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 e-commerce company receives customer support emails in over 30 languages. They want to automatically route each email to the correct department (Billing, Technical Support, or Returns) and also extract the order number and the product name mentioned in the email. The solution must be able to identify the language of each email before further processing. Which combination of Azure AI Language features should they use?
⚠ Common exam trap
Many exam-takers confuse Key Phrase Extraction with Entity Recognition, or assume Sentiment Analysis is needed for routing, when in fact the scenario's explicit requirements (language identification, department routing, and specific entity extraction) map directly to Language Detection, Custom Text Classification, and Entity Recognition.
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
✓
Language Detection, Custom Text Classification, and Entity Recognition
The scenario requires three distinct capabilities: Language Detection to identify the email's language (a prerequisite for further processing), Custom Text Classification to route emails to the correct department (Billing, Technical Support, or Returns), and Entity Recognition to extract the order number and product name. Azure AI Language provides these as separate, combinable features that directly map to the stated 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.
- ✓
Language Detection, Custom Text Classification, and Entity Recognition
Why this is correct
Language Detection first identifies the email's language so downstream processing can use the correct language model. Custom Text Classification, trained on labeled support tickets, assigns the email to the right department (e.g., refunds, technical support). Entity Recognition then extracts structured data points such as order numbers and product names using prebuilt or custom entity types, enabling automated follow-up. Together these three capabilities cover the full triage workflow.
- ✗
Language Detection, Key Phrase Extraction, and Sentiment Analysis
Why it's wrong here
This combination lacks Custom Text Classification, which is required for routing emails to the correct department, and Entity Recognition, which is needed to pull structured fields like order numbers. Key Phrase Extraction returns general important phrases without semantic labels, while Sentiment Analysis only outputs a polarity score (positive/negative/neutral). Neither provides the department-level categorization or specific entity extraction required by the support workflow.
- ✗
Entity Recognition, Sentiment Analysis, and Key Phrase Extraction
Why it's wrong here
This set omits Language Detection, which is essential because an email's language must be known before applying language-dependent NLP models, and Custom Text Classification, which is the mechanism that routes to departments. Without Language Detection, the pipeline may process non-English text with an English model; without classification, no routing decision is made. Sentiment Analysis and Key Phrase Extraction add generic insight but do not satisfy the core requirements.
- ✗
Custom Text Classification, Key Phrase Extraction, and Sentiment Analysis
Why it's wrong here
Although Custom Text Classification correctly handles department routing, this set omits Language Detection for multilingual support emails and replaces Entity Recognition with Key Phrase Extraction. Key Phrase Extraction identifies salient terms but does not label them as order numbers or product names, so structured fields cannot be reliably captured. Sentiment Analysis is unrelated to routing or extraction, making this combination incomplete.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Language detection
Language detection is an Azure AI service that automatically identifies the language of a piece of text without manual input.
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
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