Describe features of Natural Language Processing workloads on Azure →hardMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A global customer support team receives feedback messages in multiple languages. They want to build an automated pipeline that first identifies the language of each message, then translates it to English, and finally analyzes the sentiment of the translated text. Which combination of Azure AI services should they use?
⚠ Common exam trap
It's easy for candidates to confuse Azure AI Language (Text Analytics) with Azure AI Speech or Azure AI Vision, mistakenly thinking speech or vision services can handle text-based language detection and sentiment analysis, when in fact only Text Analytics provides those NLP capabilities.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI Translator and Azure AI Language (Text Analytics)
The pipeline requires language detection, translation, and sentiment analysis. Azure AI Translator provides language detection and translation, while Azure AI Language (Text Analytics) provides sentiment analysis on the translated English text. This combination directly fulfills all three requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure AI Translator and Azure Anomaly Detector
Why it's wrong here
Azure Anomaly Detector is a specialized service for identifying statistical outliers and unusual patterns in time-series numeric data, such as server metrics, IoT sensor streams, or financial trends. It cannot interpret natural-language text, so even when paired with Translator, it would receive translated strings but have no way to extract or classify sentiment from them. This combination therefore omits the required sentiment-analysis capability and misapplies an anomaly-detection tool to a language-understanding workload.
When this WOULD be correct
A question asking for a solution to detect anomalies in customer support ticket volumes over time, using time-series data from multiple regions, where Azure Anomaly Detector is needed alongside Azure AI Translator to preprocess multilingual text data.
- ✓
Azure AI Translator and Azure AI Language (Text Analytics)
Why this is correct
Azure AI Translator automatically detects the source language of the incoming text and then translates the customer feedback into a target language, while Azure AI Language's Text Analytics capability analyzes that translated text and returns sentiment scores such as positive, negative, neutral, or mixed. This pairing directly addresses both the multilingual translation requirement and the need to quantify customer sentiment from written feedback, and it works entirely on the text input without requiring audio or image processing.
- ✗
Azure AI Speech and Azure AI Language (Text Analytics)
Why it's wrong here
Azure AI Speech is designed for converting audio to text or text to speech, but the customer feedback is already available as natural-language written text, so introducing a speech service creates an irrelevant audio dependency. While Azure AI Language's Text Analytics can perform sentiment analysis, this particular pairing does not include Azure AI Translator, meaning non-English written feedback would either be scored in its original language or would require an additional, unlisted translation service. Thus the combination is misaligned with the input modality and incomplete for the global, multilingual requirement.
When this WOULD be correct
If the question involved processing audio messages (e.g., customer voicemails) that need to be transcribed to text before language identification and translation, then Azure AI Speech (for transcription) combined with Azure AI Language (for sentiment) would be correct.
- ✗
Azure AI Vision and Azure AI Language (Text Analytics)
Why it's wrong here
Azure AI Vision is focused on analyzing images and video, and although its OCR feature can extract text from pictures, the customer feedback in this scenario is already provided as written text, so calling a vision service adds an unnecessary image-processing step. Azure AI Language's Text Analytics could perform sentiment analysis on the original or extracted text, but without Azure AI Translator that extracted or already-written text cannot be translated, leaving the multilingual support pipeline without the necessary language conversion.
When this WOULD be correct
A question asks: 'A company wants to extract text from scanned customer feedback forms and then analyze the sentiment of the extracted text.' In that case, Azure AI Vision (OCR) followed by Azure AI Language (Text Analytics) would be correct.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Azure AI Translator and Azure AI Language (Text Analytics)Correct answer▾
Why this is correct
Azure AI Translator automatically detects the source language of the incoming text and then translates the customer feedback into a target language, while Azure AI Language's Text Analytics capability analyzes that translated text and returns sentiment scores such as positive, negative, neutral, or mixed. This pairing directly addresses both the multilingual translation requirement and the need to quantify customer sentiment from written feedback, and it works entirely on the text input without requiring audio or image processing.
✗Azure AI Translator and Azure Anomaly DetectorWrong answer — click to see why▾
Why this is wrong here
Anomaly Detector identifies unusual patterns in time-series data, not language identification or sentiment analysis. The pipeline requires language detection and sentiment analysis, which Anomaly Detector does not provide.
★ When this WOULD be the correct answer
A question asking for a solution to detect anomalies in customer support ticket volumes over time, using time-series data from multiple regions, where Azure Anomaly Detector is needed alongside Azure AI Translator to preprocess multilingual text data.
Why candidates choose this
Candidates may confuse 'Anomaly Detector' with a service that detects unusual content in text, or they might think it can handle language-related tasks due to the word 'detector'.
✗Azure AI Speech and Azure AI Language (Text Analytics)Wrong answer — click to see why▾
Why this is wrong here
Azure AI Speech is for speech-to-text and text-to-speech, not for language identification or translation. The pipeline requires language detection and translation, not speech processing.
★ When this WOULD be the correct answer
If the question involved processing audio messages (e.g., customer voicemails) that need to be transcribed to text before language identification and translation, then Azure AI Speech (for transcription) combined with Azure AI Language (for sentiment) would be correct.
Why candidates choose this
Candidates may confuse 'language' broadly with 'speech', assuming speech services handle all language tasks, or overlook that the input is already text (feedback messages), not audio.
✗Azure AI Vision and Azure AI Language (Text Analytics)Wrong answer — click to see why▾
Why this is wrong here
Azure AI Vision is for image analysis, not for language identification, translation, or sentiment analysis. The question requires processing text messages, not images.
★ When this WOULD be the correct answer
A question asks: 'A company wants to extract text from scanned customer feedback forms and then analyze the sentiment of the extracted text.' In that case, Azure AI Vision (OCR) followed by Azure AI Language (Text Analytics) would be correct.
Why candidates choose this
Candidates may confuse Azure AI Vision's OCR capability as a general text processing tool, or they might think 'vision' includes language understanding, leading them to select this option without reading the question carefully.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Azure AI Translator
Azure AI Translator is a cloud-based service that uses neural machine translation to convert text between languages while preserving meaning and context.
Key term
Azure AI Services
Azure AI Services is a collection of pre-built, cloud-based artificial intelligence APIs and services that allow developers and IT professionals to integrate capabilities like vision, speech, language, and decision-making into applications without needing deep machine learning expertise.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.