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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Develop multi-agent solutions in Azure | 30-35% | - Implement agent memory, context management, and knowledge integration
|
| Topic 2: Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Implement observability and monitoring
|
| Topic 3: Architect multi-agent solutions | 15-20% | - Specify technology components for multi-agent solutions
|
| Topic 4: Secure, govern, and deploy multi-agent solutions | 20-25% | - Design and implement security for multi-agent solutions
|
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NEW QUESTION # 25
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 # 26
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: D
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 # 27
You have a Microsoft Foundry Agent Sen/ice 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 set scope for each memory search tool to
{{Suserld}} Does this meet the goal?
Answer: B
Explanation:
A dedicated memory store for each agent establishes agent-domain isolation. Setting each memory search tool
' s scope to the current user identity establishes the second isolation dimension: users within the same agent domain receive separate logical memory collections. Microsoft Foundry Memory documents `scope` as the partitioning mechanism and specifically supports `{{$userId}}` for automatic per-user isolation. Memory associated with one scope can be deleted without removing memories stored under other scopes, which satisfies the deletion requirement. The source text renders the token imperfectly as `{{Suserld}}`; the current documented syntax is `{{$userId}}`. Interpreting the question as the documented user-scope token, the design satisfies domain isolation, user isolation, and per-user deletion. Therefore A, Yes, is correct. 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: Create and use memory in Foundry Agent Service
NEW QUESTION # 28
You need to recommend an end-to-end release lifecycle for claim Approval. The solution must meet the technical requirements.
What should you recommend for each requirement? To answer, drag the appropriate recommendations to the correct requirements. Each recommendation 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:
Provision Test, Acceptance, and Production by using a Bicep file applied through the CI/CD pipeline in Azure Pipelines; block promotion when quality falls below the threshold by using Foundry Agent Evaluator as a pipeline release gate.
The release lifecycle must satisfy two independent requirements: repeatable infrastructure deployment across DTAP environments and an automated quality gate before promotion. Bicep is Azure ' s infrastructure-as- code language, and Microsoft documents deploying Bicep through Azure Pipelines so the same declarative resources can be promoted consistently across subscriptions. For the promotion decision, an automated Foundry evaluation is the appropriate control because response quality is an AI-system property that should be measured against a defined threshold before the next stage is released. A manual portal deployment would weaken repeatability, while a deployment-only gate would not measure model or agent behavior. The important design point is that infrastructure provisioning and AI quality validation are separate controls in the same pipeline: Bicep establishes environment parity, and the evaluation gate prevents a technically deployable but behaviorally degraded agent version from advancing. 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: Deploy Bicep files with Azure Pipelines; AI-500 Study Guide
NEW QUESTION # 29
You have a Microsoft Foundry multi-agent assistant that uses a Retrieval-Augmented Generation (RAG)- enabled workflow. A retrieval agent returns document chunks to an answer agent, and the answer agent produces the final response.
You have a dataset that contains query, context, and response without document relevance labels.
You need to implement built-in RAG evaluators that meet the following requirements:
* Identify final responses that include content that is NOT supported by the retrieved context.
* Assess whether the retrieved context chunks and final responses support the user query.
Which evaluator should you use for each requirement? To answer, drag the appropriate evaluators to the correct requirements. Each evaluator 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:
Unsupported final-response content: Groundedness; Context support for the query: Retrieval; Final- response support for the query: Relevance.
Groundedness detects whether the generated response contains claims that are not supported by the retrieved context, which directly matches the first requirement. Retrieval evaluates how relevant the provided context chunks are to the query when no document relevance ground truth is available. Relevance evaluates whether the final response directly and adequately answers the query. Microsoft Foundry separates these metrics so teams can diagnose whether a poor RAG result originated in retrieval or generation. Document Retrieval would require labeled retrieval ground truth, which the question explicitly says is absent. Response Completeness likewise depends on expected/ground-truth information. The supplied Groundedness, Retrieval, and Relevance mapping therefore matches the documented evaluator inputs and purposes. 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. 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 - RAG evaluators
NEW QUESTION # 30
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