Microsoft Certified: Multi-Agent AI Solutions Expert
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5 original questions, none of them in the practice exams.
Pick an answer. Show answer explains every option and links its Microsoft source.
5 questions / Explanations link Microsoft sources / Last updated Oct 26, 2026
Question 1
Multiple choice
Domain 1 / Task 1.1 Design logical architecture
A help-desk agent in Foundry Agent Service runs on a model router deployment in Balanced mode, which cut costs by sending simple questions to small models. Engineers sometimes paste long diagnostic logs into a conversation, and those requests now fail with context-exceeded errors, although the largest models in the routing pool accept inputs that long. What should the team change?
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Answer: B
Why
The context window limit of a model router deployment is the limit of the smallest model in its routing pool, so a longer prompt succeeds only if it happens to be routed to a model that can hold it. For context-exceeded errors, Microsoft's guidance is to shorten the prompt or to use a model subset that keeps only models with large enough context windows.
Why not the others
AQuality mode picks the highest-rated model for each prompt, but the deployment's context limit is still set by the smallest model in the pool.
CCost mode widens the quality band toward cheaper models; it does not raise the context limit.
DAuto-update moves the deployment to new router versions, which can change the underlying models, performance and cost; it is not a fix for context limits.
Every weekend an e-commerce company must run the topic-tagging prompt that its live review-moderation agent uses over 2 million archived customer reviews. The results are needed by Monday morning, the job must cost as little as possible, and it must not take capacity from the live agent's Global Standard deployment. What should the team do?
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Answer: C
Why
The Batch API processes large asynchronous groups of requests with a 24-hour target turnaround at 50% less cost than Global Standard. Global Batch requests use a separate enqueued token quota, so the live agent's online traffic is not disrupted.
Why not the others
ACaching discounts only input tokens read from the cache, and every request would still run on the live deployment.
BA second standard deployment is still billed at the Global Standard rate, twice what Global Batch costs.
DThe job would still be billed at the Global Standard rate, twice the batch rate, and would share the live deployment with the moderation agent.
Domain 2 / Task 2.2 Memory, context and knowledge integration
A benefits agent answers employees' questions from 1,200 plan documents indexed in Azure AI Search with the Text Split skill in pages mode, with maximumPageLength set to 2,000 characters and no overlap. Traces show two failures. Eligibility rules that start near the end of one chunk and finish in the next are never retrieved whole, and chunks from the middle of a document do not say which plan they describe, so the agent mixes up plans. Which two changes address these failures? Choose two.
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Answers: B and D
Why
Overlap repeats text from the end of one chunk at the start of the next, which helps maintain continuity and context across chunk boundaries; Microsoft recommends starting at 25% overlap, which is 500 characters on 2,000-character chunks. Appending the document title to chunks from the middle of a document is Microsoft's example of preventing context loss, so each chunk names its plan.
Why not the others
ASmaller chunks add boundaries, and with no overlap each boundary can still cut a rule in two; Microsoft notes that larger chunks and overlap help preserve context.
CSentences mode makes each chunk a single sentence, which splits multi-sentence rules further; pages mode already avoids breaking up sentences.
EpageOverlapLength must be less than half of maximumPageLength, which is 1,000 characters for 2,000-character chunks, so 1,200 is not a valid setting.
A research agent retrieves from an Azure AI Search index, and the team is comparing three index configurations with different scoring profiles and vector weights. Subject experts have labeled how relevant candidate documents are for 400 test queries. The team wants to score each configuration by how well its ranked results match those labels, including whether the most relevant documents come first, and the scoring must not call a model. Which built-in evaluator should they run?
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Answer: A
Why
Document Retrieval is the process evaluator for when retrieval quality is the bottleneck and you have query relevance labels. It compares the retrieved documents with the labels, returns search-quality metrics (Fidelity, NDCG, XDCG, Max Relevance and Holes) and takes no model deployment.
Why not the others
BRetrieval uses an LLM judge to rate how relevant the retrieved chunks are to the query, and it is meant for when there are no ground-truth labels.
CGroundedness judges whether a generated response stays within its context; it does not score the ranking of retrieved documents.
DResponse Completeness compares a generated response with a ground-truth answer, not ranked documents with relevance labels.
An expense agent in a multi-agent solution must call the finance team's ERP API with a client secret. The secret is in the finance team's key vault, which uses the Azure RBAC permission model and holds about 30 other secrets for finance applications. The agent's identity must be able to read that one secret's value and nothing else in the vault, and must not be able to change or delete secrets. Which role assignment should the finance team make for the agent's identity?
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Answer: B
Why
Key Vault Secrets User can read secret contents but not change them, and Azure RBAC for Key Vault allows a role assignment on an individual secret. Microsoft generally recommends assignments at the vault scope and lists a secret shared between applications as an exception, which is this case.
Why not the others
ASecrets Officer can perform any action on every secret in the vault, including changing and deleting them, and it covers the other 30 secrets.
CKey Vault Contributor manages the vault through the control plane and does not allow access to keys, secrets or certificates.
DKey Vault Reader reads metadata only and cannot read secret contents, so the agent could not get the value.
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