could provide strong tenant/workload isolation and autoscaling, but it also requires Kubernetes cluster operations, policy, upgrades, node pools, and scaling configuration, which conflicts with the requirement to minimize administration as the tenant count grows.
Azure Container Instances is simpler but lacks the same managed autoscaling model, and GPU virtual machines create the highest infrastructure burden. Therefore C is the strongest fit for a SaaS platform that wants production agent compute without owning the orchestration platform. From an architecture perspective, the selection also creates explicit ownership and boundaries that can be tested independently. That matters in multi-agent systems because implicit sharing or loosely defined authority often becomes the source of cross- agent coupling, security drift, and difficult incident diagnosis.
Official Microsoft reference: Microsoft Foundry Agent Service overview
NEW QUESTION # 62
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 # 63
You have a Microsoft Foundry project that contains an incident triage agent.
You have a Model Context Protocol (MCP) server registered in the organizational tool catalog. The MCP server exposes two tools named docs_search and deployment_delete.
You need to ensure that the agent can only invoke docs_search.
What should you configure?
Answer: B
Explanation:
The restriction belongs in the agent ' s MCP tool configuration because Microsoft Foundry supports an
`allowed_tools` allowlist that controls which tools discovered from an MCP server are exposed to the agent.
Configuring the allowlist to include only `docs_search` makes `deployment_delete` unavailable for model selection. This is stronger than adding a sentence to the agent instructions because instructions influence behavior but do not remove a dangerous tool from the callable surface. Project details describe resources rather than per-agent tool exposure, and a transient run setting is not the appropriate persistent configuration boundary for the registered MCP integration. Therefore C, the agent tool configuration, is the correct answer.
Least privilege remains the governing principle: grant only the identity, data, tool, or deployment access required for the specific operation. The selected answer preserves that boundary while still allowing the workflow to satisfy its functional requirement. 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 agents - Model Context Protocol tools
NEW QUESTION # 64
You have a LangGraph multi-agent workflow that uses Azure Cosmos DB as a checkpointer.
You plan to change the pruning rules for the running message list before the next release You need to implement evaluations tor memory. The solution must meet the following requirements:
* Verify that claim-related state values remain correct at transition points.
* Verify that conversation context is preserved after an interruption.
* Identify the source of any loss of continuity during a run
Which three actions should you perform Each correct answer presents part of the solution NOTE: Each correct selection is worth one point.
Answer: B,C,E
Explanation:
Memory evaluation must verify persistence across interruption, correctness of checkpointed state, and enough telemetry to locate where continuity was lost. Running a scripted multi-turn case with one stable `thread_id` before and after a restart directly tests whether the Cosmos-backed LangGraph checkpointer restores the same conversation/workflow. Comparing persisted checkpoint documents with expected state values at each handoff verifies that claim-related state has not been corrupted by the new pruning rules. Correlating the evaluation run with trace spans then provides the diagnostic path to identify which agent transition or state- management operation caused a failure. Changing the thread ID each turn would intentionally create separate state scopes, and removing durable artifacts would defeat the persistence test. Tool-call arguments alone do not prove workflow-memory continuity. Therefore D, E, and F are the complementary actions required. 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: Azure Cosmos DB integrations for LangGraph and agent state
NEW QUESTION # 65
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