Q01 - Question
A release updated the ingestion, documentation generation, and reporting agents in one batch. The reporting agent consumes the documentation generation agent's output schema. Azure Monitor signals a quality regression that is traced to the documentation generation agent. The ingestion agent has no dependency on it. What should the rollback workflow do?
Domain: Secure, govern, and deploy multi-agent solutions (20–25%) Type: Single choice
- A. Roll back the documentation generation agent and the reporting agent, and keep the ingestion agent at its new version.
- B. Roll back only the documentation generation agent, and keep the reporting agent at its new version.
- C. Roll back all three agents in the batch, including the ingestion agent.
- D. Roll back only the reporting agent, because it is the agent that consumes the changed output.
A is correct.
Explanation: A partial rollback preserves forward progress for agents that are not affected. The rollback workflow must respect the dependency graph in reverse. When you roll back an agent, you also roll back all agents that depend on it and were updated in the same deployment batch. This keeps the agent network in a consistent, validated state.
B is incorrect: The reporting agent depends on the documentation generation agent and was updated in the same batch. Leaving it at its new version can leave the system in an inconsistent state.
C is incorrect: The ingestion agent does not depend on the failing agent. Rolling it back discards forward progress that the partial rollback is designed to keep.
D is incorrect: The regression is traced to the documentation generation agent. Rolling back only a dependent leaves the failing agent in place.
Q02 - Question
Fabrikam must prevent sensitive information in submitted source code from reaching its eight-agent pipeline. Fabrikam must also enforce consent boundaries and data residency requirements for the workflow. Which configuration meets both requirements?
Domain: Secure, govern, and deploy multi-agent solutions (20–25%) Type: Single choice
- A. Store the pipeline output in Azure Monitor Log Analytics and review it after processing completes.
- B. Use Azure AI Evaluation SDK probe-based testing to detect and remove sensitive information from the submissions.
- C. Redact sensitive information only at the final agent stage, so earlier agents keep the full context.
- D. Use Azure AI Language Service to detect and redact sensitive information before agent processing, and configure Microsoft Foundry project settings to enforce consent boundaries and data residency.
D is correct.
Explanation: Azure AI Language Service detects and redacts sensitive information before it reaches the agent pipeline. Microsoft Foundry project settings enforce consent boundaries and data residency requirements. Together they meet both requirements.
A is incorrect: Log Analytics stores records after processing. Reviewing output afterward does not stop sensitive information from reaching the agents.
B is incorrect: Azure AI Evaluation SDK metrics and probe-based testing measure consistency and trace bias. They are not used here to detect or redact sensitive information.
C is incorrect: Each processing stage creates a privacy exposure point. Redacting only at the final stage leaves earlier stages exposed and does not minimize exposure at each stage.
Q03 - Question
Fabrikam's production monitoring shows that code review results differ across groups of submissions. The review runs through a chain of agents, and the team must identify where in the chain the disparity is introduced. Which approach should the team use?
Domain: Secure, govern, and deploy multi-agent solutions (20–25%) Type: Single choice
- A. Calculate one aggregate fairness score on the final review output and treat the final agent as the source.
- B. Use Azure AI Language Service to redact sensitive information from each submission before it enters the chain.
- C. Use Azure AI Evaluation SDK metrics and probe-based testing to measure consistency, detect disparity, and trace bias sources across the agent chain.
- D. Use Microsoft Foundry project settings to enforce data residency for the agents in the chain.
C is correct.
Explanation: Bias compounds through agent chains, so the source can be in any upstream agent. Azure AI Evaluation SDK metrics and probe-based testing measure consistency, detect disparity, and trace bias sources across the chain.
A is incorrect: A single score on the final output can show that a disparity exists, but it does not show which agent introduced it. Each agent processes the previous agent's output, so the final agent is not necessarily the source.
B is incorrect: Azure AI Language Service detects and redacts sensitive information. It is a privacy control and does not measure or trace bias.
D is incorrect: Foundry project settings enforce consent boundaries and data residency. They do not detect disparity or locate its source.
Q04 - Question
Fabrikam runs an orchestrator agent and several specialist agents in one AKS namespace that uses Azure CNI with NetworkPolicy support. The orchestrator must call the specialists. The specialists have no business reason to call each other. A compromised specialist must be blocked from calling another specialist. Which action should you take?
Domain: Secure, govern, and deploy multi-agent solutions (20–25%) Type: Single choice
- A. Apply a deny-all NetworkPolicy to the agents namespace that preserves only DNS egress, then add an ingress policy that allows specialist pods to receive traffic only from the orchestrator pod on the required port.
- B. Enable mutual TLS between all agent services and keep the default Kubernetes pod networking behavior.
- C. Configure Azure Private Link for Azure Cosmos DB and disable public network access on it.
- D. Enable Azure Network Watcher flow logs and create Microsoft Sentinel alerts for unexpected agent paths, without changing the network configuration.
A is correct.
Explanation: Kubernetes allows all pods to communicate by default. A deny-all policy followed by narrowly scoped allow rules replaces that implicit-allow model. Specialists then accept traffic only from the orchestrator, so a specialist-to-specialist call is blocked.
B is incorrect: Network policies determine which agents can communicate. Mutual TLS determines how they communicate by authenticating both ends and encrypting the connection. It does not restrict the allowed paths.
C is incorrect: Private Link removes public exposure of a dependency. It does not control pod-to-pod traffic inside the namespace.
D is incorrect: Flow logs and Sentinel alerts detect anomalous paths. Detection alone does not block the traffic.
Q05 - Question
Fabrikam updates the security scanning agent so that a required parameter in one of its tools changes type. The orchestration agent calls that tool. Both agents are updated in the same release, and you manage the release with a GitHub Actions workflow. Which pipeline design prevents an incompatible version from reaching production?
Domain: Secure, govern, and deploy multi-agent solutions (20–25%) Type: Single choice
- A. Deploy both agents in parallel jobs, and use production error rates to detect any incompatibility.
- B. Run contract tests in a validation job before any deployment, and use the needs keyword so the orchestration agent job runs only after the security scanning agent job succeeds.
- C. Run unit tests on the security scanning agent only, and deploy the orchestration agent first so it is ready for the new tool schema.
- D. Treat the parameter type change as an optional parameter addition, and skip contract tests for the orchestration agent.
B is correct.
Explanation: Changing a required parameter type is a breaking change that requires coordinated updates. Contract tests compare the new tool schema with the schema that dependent agents expect, and they run in a validation job before deployment. If a contract test fails, the pipeline stops. The needs keyword makes GitHub Actions run the orchestration agent job only after the security scanning agent job succeeds, which matches the dependency order.
A is incorrect: Parallel jobs do not enforce deployment order, and production error rates detect the problem only after the incompatible version is deployed.
C is incorrect: Unit tests validate individual agent behavior, not the integration boundary between agents. Deploying the orchestration agent first reverses the required order, because the security scanning agent must be deployed first.
D is incorrect: Only adding optional parameters is safe. Changing the type of a required parameter is a breaking change, so contract tests are required.
Q06 - Question
An enterprise customer of Fabrikam is preparing for a compliance audit. The customer needs evidence that decisions made by the multi-agent system followed documented processes and that controls were applied consistently. Fabrikam wants to produce this evidence on request from its logs. Which approach should Fabrikam use?
Domain: Secure, govern, and deploy multi-agent solutions (20–25%) Type: Single choice
- A. Run Azure AI Language Service on the logs so that sensitive information is redacted and the audit is complete.
- B. Capture every decision point in Azure Monitor Log Analytics, and use Kusto Query Language (KQL) queries to produce compliance reports.
- C. Keep only the final recommendation for each submission and discard intermediate agent records.
- D. Run Azure AI Evaluation SDK probe-based tests on demand and provide the results as the audit record.
B is correct.
Explanation: Accountability requires audit trails and queryable compliance reporting. Azure Monitor Log Analytics captures every decision point in the workflow, and KQL queries turn the raw logs into evidence for an audit.
A is incorrect: Redaction protects sensitive information. It does not create a record of decisions or support compliance queries.
C is incorrect: Discarding intermediate records removes the history of decision points. Auditors then cannot verify how a determination was reached or whether controls were applied consistently.
D is incorrect: Probe-based tests measure consistency and trace bias. They do not record the decisions the system made in production.
Q07 - Question
After a deployment, a Fabrikam agent shows normal uptime and normal latency, but reviewers report that its outputs are often incorrect. You must add a rollback trigger that detects this kind of regression. Which trigger should you configure?
Domain: Secure, govern, and deploy multi-agent solutions (20–25%) Type: Single choice
- A. An alert on infrastructure health, such as uptime.
- B. An alert on average latency.
- C. An alert on error rate only, using a threshold of more than double the baseline.
- D. An evaluation score drop measured with the Azure AI Evaluation SDK against a baseline from the stable production version.
D is correct.
Explanation: Rollback triggers must reflect actual quality degradation, not only infrastructure health. An agent can have perfect uptime and normal latency while producing incorrect outputs. You establish a baseline by running evaluation sets against the stable production version. If the evaluation score drops more than the threshold percentage, the workflow triggers rollback.
A is incorrect: Uptime shows infrastructure health. In this scenario uptime is already normal while outputs are incorrect.
B is incorrect: Latency is already normal. Average latency also hides tail-latency problems, so P95 latency is the recommended latency signal, and it still does not measure output correctness.
C is incorrect: Error rate detects immediate failures such as exceptions, timeouts, and invalid JSON outputs. It does not measure whether valid-looking outputs are correct.
Q08 - Question
A platform team manages a multitenant multi-agent system. The team must view every active agent across the environment before planning lifecycle governance work. Which service should the team use to gain this visibility?
Domain: Secure, govern, and deploy multi-agent solutions (20–25%) Type: Single choice
- A. Azure API Management
- B. Azure Monitor
- C. Foundry Control Plane
- D. Microsoft Cost Management
C is correct.
Explanation: The Foundry Control Plane provides visibility into every active agent across the environment, making it the correct service for an inventory of active agents.
A is incorrect: Azure API Management enforces token-based quotas and rate limits. It does not provide an inventory of active agents.
B is incorrect: Azure Monitor tracks consumption metrics. It does not provide a centralized view of every active agent's operational status.
D is incorrect: Microsoft Cost Management tracks consumption and supports cost attribution. It does not provide an inventory of active agents.
Q09 - Question
An agent must call a third-party service that supports neither managed identity nor OAuth2. You must use key-based authentication. Which TWO practices should you apply?
Domain: Secure, govern, and deploy multi-agent solutions (20–25%) Type: Multi-select
- A. Store the key in an environment variable on the agent's pods so it is available at startup.
- B. Store the key exclusively in Azure Key Vault and rotate it on a defined schedule.
- C. Use one key for all environments and downstream services to simplify rotation.
- D. Use separate keys for each environment and each downstream service.
B and D are correct.
Explanation: Key-based authentication is a fallback for when managed identity and OAuth2 are unavailable. Store keys exclusively in Azure Key Vault and rotate them on a defined schedule. Use separate keys per environment and per downstream service.
A is incorrect: Keys must not be stored in application code or environment variables. Azure Key Vault is the only supported storage location in this design.
C is incorrect: A single shared key increases the impact of exposure. Separate keys per environment and per downstream service isolate that impact.
Q10 - Question
A finance team requires monthly chargeback reports that allocate AI spending fairly across tenants. The reports must reflect model token usage, container compute time, and storage operations. Which design meets the requirement?
Domain: Secure, govern, and deploy multi-agent solutions (20–25%) Type: Single choice
- A. Configure Azure API Management token-limit policies to generate billing reports.
- B. Collect tenant-tagged consumption metrics in Azure Monitor and combine them with Microsoft Cost Management.
- C. Use Azure API Management quota settings as the source of cost data for each tenant.
- D. Use agent version manifests to calculate the cost for each tenant.
B is correct.
Explanation: Azure Monitor collects tenant-tagged consumption metrics for model token usage, container compute time, and storage operations. Combined with Microsoft Cost Management, these metrics support monthly chargeback reports.
A is incorrect: Token-limit policies enforce usage quotas. They do not collect the compute and storage metrics required for chargeback reports.
C is incorrect: Quota settings in Azure API Management limit token usage. They do not provide the compute and storage data needed for chargeback.
D is incorrect: Version manifests describe configuration versions. They do not contain tenant consumption data.