‹ All AI-103 questions

AI-103 Practice Question 69: Language Detection Ticket Routing

Microsoft Azure AI Apps and Agents Developer Associate (AI-103) · Test 7 · question 9 of 10

Implement text analysis solutionsmedium

A support platform receives tickets in whatever language the customer typed, with no language field set by the submission form. Before a ticket reaches Custom Translator or a language-specific analysis model, the platform needs to identify which language each ticket is written in, including telling apart Bosnian, Croatian, and Serbian text written in Latin script versus Serbian written in Cyrillic, so it is routed to the right downstream pipeline.

Which Azure AI Language feature should sit at the front of the pipeline?

Select one answer
Show answer and explanation

Answer:

  • A. Language detection, which returns the primary language plus a script name and code for languages that use more than one script

Language detection is the purpose-built feature for this: it "can identify more than 100 languages in their primary script," and separately offers script detection, returning "a script name and script code according to the ISO 15924 standard" for languages written in more than one script; Microsoft names Kazakh as an example, and the same multi-script situation applies to Serbian's Latin/Cyrillic usage. That combination of language plus script is exactly what the ticket router needs to pick a downstream pipeline correctly.

- B is wrong because PII detection redacts sensitive spans in text; identifying the document's language isn't its purpose, and bolting language identification onto it isn't how the service is designed to be used. - C is wrong because key phrase extraction assumes the text's language is already known (or auto-detected internally) to extract phrases meaningfully; it isn't a language identifier, and inferring language from extracted-phrase vocabulary is not a supported or reliable technique. - D is wrong because it puts a translation-time setting in the position of an upstream classification step. The ticket needs to be *routed* based on its language before it reaches Custom Translator or an analysis model; Custom Translator translating a source language it auto-detects internally doesn't help the platform choose which pipeline to send the ticket to in the first place.

Reference: What is language detection in Azure Language?

Practice Test 7 ›

Study guides for AI-103

AI engineer roadmap 2026: skills, certs and the orderAI engineer roadmap for 2026: skills to learn in order, AI engineer vs ML engineer, which certifications fit each stage, and what to skip.
AI-200 exam guide: Azure AI Cloud Developer AssociateAI-200 replaced AZ-204: what the Azure AI Cloud Developer exam tests, how it differs from AI-103, price in USD and INR, format and how to prepare.
How AI Is Changing Software Jobs: Where to PrepareHow AI is changing software jobs: what history shows, the evidence on junior developers, what Klarna learned, new AI roles and five domains to prepare for.
AI-901 exam guide: Azure AI Fundamentals after AI-900AI-901 replaced AI-900: learn what changed, how much Python and Microsoft Foundry you need, exam costs in India, and how to prepare.
Best AI certifications 2026: AWS, Google, Microsoft, ClaudeThe best AI certifications in 2026 from AWS, Google, Microsoft, Claude and Databricks, by level, with price, status and which one to take first.
Microsoft AI certification path 2026: which exam firstMicrosoft AI certification path for 2026 - AI-901, AB-730, AI-103, AI-200, AI-300, AB-100, AI-500 and GH-300 by role, and which exam to take first.
AI-103 Certification Guide: What Changed From AI-102The AI-103 certification replaced AI-102 in 2026. Exam format, cost, the five skill domains, what changed and how to prepare, with free practice questions.
What Is RAG? How Retrieval-Augmented Generation WorksWhat RAG is and how it works: chunking, embeddings, vector search, hybrid search, reranking, GraphRAG and agentic RAG on AWS, Google and Azure.
Prompt engineering vs RAG vs fine-tuningWhen to use prompt engineering, retrieval-augmented generation, fine-tuning or continued pre-training, compared on cost, freshness, hallucination and privacy.