Text Analytics & Entity Linking — AI-103 Practice Question
A representative Microsoft Azure AI Apps and Agents Developer Associate (AI-103) exam question on Text Analytics & Entity Linking. Work through it below, then read why each option is right or wrong.
This question is part of the free Microsoft Azure AI Apps and Agents Developer Associate practice test.
Short answer
The correct answer is A. Azure Language Service with the combined Text Analytics API supporting key phrase extraction, sentiment analysis, and entity linking in one request.
Azure Language Service's Text Analytics API supports multiple capabilities in a single request, including key phrase extraction, sentiment analysis, and entity linking. This reduces latency and API call overhead compared to calling separate endpoints. Named Entity Recognition alone does not extract key phrases or sentiment. Azure Cognitive Search is a search and indexing service, not a primary text analysis engine. Azure OpenAI is less cost-effective for high-volume, structured extraction tasks and adds latency for deterministic NLP operations.
The Question
You are building a customer feedback analysis system for a retail company. The system receives unstructured reviews in multiple languages and must extract key phrases, sentiment scores, and linked entities (product names, locations) in a single API call. Which Azure Language service capability should you implement to meet all three requirements with minimal additional processing?
Why A is correct
Azure Language Service's Text Analytics API supports multiple capabilities in a single request, including key phrase extraction, sentiment analysis, and entity linking. This reduces latency and API call overhead compared to calling separate endpoints. Named Entity Recognition alone does not extract key phrases or sentiment. Azure Cognitive Search is a search and indexing service, not a primary text analysis engine. Azure OpenAI is less cost-effective for high-volume, structured extraction tasks and adds latency for deterministic NLP operations.
Why the other options are wrong
Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.
Option C does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.
Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.
Key idea: Text Analytics & Entity Linking
Azure Language Service's Text Analytics API supports multiple capabilities in a single request, including key phrase extraction, sentiment analysis, and entity linking. This reduces latency and API call overhead compared to calling separate endpoints. Named Entity Recognition alone does not extract key phrases or sentiment. Azure Cognitive Search is a search and indexing service, not a primary text analysis engine. Azure OpenAI is less cost-effective for high-volume, structured extraction tasks and adds latency for deterministic NLP operations. On the AI-103 exam, questions in the "Implement natural language processing solutions" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
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