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| Section | Weight | Objectives |
|---|---|---|
| Evaluate, optimize, and monitor multi-agent solutions | 20–25% | - Implement observability
|
| Architect multi-agent solutions | 15–20% | - Design workflow and tool integration
|
| Develop multi-agent solutions in Azure | 30–35% | - Implement agents using Azure AI services
|
| Secure, govern, and deploy multi-agent solutions | 20–25% | - Deploy and maintain solutions
|
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NEW QUESTION # 52
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: B
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 # 53
You have a Microsoft Foundry agent named Agent1.
You open Agent1 in the playground and update the instructions.
You need to run a full evaluation against the updated instructions. The solution must meet the following requirements:
* Test the changes by using a synthetic dataset.
* Ensure that the changes are available only for the development team that has access to Agent1.
What should you do first?
Answer: A
Explanation:
The instructions changed in the playground, but a full evaluation needs a stable, versioned agent definition.
Saving the changes as a new version creates an immutable snapshot that can be evaluated against a synthetic dataset while remaining inside the project for the development team. Publishing is a later lifecycle action that makes a version available to broader consumers or production endpoints and is not required for a private development evaluation. Previewing exercises the current playground configuration interactively but does not establish the versioned target needed for repeatable evaluation. Creating an evaluation before saving the new version risks evaluating the previous configuration instead of the intended update. Microsoft Foundry ' s development lifecycle separates edit, save/version, evaluate, and publish stages, so A is the correct first action. 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 agents - development lifecycle
NEW QUESTION # 54
You have a Microsoft Agent Framework solution for customer support that includes the following agents:
* A triage agent
* A refund specialist agent that runs in the same process as the triage agent
* A compliance review agent that is exposed by a partner team as a remote service You need to identify which integration components the triage agent will use to coordinate interactions with the other agents.
What should you identify for each agent? To answer, drag the appropriate components to the correct agents Each component 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:
Refund specialist agent: Agent-as-tools; Compliance review agent: Agent-to-Agent (A2A) protocol.
The refund specialist runs in the same process as the triage agent, so exposing it as an agent-as-tool is the lightweight composition pattern. The outer triage agent can decide when to invoke the specialist while retaining overall task ownership. The compliance reviewer is different: it is a remote service owned by a partner team. Agent-to-Agent (A2A) is the interoperable protocol intended for independently hosted agents that may use a different runtime or framework. It supports capability discovery and remote task interaction without coupling the caller to the partner ' s implementation. Using A2A for the in-process specialist would add unnecessary network/protocol complexity, while treating the partner agent as an in-process tool would not match the deployment boundary. The two selections therefore reflect Microsoft ' s distinction between local agent composition and remote agent interoperability. 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: Microsoft Agent Framework - agents as tools and A2A
NEW QUESTION # 55
You have a Microsoft Foundry agent that completes benefits enrollment during a single user conversation The agent collects the required enrollment fields during 15 turns. Users can correct earlier values before final submission. The current implementation sends the complete transcript with every model request.
You need to change the context accumulation strategy for the active enrollment. The solution must meet the following requirements:
* Preserve the latest value for each required enrollment field until submission.
* Bound the maximum number of tokens sent with each model request
* Preserve user corrections until submission.
* Prevent durable cross-session memory.
What should you do?
Answer: C
Explanation:
The application must preserve the latest authoritative value of each enrollment field even when the user corrects an earlier value, while also placing a firm bound on prompt size. A session-scoped enrollment-state snapshot provides deterministic structured state: each correction overwrites the previous field value. A recent- turn sliding window then preserves enough conversational context for natural interaction without resending the full 15-turn transcript. Response chaining with a fixed number of prior turns can lose an important field once it falls outside the retained history. Summarization can omit or distort corrected values, and automatic truncation likewise offers no guarantee that the latest value of every required field survives. Because the state is scoped only to the active enrollment and is not written to long-term memory, the design also avoids durable cross-session memory. Therefore A is correct. 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 Agent Framework - workflow state and context management
NEW QUESTION # 56
You have a Microsoft Foundry Agent Service solution that includes two agents.
You need to configure memory for the agents. The solution must meet the following requirements:
* Isolate the memory between end users
* Isolate the memory between the agent domains.
* Support the deletion of one user ' s memory without deleting other users ' memory.
Solution: You create a dedicated memory store for each agent and configure a static agent scope value for each memory search tool.
Does this meet the goal?
Answer: A
Explanation:
A separate memory store for each agent correctly isolates the two agent domains, but a static scope shared by every user inside each store does not isolate end users. Multiple users ' memories would be written and searched within the same logical scope. Microsoft Foundry Memory explicitly recommends a user-derived scope such as `{{$userId}}` when per-user isolation is required. Scope-based deletion is also how one user ' s memory can be removed without deleting other users ' memories. With a single static scope, that deletion boundary does not exist. The proposed design therefore solves only the domain part of the problem and fails both user isolation and safe per-user deletion. Since all requirements must be satisfied, the correct answer is B, 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: Create and use memory in Foundry Agent Service
NEW QUESTION # 57
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