Free Practice Questions for Microsoft AB-620 Exam

Trace each answer to its source → Instruct the agent to cite sources in knowledge settings; Retrieve from the centralized index → Configure Azure AI Search as the grounding data source; Use an enterprise-approved foundation model → Select a model from the Foundry model catalog.

Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: Each requirement is controlled by a different configuration layer. Source traceability is a presentation and grounding requirement, so citations must be enabled or explicitly required for knowledge-based responses. That allows users to inspect the material used to support a policy answer; it is not achieved merely by choosing a model. Centralized retrieval is provided by connecting the existing Azure AI Search vector index as the knowledge source. This preserves the curated index, semantic or vector retrieval behavior, and enterprise management of the policy corpus instead of copying documents into the agent. Model governance is handled in the prompt configuration by selecting the approved model from the Microsoft Foundry model catalog. Publishing does not automatically replace an unapproved model, and Azure AI Search does not enforce the foundation-model selection. Together, the three settings form a complete RAG path: Azure AI Search retrieves authoritative passages, the governed model produces the response, and citation settings expose provenance. The configuration should then be tested with representative policy questions to confirm that the response is grounded and that the cited source actually supports the answer. Study Guide alignment: Integrate and extend agents in Copilot Studio > Integrate agents with Azure > Configure generative answers by using Azure AI Search with Foundry; Configure custom prompts to use the Foundry model catalog.

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Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: The partner capability is described as an agent available only through a standardized agent-to-agent endpoint. That is the defining use case for an Agent2Agent connection. In Copilot Studio, the primary agent is configured with the external agent ' s A2A message endpoint and the applicable authentication details, after which orchestration can delegate shipment-tracking requests to it. An MCP server exposes tools or resources rather than an independently orchestrated partner agent, so option C changes the integration contract. An indexed knowledge source is also unsuitable because shipment status is dynamic and the requirement is delegation, not retrieval from a copied corpus. Generative responses in the primary agent would have no authoritative live shipment source and would violate the defined architecture. The A2A description should identify shipment tracking precisely so that it does not compete with unrelated agents or tools. Because external-agent support can be preview functionality, Fabrikam should also validate regional availability, authentication, error behavior, latency, and governance before production use. Study Guide alignment: Integrate and extend agents in Copilot Studio > Configure multi-agent collaboration from Copilot Studio > Create a multi-agent solution by using A2A protocol.

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Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: A Fabric Data Agent is the capability designed for questions that must be answered from governed analytical data. Fabrikam ' s backlog, SLA-risk, parts-shortage, and dispatch-delay measures already reside behind a Fabric semantic model, so delegating these requests to the data agent preserves the business definitions, relationships, and security applied in Fabric. The result can be returned in a structured form suitable for weekly operational review rather than being estimated from general model knowledge. A Foundry agent is already assigned to summarization and classification of long-form text; it is not the authoritative source for operational KPIs. An MCP server is appropriate when the user must perform a transaction, such as creating a parts request, but the question asks for performance information rather than an update. Azure AI Search supports document retrieval for policies and procedures, not calculation over the Fabric semantic model. The primary agent ' s description for the Fabric specialist should explicitly mention the supported metrics so generative orchestration consistently delegates those requests instead of attempting an unsupported generative answer. Study Guide alignment: Integrate and extend agents in Copilot Studio > Configure multi-agent collaboration from Copilot Studio > Integrate a Fabric data agent.

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PartsOps per-user authentication → OAuth 2.0; WarrantyRules shared team credential → API key; Tool-selection context → Configure a server description that explains the MCP server ' s role.

Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: The authentication choices must follow the identity requirements of each server. PartsOps performs attributable actions, including parts-request creation, so OAuth 2.0 is the appropriate mechanism for individual users to authorize access without sharing their credentials with the agent. That preserves user context and supports accountability for each transaction. WarrantyRules is explicitly read-only and uses a key shared by the agent team; it should therefore be configured with API-key authentication using the required header or query parameter. Applying the same authentication method to both servers would contradict the stated server designs. Authentication alone does not tell the orchestrator when a server is relevant. A concise, discriminating server description should identify PartsOps as the source for availability checks and parts-request creation and WarrantyRules as the source for warranty validation. Copilot Studio uses the names and descriptions of agents, topics, tools, and knowledge sources during generative orchestration. Accurate metadata reduces accidental calls, prevents the wrong server from handling a request, and makes the multi-agent plan more predictable during testing. Study Guide alignment: Integrate and extend agents in Copilot Studio > Add tools to agents > Configure MCP tools.

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Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: The Service Desk Agent must be both published and available for connections from other agents. Publishing establishes a runtime version that the primary agent can call; an unpublished draft is not a valid connected-agent target. On the Service Desk Agent, the setting that allows other agents to connect and use it must also be enabled. Microsoft additionally requires the connected agent to be in the same environment and the maker to own it or have it shared, but those conditions are not offered as choices. Replacing the agent with an MCP server would discard the existing agent ' s orchestration and ticket-routing logic. Adding it as a knowledge source would expose content for retrieval, not delegate an IT request to another agent. Publishing Operations Concierge alone does not satisfy the prerequisites of the target agent. After connection, the local description in Operations Concierge should clearly define when IT-specific work is delegated, because descriptions influence routing. Any later changes to the Service Desk Agent must be published before the main agent can use the updated version. Study Guide alignment: Integrate and extend agents in Copilot Studio > Configure multi-agent collaboration from Copilot Studio > Integrate an existing agent in Copilot Studio.

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Prevent most builders from modifying shared assets → Share the component collection while controlling edit access; Reuse components without copying → Create a component collection and move the shared components into it; Limit use to the intended primary agent → Restrict the component collection to the primary agent.

Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: A component collection is the correct reusable unit for the escalation topics, MCP definitions, and safety disclaimer. Moving those assets into one collection creates a maintained source that multiple agents can consume without copying and later diverging. Sharing the collection with teammates while controlling edit access separates use permission from modification permission, allowing agent authors to reference the assets while reserving changes for platform engineering. If a sensitive or tightly coupled collection must be usable only by one designated primary agent, the collection can be restricted accordingly. Exporting the primary agent and copying its components would recreate the duplication problem that Fabrikam is trying to eliminate, while restricting every collection to the primary agent would prevent the required cross-agent reuse. The collection should be versioned and tested after changes because a shared update can affect several consumers. Ownership, review, and release responsibilities should also be documented so that reuse does not become an uncontrolled dependency. This design provides consistency, centralized governance, and a smaller maintenance surface across the three agents. Study Guide alignment: Plan and configure agent solutions > Plan an agent solution > Plan reusable agent components.

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Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: The test passed because the score of 0.92 exceeds the configured threshold of 0.80 and the evaluation method recognizes semantic equivalence. The actual answer conveys the same policy meaning as the expected response even though the wording is different, so an exact textual match is unnecessary. The recorded knowledge source is RefundPolicy_KB, which is the expected grounding path; confirming that source behavior is therefore appropriate. No tool was expected or invoked, so investigating tool configuration would add noise rather than address a defect. A score below 1.00 is not itself a failure, and rewriting the expected answer to mirror every valid paraphrase would undermine a meaning-based evaluation. The result can be accepted while still being retained as regression evidence. Reviewers should confirm that the cited or retrieved policy content supports the non-refund statement and that the chosen threshold reflects the business risk. For fixed codes or legally prescribed phrases, exact match might be preferable, but this natural-language policy answer is correctly evaluated by semantic similarity. Study Guide alignment: Test and manage agents > Evaluate agent performance > Review test results.

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Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: Applying custom instructions on the existing generative answers node at the topic level meets the scope requirement because only that topic ' s response behavior is changed. The node remains responsible for searching the configured knowledge sources, so grounding is preserved while the custom instruction imposes an executive-summary structure. Other topics continue using their own node or agent-level behavior. This is different from replacing the generative answers node with an independent prompt tool, which can sever the retrieval path. The topic should keep Search only selected sources configured when source restriction is required, and general knowledge should be disabled if the answer must be based exclusively on enterprise data. Connector authentication and data-policy enforcement remain platform controls and are not weakened by a formatting instruction. The instruction should specify a stable format—such as decision, key facts, risks, and next actions—without asking the model to disclose internal reasoning. Tests should verify both consistent formatting and correct source use under users with different permissions. Study Guide alignment: Plan and configure agent solutions > Configure topics > Configure advanced agent responses with custom prompts; Configure generative answers node.

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Create a new agent flow artifact in the environment.

Configure the invocation trigger that allows the agent to call the agent flow.

Configure the actions that implement the required behavior.

Validate the agent flow behavior with representative test runs.

Make the agent flow available for agent usage.

An agent flow must first exist in the target environment before its trigger or processing logic can be configured. The invocation trigger is then configured so Copilot Studio can initiate the flow during a conversation. For an agent-callable flow, this is typically the When an agent calls the flow trigger. The flow should also return any required results through the corresponding response action.

After establishing the trigger, the author adds the actions that implement the required business process, such as retrieving records, calling connectors, applying conditions, or updating enterprise systems. Representative test runs should then be performed to confirm that inputs are accepted correctly, actions execute successfully, errors are handled appropriately, and expected outputs are returned to the agent.

Once the flow has been verified, it can be published and made available to the agent as a tool. Exposing an incomplete or unverified flow prematurely could cause conversation failures or incorrect tool results.

Relevant study-guide area: Plan and configure agent solutions → Create and monitor agent flows in Copilot Studio → Create an agent flow . See Create an agent flow as a tool and Call an agent flow from an agent .