Microsoft AI-103 Exam Dumps

The correct answer is B. Personally Identifiable Information (PII) Detection. The case study states that Agent1 must never reveal customer information, even if a document containing customer data is added accidentally to the product sheet repository in storage1. This is a privacy and compliance control requirement, so the appropriate capability is PII Detection.

Azure Language PII Detection is a Foundry Tools capability that identifies, classifies, and redacts sensitive information across text, conversations, and native documents. Microsoft states that PII Detection can be used to implement privacy controls, reduce sensitive data exposure, and support compliance requirements. In this scenario, PII Detection should be applied to retrieved product-sheet content and generated responses so customer names, contact details, identifiers, and other sensitive values are not exposed to users.

Prompt Shields are important for a separate requirement: protecting Agent1 from malicious instructions hidden in documents or embedded text. Microsoft describes Prompt Shields for documents as protection against hidden instructions embedded in external content. However, the option that directly satisfies the requirement to prevent disclosure of customer information is PII Detection. Self-harm and violence filters address harmful-content categories, not privacy leakage.

The correct selection is D. In Microsoft Foundry Agent Service, tool_choice is the runtime control used to influence whether the model may answer directly or must invoke a tool. Microsoft's tool best-practice guidance states that auto lets the model decide whether to call tools, none prevents tool calls, and required means the model must call one or more tools. This directly addresses the issue where some runs answer from the base model and skip the knowledge base.

For an agentic retrieval solution backed by Azure AI Search through an MCP tool, Microsoft's tutorial states that setting tool_choice='required' ensures the agent always uses the knowledge base tool when processing queries. This produces grounded answers because the run is forced into tool invocation before responding. auto is incorrect because it preserves the nondeterministic behavior already causing missing citations. {'type':'knowledge_base'} is not a valid Foundry tool-choice type. {'type':'mcp'} describes an MCP tool type in some Responses API schemas, but the deterministic guarantee for this agent run scenario is the required tool-call mode. Reference topics: Microsoft Foundry Agent Service, MCP tools, Azure AI Search agentic retrieval, tool_choice, and grounded citations.

The correct built-in analyzer is prebuilt-layout because the requirement is to preserve document layout while extracting content from scanned PDFs. Microsoft's Content Understanding prebuilt analyzer guidance states that prebuilt-layout extracts content and layout elements such as words, figures, paragraphs, and tables, identifies document structure including sections and formatting, and provides detailed layout information beyond basic text extraction. It also states that prebuilt-layout does not require a language model or embedding model, which directly satisfies the no language model deployment requirement.

QR codes are handled through barcode extraction. The analyzer configuration reference states that enableBarcode detects and extracts barcodes and QR codes, returns decoded values, and supports QR Code and Micro QR Code among other barcode types. This option is supported by document-based analyzers, making it compatible with layout-oriented document processing.

prebuilt-read is insufficient because it provides OCR and barcode extraction but foundational text extraction without layout analysis. prebuilt-documentSearch is optimized for RAG ingestion and semantic analysis, which is broader than required. prebuilt-documentFieldSchema proposes extraction schemas rather than extracting full document layout. Reference topics: Content Understanding prebuilt analyzers, layout analysis, OCR, barcode detection, QR code extraction, and document-based analyzers.

The correct answer is D. the custom speech project ID. For custom speech fine-tuning, the Speech to text REST API uses a project property that must refer to the Custom Speech project, not the general Microsoft Foundry project. Microsoft's Custom Speech guidance states that when using the Speech to text REST API for custom speech, you must set the project property to the ID of your custom speech project. It also explicitly notes that the custom speech project ID is not the same as the Microsoft Foundry project ID.

This distinction explains the invalid project ID error. Supplying the Foundry project ID, project URL, or endpoint URL does not identify the Custom Speech project that owns the fine-tuned speech model. The custom speech endpoint URL is used when calling a deployed custom model endpoint for recognition, but it is not the value of the REST API project property. The project URL is also not accepted because the API expects the identifier value. Reference topics: Azure Speech in Foundry Tools, Custom Speech fine-tuning, Speech to text REST API, custom speech project ID, model publication, and endpoint configuration.

The solution does not fully meet the goal. A prompt shield for documents is the correct control for the embedded-instruction portion of the scenario. Microsoft defines Prompt Shields as protection against prompt manipulation, including attacks embedded in third-party or document-like content that is supplied to a generative model. OCR-extracted text from uploaded images is untrusted contextual content, so document attack protection is appropriate for detecting hidden instructions that attempt to override the model's intended behavior.

However, the scenario contains two separate risks: unsafe image uploads and hidden instructions embedded in images. Prompt Shields for documents address indirect prompt injection, but they do not classify or block harmful visual content in the uploaded image itself. Azure AI Content Safety image moderation is the control that scans images for harmful content categories such as sexual content, violence, hate, and self-harm with severity levels that can be used for blocking decisions.

Therefore, document prompt shielding alone is incomplete. A complete mitigation would combine image moderation for unsafe images with Prompt Shields for document attacks, and optionally Spotlighting for lower-trust third-party content. Reference topics: Content Safety image moderation, Prompt Shields, document attacks, indirect prompt injection, and multimodal safety.