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You have a customer support agent that uses the Microsoft Foundry Agent Service. Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following: • Multi-turn continuity within the session • Cross-session continuity for the same case • Access to the full interaction history, including user messages, agent messages, tool calls, and tool outputs You need to ensure that the agent automatically reloads the complete history on each new turn. What should you do?
A. Persist only the final model response stored in the client application and prepend the
response to future prompts.
B. Enable memory summarization on the agent definition to persist the context automatically.
C. Create and reuse a conversation by storing the conversation’s ID and supplying the ID on subsequent requests.
You have a Microsoft Foundry project. You plan to build a customer support solution that contains an agent. The solution must meet the following requirements: • Provide accurate, context-aware responses grounded in internal product documentation stored in Azure AI Search. • Require deep, multi-step reasoning across long contexts. • Generate detailed natural language responses. Which type of model should you use to power the agent?
A. a multimodal model
B. a key phrase extraction model
C. a small language model (SLM)
D. a large language model (LLM)
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem. After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen. You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents. Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content. You need to improve response completeness. Solution: You increase the value of the temperature parameter. Does this meet the goal?
A. Yes
B. No
You have an app named App1 that uses a Microsoft Foundry multimodal model deployment. App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context. Some uploaded images contain embedded text. You need to prevent potentially malicious instructions from being processed by the model. What should you use?
A. protected material text
B. prompt shields for user prompts
C. image moderation
D. prompt shields for documents
You have an Azure Speech in Foundry Tools resource that hosts a custom speech to text model deployed to a custom endpoint. An agent uses the endpoint to perform real-time speech recognition. You are approaching the expiration date of the custom speech to text model. What is the expected behavior when the model expires?
A. Speech recognition requests will fall back to the most recent base model for the same locale.
B. Speech recognition requests will continue to use the expired custom model until the model is removed manually.
C. Speech recognition requests will return a 4xx error until a new custom model is deployed.
D. The custom model will be deleted automatically when the model expires.
You have a Microsoft Foundry project that contains a model deployment. You have an application that calls the deployment by using the Azure OpenAl v1 API and DefaultAzureCredential. The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login. You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege. Which role-based access control (RBAC) role should you assign to the developers?
A. Cognitive Services OpenAl User
B. Cognitive Services Data Reader
C. Cognitive Services User
D. Contributor
You have a Microsoft Foundry project that uses Azure Al Search to ground an agent in internal documentation. After a recent content update, users report that the agent's answers have become less accurate. You need to identify whether the retrieved content is negatively influencing the model's generated responses. Which observability signal should you review?
A. prediction drift metrics
B. groundedness evaluation metrics
C. latency breakdown traces
D. indexer status and failure history
You have a Microsoft Foundry project that contains an agent. The agent ingests scanned PDF vendor invoices that contain tables and embedded QR codes. The agent must preserve the PDF layout in the extracted output to ensure that downstream processing can reference sections and tables. You plan to call Azure Content Understanding in Foundry Tools. You need to extract content and layout elements and detect QR codes without requiring a language model deployment. Which built-in analyzer should you use?
A. prebuilt-layout
B. prebuilt-documentFieldSchema
C. prebuilt-read
D. prebuilt-documentSearch
You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed line items and has a table-based layout. Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution. You need to create a single analyzer that meets the following requirements: • Extracts the invoice number, invoice date, vendor name, and total amount across varying templates • Returns confidence scores so that results with confidence below 0.80 can be routed for supervisor review What should you use?
A. the Azure Content Understanding in Foundry Tools prebuilt-layout analyzer
B. a Foundry agent that has groundedness guardrails enabled to extract invoice fields and confidence scores
C. a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing
D. the Azure Content Understanding in Foundry Tools prebuilt-documentSearch analyzer and search.score from the Azure AI Search results for routing
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Al Search as the retriever. You plan to ingest PDFs into an Azure Al Search index to ensure that the agent can ground responses in texts in both documents and embedded images. Users require citations that link to the source files. You need to ensure that during indexing, the images are extracted into a structure that can be used as input for the built-in optical character recognition (OCR) skill. Which indexing approach should you use?
A. a skillset to run the OCR skill directly against the content field of the index
B. the outputFieldMappings parameter to write image data to a searchable field
C. an indexer to extract image data into a normalized_images collection
D. a Shaper skill to restructure the OCR input
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