For a social media messaging app, you need real-time language identification on short text messages. The Azure AI Translator service provides a dedicated Detect API that returns the language and a confidence score for each input text, designed for low-latency scenarios such as chat and in-app communication. This aligns directly with "identify in real time the language used in messages." Microsoft Azure +2 Why the other options are not the best fit:
Azure AI Speech (A) targets spoken audio (speech-to-text, translation, diarization). It's not intended for detecting the language of text messages. Azure AI Azure AI Content Safety (B) focuses on safety/moderation (harmful or unsafe content), not language identification.
Azure AI Language (D) also offers a Language Detection feature, but it's typically used within broader text analytics workflows. For real-time messaging and potential downstream translation, Translator's Detect endpoint is the targeted and streamlined choice.
References (Microsoft Learn)
Translator - Detect method (returns detected language + confidence). Microsoft Learn Translator - Product overview and real-time translation scenarios. Microsoft Azure Translator - Language support (Auto Language Detection). Microsoft Learn Azure AI Language - Language Detection (feature overview/how-to) for context.

Explanation:

First blank: speech_recognition_language
Second blank: add_target_language
speech_translation_config = speechsdk.translation.SpeechTranslationConfig( subscription=os.environ.get( ' SPEECH_KEY ' ), region=os.environ.get( ' SPEECH_REGION ' ) )
# Source (recognition) language: English (US)
speech_translation_config.speech_recognition_language = " en-US "
# Target translation language: Italian
speech_translation_config.add_target_language( " it " )
audio_config = speechsdk.audio.AudioConfig(use_default_microphone= True ) recognizer = speechsdk.translation.TranslationRecognizer( translation_config=speech_translation_config, audio_config=audio_config ) Comprehensive Detailed Explanation along with All References available from Microsoft Azure AI Solution at the end of the explanation To translate speech, configure a SpeechTranslationConfig with (1) the recognition language of the input audio and (2) one or more target languages for translation. In Python, you set the input language with the speech_recognition_language property (e.g., " en-US " ), and you add a target with add_target_language( " < locale > " ) (e.g., " it " for Italian). This exactly matches the SDK guidance and examples for speech translation. Microsoft Learn speech_recognition_language specifies the locale used by the recognizer to understand the spoken input.
add_target_language( " it " ) adds Italian as a translation output language.
Microsoft References
How to translate speech with the Speech SDK (Python examples show translation_config.
speech_recognition_language = from_language and translation_config.add_target_language(...) ). Microsoft Learn SpeechTranslationConfig (Python) reference - add_target_language(language: str) . Microsoft Lea rn Recognize speech (property usage for speech_recognition_language ). Microsoft Learn Language support for Speech service (locales and codes for recognition/translation)

Explanation:

Explanation:

To build a product support chatbot from a product support manual with minimal development effort and cost, use Custom question answering (CQA) in Azure AI Language. CQA lets you ingest files (including manuals
/FAQs), automatically extract Q & A, author answers where needed, and expose a ready-to-call endpoint that plugs straight into Azure Bot Service-no fine-tuning or complex retrieval stack required.
Not A (Phi-3 with fine-tuning): fine-tuning adds cost/complexity and isn't necessary for a manual-based Q & A bot.
Not C (GPT-4 with grounding via Azure AI Search): powerful but more engineering and cost (indexing, RAG orchestration) than needed.
Not D (Document Intelligence): focuses on OCR/structured data extraction, not conversational Q & A.
References (Microsoft Docs):
Custom question answering overview and scenario (create a conversational layer over your data; supports manuals/FAQs).
CQA supports unstructured documents and quick setup in Language Studio/Azure AI Foundry.