Q01 - Question
You are designing peer-to-peer handoffs between specialist agents by using Agent Framework handoff orchestration with HandoffBuilder. You want to prevent context loss and unexpected transfer paths. Which TWO actions should you take?
Domain: Architect multi-agent solutions (15–20%) Type: Multi-select
- A. Write custom code that packages a conversation summary into every handoff, because the framework does not pass conversation history to the receiving agent.
- B. Write target agent instructions that include a domain statement followed by the behavior for out-of-scope requests.
- C. Remove all explicit routing rules, because context broadcast makes circular handoff patterns impossible.
- D. Define explicit routing rules that constrain which agents can transfer to which other agents.
B and D are correct.
Explanation: In Agent Framework handoff orchestration, acknowledgment is implicit in agent instructions. A domain statement plus out-of-scope behavior makes the receiving agent's response explicit and traceable. Explicit routing rules prevent unexpected handoff paths and help the framework detect circular patterns.
A is incorrect: The framework broadcasts the full conversation history to the receiving agent, so manual context packaging is not required.
C is incorrect: Explicit routing rules constrain permitted transfers and help detect circular routing. Context broadcast does not replace them, and all participants still receive broadcast context even with custom rules.
Q02 - Question
Contoso Capital receives research requests that vary widely in scope. The task structure is not known at design time. Compliance reviewers must be able to inspect and modify the decomposition for a request before any specialist agent consumes compute resources. Which architecture meets these requirements?
Domain: Architect multi-agent solutions (15–20%) Type: Single choice
- A. A meta-agent planner that outputs a JSON execution plan in a planning phase, followed by a separate execution phase that runs the subtasks according to the reviewed plan.
- B. A static pipeline of specialist agents that is defined at development time and runs the same way for every request.
- C. A meta-agent planner that executes each subtask itself while it creates the plan, so the plan is built as the work proceeds.
- D. A meta-agent planner that reviews results only after all subtasks finish, and then decides whether to replan.
A is correct.
Explanation: Plan-and-execute architecture separates planning from execution. The plan phase produces a complete decomposition without running any subtask, which gives you an inspection point. You can review the plan, modify it, and then execute it. The planner outputs a structured JSON plan that also serves as an audit trail.
B is incorrect: A static pipeline is designed at development time. It does not adapt to the runtime variability of requests and does not produce a per-request plan to review.
C is incorrect: A meta-agent planner does not execute the task. It analyzes requirements and determines the subtasks, their sequence, and the agents that handle them. Combining planning with execution removes the review point.
D is incorrect: Reflection and replanning review intermediate results after tasks or batches of tasks run. Reviewing only after all subtasks finish occurs after compute is already committed.
Q03 - Question
After a market data collection phase that is expensive to repeat, a Contoso Capital analyst wants bull-case and bear-case analyses to run at the same time. The agents use the Agents v2 Responses API. Which two actions support this design? (Choose two.)
Domain: Architect multi-agent solutions (15–20%) Type: Multiple choice
- A. Serialize the thread state to durable storage, and restore it into a separate thread for each branch.
- B. Point a new response for each branch at the same parent response ID.
- C. Run the data collection phase again for each branch so that the contexts stay independent.
- D. Use background mode so that the parallel branches run concurrently.
B and D are correct.
Explanation: The evidence supports B and D because in Agents v2, you fork by pointing multiple new responses at the same parent response ID. Each branch shares the parent context and diverges independently. Background mode lets the parallel branches run concurrently.
A is incorrect: Thread serialization is the Agents v1 fork approach. Agents v2 forks with a shared previous_response_id and avoids serialization.
C is incorrect: Running data collection again for each branch doubles cost and latency. Forking exists so that the shared setup runs once.
Q04 - Question
Several agents contribute to one research task that is stored as a single Azure Cosmos DB document. Each agent owns its own section of the document, so simultaneous writes to the same version are rare. You need to detect conflicting writes without granting exclusive access for every update. Which design should you use?
Domain: Architect multi-agent solutions (15–20%) Type: Single choice
- A. Use Azure Managed Redis pub/sub to send invalidation messages, and rely on those messages to detect conflicting writes.
- B. Acquire a Redis distributed lock with SETNX for every read-modify-write cycle.
- C. Use a Cosmos DB ETag version check on each write. If the write returns a 412 response, reread the document, reapply the change, and retry.
- D. Disable the Redis cache so that every agent reads directly from Cosmos DB.
C is correct.
Explanation: Optimistic concurrency fits cases where conflicts are rare. The agent reads state, applies its change, and writes with a version check. An ETag mismatch returns a 412 response, and the agent rereads, reapplies, and retries.
A is incorrect: Pub/sub invalidation tells agents to remove cached copies after an authoritative write. It helps agents consume fresh state, but it does not check versions on writes.
B is incorrect: Pessimistic locking grants exclusive access during each read-modify-write cycle. It is intended for cases where conflicts are common, and it adds lock management. Conflicts are rare in this scenario.
D is incorrect: Redis is a hot cache, and Cosmos DB remains authoritative. Removing the cache does not add a version check, so concurrent writes are still not detected.
Q05 - Question
Your A2A platform uses Azure Cosmos DB as a central discovery registry. Agents register when they start. Some agents crash without deregistering. Routing services then return endpoints for agents that no longer exist. You need the registry to remove these entries without manual cleanup. What should you configure?
Domain: Architect multi-agent solutions (15–20%) Type: Single choice
- A. Register each agent with a time-to-live (TTL), and have each agent send heartbeat updates that reset the TTL.
- B. Store each agent endpoint URL in the client configuration so that clients do not query the registry.
- C. Use round-robin distribution across all registered agent instances.
- D. Cache registry results on the client with no expiration so that clients keep using the last known endpoint.
A is correct.
Explanation: A registration TTL lets the registry expire the record of an agent that crashes. Heartbeat updates reset the TTL for agents that are still running, so only orphaned entries expire.
B is incorrect: Hardcoded endpoints in client configuration are the dependency that the registry replaces. They do not remove entries for crashed agents, and they require manual updates.
C is incorrect: Round-robin distribution suits equivalent stateless instances. It does not remove registry records for agents that are no longer running.
D is incorrect: Client-side caching trades possible endpoint staleness for continuity during a registry outage. A cache with no expiration increases stale endpoints and does not clean up the registry.
Q06 - Question
An organization runs on Microsoft Foundry. One team has a large existing Semantic Kernel codebase for its agents. A second team starts a new multi-agent project. That project needs native Foundry integration, managed identity, and explicit, auditable workflow control. Which two recommendations are supported? (Select two.)
Domain: Architect multi-agent solutions (15–20%) Type: Multi-select
- A. Use Microsoft Agent Framework for the new project.
- B. Use AutoGen for the new project so that agents use Agent Service and managed identity.
- C. Let the first team continue using Semantic Kernel, and migrate to Agent Framework on its own timeline.
- D. Use CrewAI for the new project because it provides fine-grained failure handling for production.
A and C are correct.
Explanation: Microsoft Agent Framework is the primary recommendation for new Foundry multi-agent projects. It provides native Foundry integration, managed identity, and a data-flow Workflow model with explicit, type-safe control. Semantic Kernel remains a fully supported stable option for teams with existing investments, and those codebases can migrate when the team chooses.
B is incorrect: AutoGen agents can call Azure OpenAI endpoints, but they do not use Agent Service, managed identity, or conversation persistence. AutoGen is also not the recommended foundation for new Foundry projects.
D is incorrect: CrewAI does not integrate with Agent Service or managed identity. For production systems that need fine-grained failure handling, observability, and Azure ecosystem integration, its abstractions are limiting.
Q07 - Question
A Contoso Capital fundamental-research agent uses the Agents v2 Responses API. A request starts a long-running analysis in one service instance. A different process or instance must retrieve the result later. Which approach meets this requirement?
Domain: Architect multi-agent solutions (15–20%) Type: Single choice
- A. Call responses.create synchronously, and poll AgentRunStatus values until the analysis finishes.
- B. Set store=False so that any process can retrieve the response.
- C. Create a thread in each instance, and copy the messages between the threads.
- D. Use background mode, store the response ID, and retrieve the response from the other process or instance.
D is correct.
Explanation: The evidence supports D because background mode runs the agent asynchronously and returns immediately. You store the response ID and retrieve the response from any process or instance.
A is incorrect: AgentRunStatus values do not exist in Agents v2. A synchronous call also does not return immediately for a long-running task.
B is incorrect: The store=False setting is for zero-data-retention scenarios. It does not provide a way to retrieve a response from another instance.
C is incorrect: Threads and manual message copying belong to the Agents v1 model. Agents v2 uses conversations and responses, and copying messages adds work that background mode does not require.
Q08 - Question
A single agent with a strong system prompt and good tools already handles a company-summary workflow successfully. The workflow steps are strictly sequential and share most of their context. A colleague proposes splitting the workflow into five specialist agents. What should you recommend?
Domain: Architect multi-agent solutions (15–20%) Type: Single choice
- A. Split the workflow into five agents to add parallelism, because more agents improve quality.
- B. Keep single-agent execution, and compare any proposed decomposition against it by measuring latency, API call count, token consumption, and quality scores.
- C. Split the workflow into the smallest possible tasks and assign each task to its own agent.
- D. Apply fine-grained decomposition to every request, and use a meta-agent planner to coordinate it.
B is correct.
Explanation: Single-agent execution is preferable when context sharing is high, dependencies are strictly sequential, or an existing agent already handles the full workflow successfully. Coordination overhead includes API calls, handoff token usage, added latency, and error propagation risk. Compare decomposed and single-agent execution on latency, API call count, token consumption, and quality scores before you add agents.
A is incorrect: Strictly sequential steps do not benefit from parallelism. Add agents only when there is a quality problem that requires them, not to demonstrate architectural sophistication.
C is incorrect: Over-decomposition creates nano-tasks that contain no useful reasoning. These tasks should be handled by tools or utility functions, not agents. It also splits a unified context.
D is incorrect: Fine decomposition adds coordination overhead and should be reserved for high-stakes analyses where maximum quality justifies the extra cost. Applying it to every request does not match this workflow.
Q09 - Question
A research agent uses a five-step prompt chain to analyze a company. Steps 1 through 3 finish and produce structured JSON output. Step 4 fails because of an API rate limit. Steps 1 through 3 are expensive to run. You need a recovery design that handles this transient failure without repeating completed work. Which design should you use?
Domain: Architect multi-agent solutions (15–20%) Type: Single choice
- A. Restart the chain from step 1 and fail fast on every error.
- B. Pass the complete raw output of all earlier steps into every later step so that any step can recompute missing results.
- C. Cache each step's output with a unique identifier, and retry the failed step with exponential backoff by using the cached output of the preceding step.
- D. Replace the chain with one monolithic prompt that performs all five steps at once.
C is correct.
Explanation: Chain checkpointing stores each step's output with a unique identifier. When step 4 fails, you retrieve the cached output of step 3 and retry step 4 without re-executing steps 1 through 3. Exponential backoff fits transient failures such as API rate limits.
A is incorrect: Restarting from step 1 repeats the expensive completed steps. Fail fast applies to persistent failures that indicate logic errors, not transient failures.
B is incorrect: Context injection should pass only the data points each step needs. Passing all raw output increases token cost and reduces focus, and it does not provide a recovery mechanism.
D is incorrect: A monolithic prompt tries to accomplish everything at once. It reduces output consistency compared to a chain and provides no cached intermediate results for retry.
Q10 - Question
A compliance agent and a market analysis agent return conflicting recommendations. Your platform policy defines a domain authority hierarchy in which compliance overrules market analysis. You must choose the resolution strategy for this conflict type. Which strategy should you use?
Domain: Architect multi-agent solutions (15–20%) Type: Single choice
- A. Consensus-based resolution that aggregates the opinions of independent agents.
- B. Priority-based resolution that selects the output from the highest-authority agent.
- C. Orchestrator synthesis that produces a unified recommendation from both perspectives.
- D. Decoupling by using an approximation or restructuring a dependency.
B is correct.
Explanation: Priority-based resolution uses a domain authority hierarchy, such as compliance overruling market analysis. The policy already defines which agent takes precedence, so the platform selects the compliance output.
A is incorrect: Consensus-based resolution aggregates independent opinions. It does not apply a defined authority order, and it cannot correct an error that agents share from an upstream source.
C is incorrect: Orchestrator synthesis handles conflicts that priority and consensus cannot resolve. A defined hierarchy resolves this conflict, so synthesis is not needed.
D is incorrect: Decoupling by approximation or restructuring applies to deadline conflicts. This scenario is a conflict between output recommendations.