AI-102 Plan and manage an Azure AI solution Practice Question
You are planning a solution that uses Azure AI Language to analyze customer feedback from social media posts. The solution must: - Detect sentiment (positive, negative, neutral) for each post. - Extract key phrases. - Support English and Spanish languages. - Run asynchronously for a batch of 10,000 posts. - Use the least expensive option that meets requirements. What should you do?
⚠ Common exam trap
It's easy for candidates to assume custom models are required for multilingual support or that translation is necessary, when in fact Azure AI Language's built-in capabilities already cover English and Spanish natively.
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
✓
Use the Azure AI Language service with the built-in sentiment analysis and key phrase extraction capabilities. Process posts in batches using the async API.
Azure AI Language's built-in sentiment analysis and key phrase extraction natively support both English and Spanish, and the async batch API is designed for high-volume processing (e.g., 10,000 posts) at a lower cost than per-document calls. This approach meets all requirements without custom models or translation overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the Azure AI Language service with the built-in sentiment analysis and key phrase extraction capabilities. Process posts in batches using the async API.
Why this is correct
The built-in sentiment analysis and key phrase extraction capabilities cover both required tasks and support English and Spanish. The async API handles the 10,000-post batch, and using built-in features avoids the higher cost of custom models.
- ✗
Build a custom text classification model in Azure AI Language to detect sentiment and extract key phrases.
Why it's wrong here
Custom text classification assigns your own labels to documents; it neither performs sentiment scoring nor key phrase extraction, and training it adds cost. It is tempting because custom models handle domain-specific categories, but it would be the correct choice only when you need bespoke labels beyond the prebuilt sentiment and key phrase features.
- ✗
Use the Azure AI Language service with the single-document API for each post.
Why it's wrong here
The single-document API processes one post per request, so 10,000 posts means 10,000 calls, which is slower and costlier than the asynchronous batch API the stem requires. It is tempting because per-post calls are simple to implement, but it would be the correct choice only for small volumes or real-time single-item analysis.
- ✗
Use Azure AI Translator to translate all posts to English, then use Azure AI Language for analysis.
Why it's wrong here
Translation adds a separate Azure AI Translator resource and cost, and sentiment and key phrase extraction can run directly on Spanish text, so translating first wastes spend. It is tempting because normalising to one language simplifies downstream processing, but it would be correct only if the service lacked native Spanish support.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.