This AI-102 practice question tests your understanding of plan and manage an azure ai solution. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are designing a solution that uses Azure AI Language to analyze customer feedback. The solution must detect sentiment, extract key phrases, and identify named entities. Which feature should you use?
Azure AI Language service
Azure AI Language provides sentiment, key phrases, and entity recognition.
Azure AI Speech service
Why wrong: Speech service handles speech-to-text, not text analytics.
Azure AI Computer Vision
Why wrong: Computer Vision analyzes images, not text.
Translator API
Why wrong: Translator API provides translation, not analysis.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure AI Language service
The Azure AI Language service provides pre-built capabilities for sentiment analysis, key phrase extraction, and named entity recognition (NER) as part of its text analytics features. These three tasks are directly supported by the service's Analyze API, making it the correct choice for analyzing customer feedback text.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure AI Language service
Why this is correct
Azure AI Language provides sentiment, key phrases, and entity recognition.
Related concept
Read the scenario before looking for a memorised answer.
Azure AI Speech service
Why it's wrong here
Speech service handles speech-to-text, not text analytics.
Azure AI Computer Vision
Why it's wrong here
Computer Vision analyzes images, not text.
Translator API
Why it's wrong here
Translator API provides translation, not analysis.
Common exam traps
The trap here is that candidates may confuse Azure AI Language with other Azure AI services that have overlapping names (e.g., Translator API for language tasks) or assume Speech or Vision services can perform text analysis, but only the Language service provides the specific trio of sentiment, key phrases, and NER.
Detailed technical explanation
Under the hood, the Azure AI Language service uses deep learning models (e.g., transformers) for each task: sentiment analysis outputs a sentiment label (positive, negative, neutral, mixed) with confidence scores per sentence, key phrase extraction identifies important terms using a statistical model, and NER tags entities like Person, Organization, Location, and DateTime using a pre-trained model. A subtle behavior is that the service can handle multiple languages and returns results in a structured JSON response, allowing batch processing of up to 1,000 documents per request with a maximum size of 5,120 characters per document.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
The correct answer is: Azure AI Language service — The Azure AI Language service provides pre-built capabilities for sentiment analysis, key phrase extraction, and named entity recognition (NER) as part of its text analytics features. These three tasks are directly supported by the service's Analyze API, making it the correct choice for analyzing customer feedback text.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This AI-102 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-102 exam.
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