AI-103 Sample Questions Answers

The correct answer is B. Modify the system message instructions . The case study states that Agent1 answers general questions about Contoso products and that the business requirement is for Agent1 to answer questions only about the products sold by Contoso . This is a behavioral boundary for the agent, so it should be implemented in the highest-priority instructions that define the agent’s role, allowed scope, and refusal behavior.

Microsoft Foundry guidance states that a system message is used to steer model behavior, define the assistant’s role and boundaries, and add safety or quality constraints for the scenario. The system message should instruct Agent1 to answer only when the question concerns Contoso products, use the configured Contoso product documentation as grounding, and politely refuse or redirect questions about non-Contoso products.

Top-p sampling and temperature control randomness, not business-domain scope. Increasing temperature would make responses less deterministic. Few-shot examples can support desired behavior, but examples alone are weaker than explicit system-level instructions for defining operating boundaries. Reference topics: system message design, prompt engineering, agent instructions, response constraints, and grounded generative AI behavior.

The correct recommendation is Azure Content Understanding in Foundry Tools . The case study states that Contoso’s finance department must manually review vendor invoices to verify that invoice details match vendor contract terms, and that the invoices contain tables, logos, and varied layouts that make consistent processing difficult. It also states that the planned solution must evaluate both the visual layout and textual content of the invoices.

Azure Content Understanding is designed for this type of multimodal document-processing workload. Microsoft describes Content Understanding as a Foundry Tool that processes unstructured and multimodal content, including documents and images, and transforms it into structured output for AI applications. It can use document analyzers to extract text, layout, tables, fields, and relationships from diverse document types.

Chat completions alone would not reliably extract structured invoice fields from complex layouts. Azure Document Intelligence can extract OCR, layout, and tables, but Content Understanding is the better end-to-end Foundry capability for combining visual and textual understanding with structured extraction for downstream verification. Image Analysis focuses on image-level visual features and is insufficient for invoice field and table review. Reference topics: Content Understanding, document analyzers, multimodal extraction, invoice processing, tables, layout, and structured JSON output.

The correct recommendation is Azure AI Search. The case study states that the product detail sheets are stored as PDFs in storage1, and that Agent1 must be enabled to retrieve and use detailed product information from those sheets. It also specifies that the indexing pipeline must enable semantic and vector search, and that Agent1 must answer natural language questions about product details by using the product sheet information. Azure AI Search is the Azure service designed to ingest content from sources such as Azure Blob Storage, create searchable indexes, and support keyword, semantic, hybrid, and vector retrieval for Retrieval Augmented Generation (RAG) solutions.

Microsoft’s Azure AI Search guidance states that integrated vectorization can chunk content and generate embeddings during indexing, enabling vector search over source documents. It also states that Azure AI Search supports text and vector queries and can improve raw content for search-related scenarios through enrichment pipelines. Azure Translator is unrelated to retrieval. Document Intelligence can extract document structure, but it is not the retrieval index for Agent1. Grounding with Bing Search retrieves public web content, not Contoso’s private PDFs in storage1. Reference topics: Azure AI Search, RAG, semantic search, vector search, Azure Blob Storage indexing, and agent grounding.

The correct built-in skills are Azure OpenAI Embedding and Text Split. The case study requires an indexing pipeline that enables semantic and vector search over the product sheets stored in Azure Blob Storage, so Agent1 can retrieve relevant product information for natural language customer questions. For a RAG pipeline, long PDF content must first be broken into retrievable chunks, and each chunk must then be vectorized for semantic similarity retrieval.

Microsoft’s Azure AI Search integrated vectorization guidance states that you create a skillset that calls the Text Split skill for chunking and the Azure OpenAI Embedding skill to vectorize the chunks. The Text Split skill breaks text into chunks and provides positional metadata, making it suitable when downstream embedding skills have input-length limits. The Azure OpenAI Embedding skill connects to an embedding model deployed in Azure OpenAI or a Microsoft Foundry project and generates embeddings during indexing.

Language Detection, Entity Recognition, and key phrase extraction can enrich text, but they do not create vector embeddings. Merge is useful for combining OCR text with document text, but it does not satisfy the core vector-search requirement. Reference topics: Azure AI Search skillsets, Text Split skill, Azure OpenAI Embedding skill, integrated vectorization, and RAG indexing.

The correct answer is B. Modify the system message instructions . The case study states that Agent1 answers general questions about Contoso products and that a business requirement is for Agent1 to answer questions only about products sold by Contoso. This requirement defines the agent’s allowed domain and refusal boundary, so it must be expressed in the agent’s system-level instructions. Microsoft Foundry guidance states that system messages steer Azure OpenAI chat model behavior and are used to define the assistant’s role, boundaries, output format, and safety or quality constraints.

The system message should instruct Agent1 to answer only Contoso-product questions, use Contoso product documentation when available, and decline questions about non-Contoso products. This directly enforces the intended business scope at the highest instruction level. Few-shot examples can reinforce desired behavior but are not the primary control for defining mandatory operating boundaries. Top-p sampling and temperature are decoding controls; they influence randomness and diversity, not whether the agent restricts answers to a specific product domain. Increasing temperature would likely reduce consistency. Reference topics: Microsoft Foundry agent instructions, system message design, prompt engineering, response boundaries, and grounded generative AI behavior.

The correct capability is tracing because the requirement is to inspect the execution path of an individual agent run. Microsoft Foundry tracing captures detailed telemetry for agent behavior, including LLM calls, tool invocations, agent decision flows, inputs, outputs, tool results, token consumption, duration, and latency. This is the appropriate observability mechanism when you need to determine which step introduced a delay, whether the agent called the internal knowledge API, what data the tool returned, and how the model used that data before producing the final response. Microsoft’s Foundry observability guidance describes distributed tracing as the mechanism that provides visibility into LLM calls, tool invocations, agent decisions, and inter-service dependencies.

Token usage is useful for cost analysis and prompt optimization, but it does not show ordered run steps or tool-call sequencing. Safety metrics evaluate risk-related output behavior, not latency or tool execution. General monitoring provides aggregate health, latency, success-rate, and dashboard views, but the question asks for per-run sequence inspection and timing breakdowns. Foundry agent tracing specifically supports debugging unexpected behavior and monitoring latency across requests. Reference topics: Microsoft Foundry observability, agent tracing, OpenTelemetry-based traces, tool invocations, LLM call inspection, and latency diagnostics.

The correct answer is D. prompt shields for documents . The OCR text is extracted from uploaded images and appended as additional context, so it is third-party content rather than a direct trusted user instruction. Microsoft Foundry Prompt Shields distinguish between user prompt attacks , which are malicious instructions directly supplied as user prompts, and document attacks , which are hidden or embedded instructions in third-party content such as documents, emails, webpages, or grounded data. Microsoft also notes that Prompt Shields analyze indirect attacks embedded in input documents or images.

This scenario is an indirect prompt injection pattern: an uploaded screenshot may contain text that says to ignore the system prompt or perform unauthorized actions. Because the OCR output is being passed to the multimodal model as contextual content, the appropriate protection is Prompt Shields for documents, which scans externally sourced context for malicious embedded instructions before generation. Protected material text detects known copyrighted text in model outputs, not prompt injection. Image moderation detects harmful visual content categories, not malicious instructions in extracted context. Prompt Shields for user prompts are less precise here because the risk comes from document-like content derived from the uploaded image. Reference topics: Prompt Shields, document attacks, indirect prompt injection, multimodal safety, OCR-derived context, and Foundry guardrails.