Implement text analysis solutions

A legal-tech company processes thousands of lengthy contract documents overnight. They need each document condensed into a concise, fluent narrative overview that rephrases key clauses in natural language for busy executives, rather than pulling verbatim sentences. They want to use Azure AI Language's summarization capability. Which summarization approach should they configure?

You are building a voice-enabled agent for a hospital's radiology department. During testing, the standard Azure AI Speech-to-text model frequently mis-transcribes specialized medical terms (e.g., 'oligodendroglioma', 'cholecystectomy') that clinicians dictate, even though general conversational speech transcribes accurately. You have a corpus of correctly spelled domain terms and representative sentences but only a small amount of matching audio. Which approach best improves recognition accuracy for these terms?

A support platform team is building an agent that reviews written customer chat transcripts. They need to detect whether messages express negative sentiment AND identify specific opinion targets (e.g., 'the checkout was slow' should link the negative opinion to 'checkout'). They want a managed Azure AI Language capability rather than crafting their own generative prompt, to keep results consistent and low-cost at high volume. Which capability should they use?

A product team collected 40,000 free-text customer feedback comments with no predefined categories. They want to discover the recurring themes across the corpus—without maintaining a fixed label list—so they can prioritize product improvements. Which Azure AI Language capability best fits this requirement?

A legal-tech company wants a solution that reads long contract documents and produces a consistent one-paragraph plain-language summary followed by a bulleted list of key obligations. The summaries must always follow the same structure and use the firm's simplified vocabulary, and the team wants to iterate quickly without training a custom model. Which approach best meets these requirements?

You are building a voice-enabled meeting assistant agent. It ingests recorded audio files from conference calls with three to five participants and must produce a transcript that labels which participant said each utterance so downstream summarization can attribute action items to individuals. Which Azure AI Speech-to-text capability should you enable to meet this requirement?

You are building a customer support agent that answers phone calls. Callers speak in various languages (Spanish, French, German), and human agents respond in English. The system must transcribe caller speech, translate it to English in near real time, and also translate the English agent replies back into the caller's spoken language as audio. You want the lowest-latency path that handles speech-to-text, translation, and text-to-speech as an integrated pipeline optimized for spoken conversation. Which approach best meets these requirements?

You are building a text analysis pipeline that ingests free-form customer support emails and must produce a strictly typed JSON object for each email containing fields for issue_category (from a fixed list), urgency (integer 1-5), and a one-sentence summary. Downstream systems reject any output that is not valid JSON matching the expected schema. Using an Azure OpenAI chat model in Azure AI Foundry, which approach best guarantees the model returns parseable, schema-conformant output?

A support operations team wants to generate a concise, natural-language summary from multi-turn customer support chat logs. The summary must specifically capture the customer's issue and the resolution provided, phrased in fluent prose rather than a list of extracted sentences. Which Azure AI Language capability should you use?

A media monitoring team processes thousands of news articles daily. For each article they must not only detect mentions of organizations and people, but also disambiguate each mention to a canonical knowledge-base identity so that references like 'Apple' (the company) and 'apple' (the fruit) resolve to distinct, verifiable entries with links to a reference source. Which Azure AI Language capability directly satisfies this requirement?

A product team is building a text-analysis feature that ingests thousands of unstructured customer support emails. For each email, they need to output a short set of the most salient noun-based concepts (e.g., 'battery drain', 'shipping delay', 'refund process') so analysts can cluster recurring themes—without requiring the concepts to be linked to a predefined category or knowledge base. Which Azure AI Language capability best fits this requirement?

A hospital is building a solution to process clinical notes written by physicians. The team must extract medical conditions, medications, and dosages, and link each extracted term to standardized medical ontology codes (such as UMLS) so downstream analytics systems can normalize terminology across documents. Which Azure AI Language capability should the team use?

A retail company receives thousands of product reviews daily in multiple languages. The product team wants to understand not just whether each review is positive or negative overall, but also the sentiment expressed toward specific product attributes mentioned in the text (for example, 'battery life' or 'screen quality'). They want a solution that requires minimal custom prompt engineering and returns structured aspect-level results. Which approach should you use?

A publishing company needs to convert 800 full-length novels (each 60,000+ words) into narrated audio files using Azure AI Speech. The process runs overnight as a scheduled job, and the resulting MP3 files are stored in Azure Blob Storage for later distribution. Latency per request is irrelevant, but the solution must handle very large text inputs efficiently without maintaining a persistent client connection. Which text-to-speech approach should you use?

You are building a voice-enabled customer support agent that must speak responses aloud in a distinctive, consistent brand persona that does not match any of the prebuilt Azure voices. Legal has approved using recordings from a professional voice talent who signed a consent form. Which Azure AI Speech capability should you use to generate the agent's spoken output?

You are building a voice-enabled agent for a pharmacy that reads medication instructions aloud using Azure AI Speech text-to-speech. Testers report two problems: drug names like 'metoprolol' are mispronounced, and dosage phrases such as 'take 1 tablet twice daily' are spoken too quickly for elderly users to follow. You must fix both issues without training a custom neural voice. What should you do?

A pharmaceutical company uses Azure AI Translator to localize its drug-safety documentation from English to German and Japanese. Reviewers report that specific product names, chemical compound abbreviations, and internal regulatory terms are being translated inconsistently or incorrectly, even though the general sentence quality is acceptable. The team must ensure these specific terms are always rendered with the company's approved translations without retraining a full custom model. What should they configure?

You are building an Azure AI service that ingests customer support emails from a global audience. Each email may contain text in an unknown language, and some emails mix two languages in the same message. Before summarizing each email with a language model, you need to reliably determine the dominant language of every message so you can route it to the correct regional team. You want the most accurate, purpose-built approach with minimal custom logic. What should you use?

A legal firm needs to translate thousands of contract documents (PDF and DOCX) from English into French and German. The translated files must retain the original layout, formatting, tables, and embedded images. The team wants a fully managed batch process that reads and writes files directly from Azure Blob Storage containers. Which approach should you implement?

A media company uses Azure Translator to localize user-submitted movie reviews from English into German, French, and Japanese for a family-friendly public portal. Some reviews contain profanity that must not appear in the published translations, but the company still wants readers to know that offensive language was present in the original. Which Translator configuration meets this requirement?