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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
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NEW QUESTION # 82
You have a Microsoft Foundry project that contains three agents as shown in the following table.
Name
Description
TriageAgent
Classifies incoming customer requests
PolicyAgent
Answers policy questions by searching internal content
ActionAgent
Creates or updates tickets by calling an HTTP API
You need to orchestrate the agents to ensure that the customer requests meet the following requirements:
* Support a deterministic, step-based process that uses conditional branching and shared state across the agents.
* Optionally trigger a ticket action based on the triage result.
The solution must minimize development effort.
What should you include in the solution?
Answer: B
Explanation:
The correct answer is a workflow . Microsoft Foundry workflows are designed to orchestrate agents and business logic as declarative, predefined sequences of actions. The official workflow guidance states that workflows are ideal when you need to orchestrate multiple agents in a repeatable process, add branching logic such as if/else, and handle variables without writing application orchestration code. This directly matches the requirement for a deterministic, step-based process with conditional branching and shared state.
In this scenario, TriageAgent can classify the request first, the workflow can store the triage result, and conditional logic can determine whether to invoke PolicyAgent, ActionAgent, or both. The ticket action is optional, so it should be triggered through a workflow condition based on the triage output. This minimizes development effort because the branching, sequencing, and variable handling are managed in the Foundry workflow rather than being manually implemented across separate runs in application code.
A group chat session is better for dynamic agent handoff, not a strict deterministic process. Threads and runs or separate app-coordinated calls require more custom orchestration. Reference topics: Microsoft Foundry workflows, multi-agent orchestration, conditional branching, variable handling, and agent-driven workflows.
NEW QUESTION # 83
You have a Microsoft Foundry project that contains an agent.
The agent uses tools to retrieve internal content and call external APIs. The agent is configured to let the model decide when to call the tools.
You need to publish the agent for a compliance workflow. The solution must meet the following requirements:
* Each workflow run must include a retrieval step before generating a response.
* Tool calls must authenticate by using the published agent's own identity.
* Tool access must use an identity isolated from other project resources.
* Tool access must support audit tracing.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Set tool_choice to: required
Configure the tool to authenticate by: Using a distinct agent identity bound to the client application Set tool_choice to required because the compliance workflow must deterministically include a tool-based retrieval step before the agent generates a response. Microsoft Foundry Agent Service guidance states that tool_choice provides the most deterministic control over tool use: auto lets the model decide, none prevents tool calls, and required forces the model to call one or more tools. This directly corrects the current nondeterministic behavior where the model decides whether to call tools.
For authentication, use a distinct agent identity bound to the client application . Microsoft Foundry creates a shared identity for unpublished or in-development agents, but publishing an agent automatically creates a dedicated agent identity blueprint and agent identity associated with the agent application resource. Published agents authenticate tool calls by using that unique agent identity, and RBAC permissions must be assigned to the new identity. This provides isolation from the broader shared project identity and supports independent audit trails for compliance workflows.
Storing API keys in prompts violates security guidance and prevents robust audit attribution. The shared project agent identity is easier for development, but it has a broader blast radius and does not meet the isolation requirement. Reference topics: Foundry Agent Service tool choice, tool authentication, published agent identities, RBAC, and auditability.
NEW QUESTION # 84
You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions.
The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements:
. Support turn-taking dynamics, where the agent begins to generate the speech output before the user finishes speaking.
. Operate with low latency to maintain a conversational experience.
You need to enable both speech to text and text to speech in a real-time agent interaction.
What should you do?
Answer: C
Explanation:
The correct answer is D. Use real-time speech to text for incoming audio and text to speech for agent responses . The workflow requires continuous audio input, low-latency transcription for reasoning, and spoken output back to the user. Azure Speech in Foundry Tools real-time speech to text is designed for immediate transcription from streaming audio, which satisfies the incoming-audio side of the interaction. Text to speech provides the outbound spoken response path after the agent generates its answer.
This pattern aligns with Microsoft's real-time voice-agent architecture. The Voice Live API overview explains that low-latency speech-to-speech systems integrate speech recognition, generative reasoning, and text-to-speech functionality to create natural voice experiences. It also identifies contact centers as a key scenario and highlights low perceived latency for end users. Embeddings do not decode audio into conversational speech. Batch transcription introduces file-oriented delay and is not suitable for turn-taking.
Speech translation is only appropriate when translating between languages and does not provide the required reasoning-plus-spoken-response loop. Reference topics: Azure Speech in Foundry Tools, real-time speech to text, text to speech, voice agents, low-latency interaction, and conversational turn-taking.
NEW QUESTION # 85
You have an Azure subscription.
You plan to build an app that will use the Azure OpenAI DALL-E model.
You need to deploy the model.
What should you use?
Answer: B
Explanation:
To deploy the Azure OpenAI DALL-E model, the most appropriate tools to use are Azure AI Studio for model management and interaction, and the Azure Command-Line Interface (CLI) for deployment and configuration tasks. Azure AI Studio provides an interactive interface for working with Azure AI models, and the Azure CLI allows you to manage resources, deployments, and other Azure services from the command line.
NEW QUESTION # 86
Hotspot Question
You have a Microsoft Founcy project that contains a Retrieval Augmented Generation (RAG) solution.
You need to run a pre-production evaluation by using labeled CSV dataset that contains the query, context, response and ground truth. The evaluation must measure the following:
- Whether responses address the user query
- Whether responses are supported by the provided context
- Whether responses contain sensitive or proprietary information
Which AI quality evaluation metrics should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Groundedness and Relevance
The two best AI quality evaluation metrics for your pre-production evaluation in Microsoft Foundry are Relevance and Groundedness.
Microsoft Foundry provides built-in, AI-assisted quality evaluators specifically designed to measure these two critical dimensions of a Retrieval-Augmented Generation (RAG) system:
Relevance: This metric directly addresses your first requirement: whether responses address the user query. It evaluates how well the gens.nerated response aligns with and answers the initial user query.
Groundedness: This metric directly addresses your second requirement: whether responses are supported by the provided context. It evaluates the degree to which the generated response is factually supported by and derived strictly from the retrieved context, which helps capture model hallucinatio Box 2: Protected material The best AI quality evaluation metric for this scenario is Protected material.
Direct alignment: The Protected material metric specifically checks whether an AI model outputs copyrighted text, proprietary data, or sensitive information that matches its training data or internal context.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/observability
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/langchain-middleware
NEW QUESTION # 87
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