Plan and manage an Azure AI solution

Your team deploys a customer-support agent in Azure AI Foundry that answers questions using an Azure AI Search index built from a product knowledge base. Users report that the agent frequently returns answers based on outdated product documentation, even though the underlying source documents in Blob Storage were updated two weeks ago. The index was configured with an indexer that ran once during initial setup. What is the most appropriate action to ensure the agent grounds its answers on current content while minimizing manual effort?

Your team deploys an Azure AI Foundry agent that calls a third-party payment API requiring a static API key. Security policy mandates that the key never appears in code, configuration files, or the Foundry project settings in plaintext, and that the key can be rotated without redeploying the agent. Which approach best satisfies these requirements while integrating the credential into the agent's tool call?

You are designing an Azure AI Foundry agent for a legal firm. The agent must answer questions grounded in a corpus of 50,000 contract PDFs that are updated weekly. Answers must cite the exact clause and document, and the firm requires that the retrieval approach handle both keyword-heavy legal terminology (e.g., specific statute numbers) and conceptual paraphrased questions. Which knowledge integration approach should you configure for the agent?

Your team is building an Azure AI Foundry agent that must automatically process supplier invoices that arrive as scanned PDF and image files. The agent needs to extract structured fields (invoice number, line items, totals) from these documents, then reason over the extracted data to flag anomalies before routing them for payment. You need to choose the most appropriate Foundry capability for the document extraction step while keeping the reasoning inside the agent. Which approach should you adopt?

A financial services team is deploying an Azure AI Foundry agent that can execute stock trades through a registered tool. Regulatory policy requires that any trade exceeding $10,000 be reviewed by a licensed broker before execution, while smaller trades and read-only balance inquiries may proceed automatically to maintain responsiveness. Which oversight configuration best satisfies these requirements?

You are building a customer support agent in Azure AI Foundry Agent Service. Users interact with the agent across multiple sessions over several days, and the business requires that the agent recall prior conversation context (previous questions, order numbers, and stated preferences) within an ongoing support case without you having to manually re-send the entire history in each request. Which approach should you use to persist and manage conversational context across turns?

Your team deploys a customer-support agent in Azure AI Foundry that has access to three tools: a knowledge-base retrieval tool, an order-lookup API tool, and a refund-processing API tool. Compliance requires that the agent be able to autonomously answer questions and look up orders, but any refund action must never be executed by the agent without an explicit human decision. You must configure this while keeping all three tools registered to the agent. What is the most appropriate way to govern the agent's behavior?

You are building an Azure AI Foundry agent for a logistics company. The agent must be able to call an existing internal REST API that returns real-time shipment tracking data. The API is documented with an OpenAPI 3.0 specification and is secured behind Microsoft Entra ID. You need the agent to invoke this API as a tool while authenticating without storing any secrets in the agent configuration. Which approach should you use?

Your team is building a customer-support solution in Azure AI Foundry that must orchestrate multiple specialized agents: one agent retrieves order history from an internal database, another drafts responses grounded in policy documents, and a supervisor agent decides which sub-agent to invoke and combines their outputs into a single reply. The team wants a managed capability that coordinates this multi-agent collaboration without building custom routing logic. Which Foundry capability should you use?

You operate a production chat application backed by an Azure AI Foundry model deployment. A newer version of the base model has been released, and you must roll it out to real users while minimizing risk. Your requirements are: expose the new version to only a small percentage of live traffic initially, keep the existing version serving the majority of requests, and be able to shift traffic gradually or roll back instantly if quality metrics degrade. Which deployment approach best meets these requirements?

Your team develops a RAG-based chat application in Azure AI Foundry. You maintain separate development, staging, and production Foundry projects. The lead wants automated promotion of validated changes through these environments using Azure DevOps, with the same evaluation suite executed as a quality gate before any change reaches production. Which approach best integrates the Foundry project lifecycle into a CI/CD pipeline while enforcing this gate?

Your team manages an Azure AI Foundry hub that serves three separate projects: a customer-facing chatbot, an internal HR assistant, and a finance analytics tool. The HR assistant must connect to an Azure AI Search index containing sensitive employee records. Compliance requires that this index be accessible only to the HR assistant project and never exposed to the other two projects, while other resources (such as the shared Azure OpenAI model deployment) remain available to all three. How should you configure the Azure AI Search connection?

You are deploying a customer-facing generative AI assistant on an Azure AI Foundry model deployment. The compliance team requires that any output containing self-harm content be blocked entirely, but they also report that legitimate medical-support responses are being incorrectly rejected because the default content filter is too aggressive on the 'violence' category. You must reduce false positives on violence while keeping the strictest possible blocking for self-harm. What should you do?

Your team deploys a customer-facing chatbot built on an Azure AI Foundry model. During a security review, testers demonstrate that they can bypass instructions by embedding malicious commands inside user-supplied documents that the agent summarizes, causing it to leak its system prompt. You must add a specific guardrail that detects and blocks these indirect injection attempts embedded in external content, while continuing to allow legitimate document summarization. Which Azure AI Content Safety capability should you enable?

Your team runs a customer-support agent in Azure AI Foundry backed by a GPT-4o standard deployment. Finance reports that monthly spend has tripled unexpectedly, and leadership wants proactive notification before the budget is exceeded next month, plus visibility into which application is driving token consumption. Which combination of actions best meets these requirements with the least custom development?

You operate a customer-support classification model deployed through Azure AI Foundry. Over the past two months, support agents report that the model increasingly misroutes tickets, even though it performed well at launch and the model itself has not been retrained or changed. You suspect the nature of incoming ticket text has shifted due to a new product line. Which monitoring capability should you configure to confirm and quantify this problem?

A financial services company is designing an Azure AI Foundry solution that must serve customers in the European Union. Legal compliance requires that all customer data used for inference and all model processing remain within EU borders. The team also needs guaranteed capacity for a GPT-4o model deployment during predictable business-hour peaks, with no cross-region data transfer under any circumstances. Which combination of choices best satisfies these requirements?

Your team is preparing to promote a customer-support generative AI application from staging to production in Azure AI Foundry. Compliance requires that, before each release, the app be tested against a curated dataset of hundreds of representative and adversarial prompts to produce quantitative scores for groundedness, relevance, and content harm categories such as violence and self-harm. The results must be reviewable as a report and comparable across releases. Which approach in Azure AI Foundry best meets this requirement?

Your organization is standing up Azure AI Foundry for three separate application teams. Each team needs an isolated workspace for its own agents and model deployments, but the platform team wants to centrally configure shared connections (to Azure AI Search and Azure OpenAI), enforce common networking, and manage security and cost governance in one place. Which infrastructure design best meets these requirements?

Your team deployed a fine-tuned language model to an Azure Machine Learning managed online endpoint that is integrated with your Foundry project. During business hours, traffic is highly variable, with sudden spikes that occasionally cause request queuing and timeouts, while overnight traffic is nearly zero. You must minimize cost during idle periods while automatically handling spikes without manual intervention. What should you configure?