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| Section | Weight | Objectives |
|---|---|---|
| Secure, govern, and deploy multi-agent solutions | 20-25% | - Design and implement guardrails
|
| Develop multi-agent solutions in Azure | 30-35% | - Implement multi-agent orchestration
|
| Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Design and implement evaluation and validation strategies
|
| Architect multi-agent solutions | 15-20% | - Design logical architecture for multi-agent solutions
|
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NEW QUESTION # 24
You have a multi-agent Retrieval-Augmented Generation (RAG) solution that uses a Foundry IQ knowledge base. The solution includes a support agent and a policy agent.
You have an evaluation dataset that contains the following for each user query
* The expected source IDs
* Retrieved chunks in rank order
* The final agent response
You discover that an embedding model change and a custom analyzer change cause the failed traces shown in the following table.
You need to isolate the failing parts of the RAG pipeline.
Which evaluator should you use for each agent? To answer, drag the appropriate evaluators to the correct agents. 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:
Support agent: Document Retrieval; Policy agent: Document Retrieval.
Both failures are retrieval failures and should therefore be diagnosed with the Document Retrieval evaluator.
The dataset includes expected source IDs and ranked retrieved chunks, which provides the retrieval ground truth required by that evaluator. For the support agent, the expected source appears too low in the ranking after unrelated chunks. For the policy agent, the required exception-table source is missing entirely.
Groundedness would evaluate whether the final response is supported by the context it actually received; it could therefore pass even when the retrieval stage failed to fetch a required source. Microsoft Foundry ' s Document Retrieval evaluator compares retrieved documents against labeled ground truth and reports ranking
/search metrics such as NDCG and Fidelity. The corrected answer is Document Retrieval for both agents. 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 # 25
You have a multitenant platform that uses Microsoft Foundry agents. The agents use conversation-based history for active turns, and the platform runs on multiple stateless container instances. Each tenant has a different transcript retention period, and users expect preferences from previous conversations to influence future sessions.
You need to persist cross-session memory after a service conversation is deleted The solution must meet the following requirements:
* Apply tenant-specific retention to transcript exports.
* Keep the compute tier stateless during scale-out.
* Minimize operational complexity
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Extracted memory facts: Azure Cosmos DB; Cache: Azure Managed Redis with tenant-prefixed keys; Transcript exports: A shared blob container with lifecycle management policies.
The design separates durable memory, distributed cache, and retained transcripts. Azure Cosmos DB is appropriate for durable extracted facts that must survive deletion of a service conversation and remain available across stateless compute instances. Azure Managed Redis provides a shared low-latency cache; tenant-prefixed keys are a standard multitenant pattern that avoids local-process affinity. Transcript exports belong in Blob Storage, where lifecycle management rules can enforce retention and can be filtered by prefixes or tags so different tenant retention policies can coexist in one managed storage design. This combination keeps the compute tier stateless while avoiding unnecessary per-tenant infrastructure. The important security requirement is that every persistence layer also enforce tenant authorization, not just naming conventions. Within the options provided, the stated combination minimizes operational complexity while meeting cross-session persistence and retention needs. 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 Architecture Center - multitenant data and cache patterns
NEW QUESTION # 26
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 # 27
Solution: Use separate a managed identity for each agent and environment Assign Azure roles at the resource level. Does this meet the goal?
Answer: B
Explanation:
Separate managed identities for each agent and environment remove the need to store application secrets and create independent authorization boundaries. Assigning Azure roles at the resource level further limits each identity to only the Storage resource it requires. This sharply reduces lateral movement compared with a shared application principal or subscription-wide role. Microsoft Foundry and Azure identity guidance consistently recommend identity-based authentication, distinct identities when permissions differ, and the narrowest practical RBAC scope. In current Foundry deployments, the exact identity object may be represented through Microsoft Entra agent identity or a federated managed identity relationship, but the architectural principle in the option is correct. Therefore the solution meets both stated goals and the answer is A, Yes. 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 # 28
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 # 29
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