Free 10-Question Microsoft AI-103 Quick-Start Practice Test

Check your AI-103 readiness in under 15 minutes with 10 practice questions spanning all 5 Microsoft exam domains — Plan & Manage an Azure AI Solution (25–30%), Generative AI & Agentic Solutions (30–35%), Computer Vision (10–15%), Text Analysis (10–15%), and Information Extraction (10–15%). Instant scoring with detailed explanations, no signup required.

10 AI-103 Sample Questions with Answers

Sample Question 1 — Implement computer vision solutions

A retail company is building an Azure-based shopping app for screen-reader users. When a product photo is uploaded, the app must generate short alt text that helps a person understand what is shown. Which approach is the best fit?

  1. A. Use image understanding or a multimodal model to generate a human-readable description of the photo (Correct answer)
  2. B. Use OCR only and set the extracted text as the alt text for every image
  3. C. Return keyword tags such as colors and objects, and use the tags directly as alt text
  4. D. Use an image generation model to create a cleaner replacement image for accessibility

Correct answer: A

Explanation: Correct answer (A): Accessibility-focused alt text should be a useful human-readable description of what is actually in the image. Image understanding or a multimodal model is the best fit because it can describe visual content in natural language. OCR is only helpful when the key requirement is reading text that appears in the image, and keyword tags are not a good substitute for readable alt text. Image generation solves a different problem and does not help interpret the uploaded image. Why the other options are wrong: - Option B: OCR extracts visible text from an image, but many product photos contain little or no useful text. Even when text exists, OCR alone does not describe the non-text visual content needed for alt text. - Option C: Keyword tags are metadata, not good alt text. Accessibility support should provide a readable description rather than a list of labels. - Option D: Image generation creates or modifies images. It is not the right choice for understanding an existing image and producing alt text.

Sample Question 2 — Implement computer vision solutions

An accounts payable team receives supplier invoices as scans and phone photos. The solution must extract vendor name, invoice total, due date, and line-item tables into structured data for downstream processing. Which Azure approach is best?

  1. A. Use OCR only to read all visible text, then rely on manual parsing for the fields and tables
  2. B. Use Azure AI Document Intelligence for layout-aware field and table extraction (Correct answer)
  3. C. Use image captioning to summarize what the invoice looks like and infer the values
  4. D. Use a multimodal chat model mainly for open-ended questions about the invoice image

Correct answer: B

Explanation: Correct answer (B): This is a structured business-document extraction scenario, not a generic image-description task. Azure AI Document Intelligence is the best choice because it is designed for layout-aware extraction of fields, key-value pairs, and tables from documents such as invoices. OCR alone can read text, but it does not provide the same document-aware extraction behavior. Captioning and open-ended multimodal chat are less suitable for repeatable, production-grade invoice processing. Why the other options are wrong: - Option A: OCR can read text, but the requirement includes structured extraction of fields and tables. Document Intelligence is more appropriate for layout-aware business document processing. - Option C: Captioning gives a high-level description, not precise extraction of invoice fields and tables. - Option D: A multimodal chat model can discuss the document, but the requirement is repeatable structured extraction for downstream processing, which is better handled by Document Intelligence.

Sample Question 3 — Implement generative AI and agentic solutions

You are building a Microsoft Foundry employee policy copilot. HR policies change weekly, and the app must answer only from approved policy documents and include citations. Users currently receive confident answers that are not supported by the source documents. What is the best design change?

  1. A. Move to a larger model deployment and keep all policy knowledge only in the system prompt.
  2. B. Fine-tune the model on the policy documents after each policy update.
  3. C. Use retrieval-augmented generation with Azure AI Search over the approved documents and return citations. (Correct answer)
  4. D. Enable stricter content filtering so unsupported answers are automatically blocked.

Correct answer: C

Explanation: Correct answer (C): RAG with Azure AI Search is the best fit because the requirement is current, enterprise-specific, source-backed answers from approved documents. RAG grounds the model on retrieved content, helps reduce hallucinations, and supports citations for traceability and trust. This is a better Azure-native pattern than relying on a larger model or repeatedly fine-tuning for frequently changing policies. Why the other options are wrong: - Option A: A larger model can still hallucinate and does not by itself provide grounded, source-backed answers from current enterprise documents. - Option B: Fine-tuning is not the default solution for frequently changing business knowledge. Searchable external knowledge sources are usually better for dynamic content. - Option D: Content filtering can reduce unsafe outputs, but it does not guarantee factual grounding, current knowledge, or answers restricted to approved documents.

Sample Question 4 — Implement generative AI and agentic solutions

A product-manual assistant uses Azure AI Search. Users search for exact part numbers such as PX-4400, but they also ask conceptual questions phrased differently from the source text. You need retrieval that handles both exact lexical matches and embedding-based similarity. Which search approach is the best fit?

  1. A. Vector search only
  2. B. Keyword search only
  3. C. Hybrid search (Correct answer)
  4. D. Semantic search only

Correct answer: C

Explanation: Correct answer (C): Hybrid search is the best answer because the scenario requires both exact lexical matching for identifiers like part numbers and embedding-based similarity for conceptually similar phrasing. The blueprint specifically identifies hybrid retrieval as the common choice when both lexical and vector-style matching are needed. Why the other options are wrong: - Option A: Vector search is useful for semantic similarity, but by itself it is not the best fit when exact lexical matches are also critical. - Option B: Keyword search can help with exact identifiers, but it may miss semantically similar content when wording differs from the source documents. - Option D: Semantic search can improve meaning-aware retrieval or ranking, but the scenario explicitly calls for both lexical and embedding-based matching, which is why hybrid is the best choice here.

Sample Question 5 — Implement information extraction solutions

A team is digitizing scanned warehouse inspection checklists. Their rules engine needs table rows, checkbox states, and reading order from each page, but it does not yet need business-specific fields such as an inspection score. Which approach should the developer choose first?

  1. A. Use OCR-only text extraction on each page
  2. B. Use layout analysis to preserve structure and selection marks (Correct answer)
  3. C. Use a prebuilt invoice extraction model for normalized fields
  4. D. Use a chat model to summarize each checklist into free text

Correct answer: B

Explanation: Correct answer (B): Layout analysis is the best first step because the rules engine depends on document structure, not just plain text. It preserves tables, selection marks, and reading order, which are critical for deterministic downstream processing. OCR-only extraction can read text but will not reliably preserve those relationships. Prebuilt invoice extraction targets invoice fields, and summarization would throw away the structure the rules engine needs. Why the other options are wrong: - Option A: OCR-only processing can recover text, but it does not preserve tables, selection marks, or reading order well enough for structural rule processing. - Option C: Prebuilt invoice models are intended for invoice-specific normalized fields, not general checklist structure. - Option D: A free-text summary is not appropriate when the downstream system needs deterministic structural elements from the page.

Sample Question 6 — Implement information extraction solutions

An accounts payable app must process supplier invoices from many vendors. The app needs standardized fields such as vendor name, invoice total, and due date for downstream approval rules. What is the best Azure-native extraction choice?

  1. A. Extract raw text with OCR and parse totals with prompt-based summarization
  2. B. Run layout analysis only and let downstream code infer every field
  3. C. Use a prebuilt document extraction model for invoices (Correct answer)
  4. D. Index scanned PDFs directly in Azure AI Search without field extraction

Correct answer: C

Explanation: Correct answer (C): A prebuilt invoice extraction model is the best fit because invoices are a common document type and the application needs normalized business fields such as totals and due dates. Specialized extraction is more reliable for deterministic invoice fields than OCR plus prompting or layout-only output. Search indexing can support retrieval later, but it does not replace field extraction for approval workflows. Why the other options are wrong: - Option A: OCR plus prompting may work inconsistently, but it is usually less reliable than specialized invoice extraction for deterministic fields. - Option B: Layout analysis preserves structure, but it does not by itself return normalized invoice fields such as vendor name or invoice total. - Option D: Azure AI Search helps with retrieval after extraction, but it does not replace extraction of the fields needed for downstream approval logic.

Sample Question 7 — Implement text analysis solutions

A product team stores plain-text survey comments in Azure. They need to label each comment as positive, neutral, or negative and identify which product features customers praise or criticize. The results will be shown in a dashboard, not used for open-ended generation. Which approach should the developer choose?

  1. A. Use an Azure AI text analysis capability for sentiment analysis with opinion mining. (Correct answer)
  2. B. Use a Microsoft Foundry generative model to rewrite each comment as marketing copy.
  3. C. Use translation to convert every comment into one language before any analysis.
  4. D. Use speech-to-text to infer the tone of each survey response.

Correct answer: A

Explanation: Correct answer (A): Sentiment analysis with opinion mining is the best fit because the requirement is standard text analysis on already-available text: classify sentiment and identify which aspects or features are being praised or criticized. This is exactly the kind of task Azure AI text analysis capabilities are designed for, and it is more direct than using open-ended generation. Why the other options are wrong: - Option B: A generative model can produce text, but rewriting comments as marketing copy does not solve the requirement to classify sentiment and identify praised or criticized features in a consistent analytics pipeline. - Option C: Translation changes language, not meaning extraction. The stem does not say translation is required, so translating first adds unnecessary work and does not directly perform sentiment or opinion mining. - Option D: Speech-to-text is for spoken audio. The source material is already text, so introducing a speech step is unnecessary and does not directly provide sentiment analysis.

Sample Question 8 — Implement text analysis solutions

An insurance intake app receives already-extracted text from claim notes. Adjusters want the app to highlight customer names, company names, and city names so they can route claims to the right team. Which capability is the best fit?

  1. A. Use an Azure AI text analysis capability for named entity recognition. (Correct answer)
  2. B. Use an Azure AI text analysis capability for key phrase extraction.
  3. C. Use an Azure AI text analysis capability for sentiment analysis.
  4. D. Use text-to-speech to read each note aloud to the adjusters.

Correct answer: A

Explanation: Correct answer (A): Named entity recognition is the best choice because the requirement is to identify typed entities such as people, organizations, and locations from text. That differs from key phrase extraction, which surfaces important phrases or topics but does not specifically classify them as names, companies, or cities. Why the other options are wrong: - Option B: Key phrase extraction can identify important topics or phrases, but it is not the best choice when the app must specifically detect and classify names, company names, and city names. - Option C: Sentiment analysis determines tone or opinion, not structured entities such as people, organizations, or locations. - Option D: Text-to-speech synthesizes audio from text. It does not extract entities from the claim notes.

Sample Question 9 — Plan and manage an Azure AI solution

A legal operations team is building an internal assistant in Microsoft Foundry. The assistant must answer only from policy documents that change every week, and users need answers grounded in the latest content. What is the best design choice?

  1. A. Deploy a larger model and rely on its pretraining
  2. B. Fine-tune the model whenever the policies change
  3. C. Use RAG with Azure AI Search over the policy corpus (Correct answer)
  4. D. Increase content filtering to block unsupported answers

Correct answer: C

Explanation: Correct answer (C): RAG is the best fit when answers must come from changing enterprise content. By retrieving current policy documents at runtime and grounding the response on those sources, the assistant can answer from the latest information instead of relying on static model knowledge. Why the other options are wrong: - Option A: A larger model does not solve the core problem of needing current private knowledge. Pretraining alone is not a reliable source for frequently changing enterprise policies. - Option B: Fine-tuning can shape behavior or style, but it is not the primary solution for rapidly changing private documents. Retrieval quality and freshness are more relevant here. - Option D: Content filters can help with harmful content, but they do not provide factual grounding or ensure that answers reflect the latest documents.

Sample Question 10 — Plan and manage an Azure AI solution

A support assistant indexes repair manuals in Azure AI Search. Users often search by exact part number, but they also ask natural-language questions that use different wording from the manuals. Which retrieval approach is the best fit?

  1. A. Use keyword-only retrieval across the index
  2. B. Use vector-only retrieval across the index
  3. C. Use hybrid retrieval in Azure AI Search (Correct answer)
  4. D. Fine-tune the model on the manuals instead

Correct answer: C

Explanation: Correct answer (C): Hybrid retrieval is the best choice when both exact terminology and semantic relevance matter. It combines lexical matching for exact identifiers such as part numbers with vector similarity for natural-language phrasing and conceptually similar content. Why the other options are wrong: - Option A: Keyword-only retrieval can work for exact part numbers, but it is weaker when users ask semantically similar questions using different wording. - Option B: Vector-only retrieval helps with semantic similarity, but it can miss the importance of exact lexical matches such as precise codes or identifiers. - Option D: Fine-tuning changes model behavior, but it does not replace a retrieval strategy when the requirement is to search indexed content effectively.

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Ready for more? The full AI-103 practice test hub has 200+ questions with per-domain drilling: Plan & Manage Azure AI, Generative AI & Agentic, Computer Vision, Text Analysis, and Information Extraction.