AI-103 Exam Questions & Answers

The correct answer is A. an Azure Content Understanding in Foundry Tools analyzer. In Azure Content Understanding, an analyzer is the reusable configuration that defines what content type is processed, which elements are extracted, and how the output is structured. Microsoft's analyzer guidance states that analyzers define extraction for text, layout, tables, fields, and output formats such as Markdown and JSON. This makes the analyzer the first required configuration step before downstream reasoning or agent orchestration can use the extracted content.

This matches the requirement because the documents contain scanned text, tables, and multicolumn layouts. Content Understanding document analysis is designed to transform unstructured documents into structured, machine-readable output while preserving document structures and relationships. Its Markdown representation converts unstructured documents into GitHub Flavored Markdown while maintaining content and layout for downstream use.

A generative chat completion request or Azure OpenAI Responses API call could reason over extracted content, but it is not the correct first step for OCR, layout preservation, and Markdown conversion. Azure Language focuses on text analysis after content has already been extracted. Reference topics: Content Understanding analyzers, document extraction, OCR, layout analysis, Markdown output, and downstream reasoning.

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.

The correct answer is A. Set the input_fidelity parameter to high. The scenario requires the generated image to preserve the identity and visual characteristics of the user-supplied product photo. In Azure OpenAI image editing and generation workflows, input_fidelity controls how strongly the model attempts to match the style and features of the input image. Microsoft's documentation states that this parameter lets you make subtle edits without changing unrelated areas, and that high input fidelity preserves input-image features more accurately than standard mode.

Including a prompt and input image is necessary for image-guided generation, but it does not by itself maximize preservation of the product's appearance. The explicit preservation control is input_fidelity, and the requirement specifically asks to maintain product identity and visual characteristics. A groundedness detection filter applies to validating generated text against source data, not preserving visual features in image generation. Lowering temperature may reduce randomness in text generation, but it is not the image-control parameter used to retain product-specific visual details. Reference topics: Azure OpenAI image generation, image edit API, input images, input_fidelity, image-to-image generation, and visual identity preservation.

The correct indexing approach is to use an indexer to extract image data into a normalized_images collection. In Azure AI Search enrichment pipelines, embedded images in PDFs are not passed directly from the text content field into OCR. Instead, the indexer must perform document cracking and image extraction by enabling the indexer image action. Microsoft's Azure AI Search documentation states that image-processing skills such as OCR and image analysis expect normalized images, and that enabling imageAction causes embedded images to be extracted and normalized for downstream skills.

The OCR skill is designed to receive image input from /document/normalized_images/*. Microsoft's skillset tutorial specifically states that the OCR skill assumes a normalized_images field exists and that this field is generated by setting the indexer imageAction configuration to generateNormalizedImages. The document extraction skill reference also confirms that generateNormalizedImages creates an array of normalized images during document cracking for OCR and image analysis.

Option A is incorrect because OCR does not run directly against the index content field. Option B maps outputs after enrichment; it does not extract images. Option D reshapes data but does not create the required normalized image collection. Reference topics: Azure AI Search indexers, AI enrichment, OCR skill, imageAction, and normalized_images.

The solution does not fully meet the goal. Image moderation is appropriate for one part of the risk: blocking unsafe image content before the image is processed. Azure AI Content Safety provides image APIs that detect harmful content, and its harm categories and severity levels can be used to classify and block objectionable image content. This addresses unsafe photos, but it does not address hidden instructions embedded in images.

The second risk is prompt manipulation through extracted image text. After OCR extracts text from the uploaded image, that text becomes untrusted third-party content supplied to a generative model. Microsoft defines document attacks as malicious instructions embedded in third-party content, where the objective is to cause the model to execute unintended commands or alter intended behavior. Prompt Shields are the control designed to detect user prompt attacks and document attacks, including indirect attacks that come from uploaded or referenced content.

Therefore, image moderation alone is incomplete. A complete mitigation would combine image moderation for harmful visual content with Prompt Shields for document attacks, and optionally Spotlighting, so extracted or embedded text is treated as lower trust. Reference topics: Azure AI Content Safety, image moderation, Prompt Shields, document attacks, indirect prompt injection, and multimodal safety.