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AI-102 Practice Question: Implement natural language processing solutions

You are building a solution to analyze customer feedback from multiple sources: emails, chat logs, and survey responses. You need to detect the overall sentiment trend over time and identify the most frequently mentioned topics. The solution must also allow the business analyst to ask natural language questions about the data (e.g., 'Show me complaints about shipping in the last month'). You have all data in Azure Blob Storage. You need to implement a solution with minimal custom code. Which combination of Azure services should you use?

⚠ Common exam trap

A common mix-up: candidates confuse Azure AI Language's custom question answering (which is for Q&A over static content) with the broader NLP and search capabilities needed for dynamic trend analysis and natural language queries over unstructured data, leading them to pick Option D.

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 Azure AI Language to extract sentiment and key phrases, then index the data in Azure Cognitive Search with semantic search enabled; use the search's built-in features for trend analysis and natural language queries.

Azure AI Language provides built-in sentiment analysis and key phrase extraction, and Azure Cognitive Search with semantic search enables indexing the extracted data for trend analysis and natural language queries without custom code. This combination directly meets the requirements of detecting sentiment trends, identifying topics, and allowing natural language questions, all with minimal custom development.

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 Azure OpenAI Service to analyze sentiment and generate summaries, and store results in a Cosmos DB for querying.

    Why it's wrong here

    This requires custom coding to process data and build the query interface; also, Azure OpenAI is not optimized for structured analytics.

  • Use Azure AI Language to extract sentiment and key phrases, then index the data in Azure Cognitive Search with semantic search enabled; use the search's built-in features for trend analysis and natural language queries.

    Why this is correct

    Azure Cognitive Search can index the feedback with extracted metadata, and its semantic search can interpret natural language queries like 'complaints about shipping' and return relevant documents.

  • Use Azure AI Language to perform sentiment analysis and key phrase extraction, then load the results into Power BI for trend analysis and natural language Q&A.

    Why it's wrong here

    Power BI's Q&A is limited to data in its model; it cannot directly query raw text. The analyst wants to ask questions about the original feedback, not just the extracted metrics.

  • Use Azure AI Language's custom question answering to create a knowledge base from the feedback and allow natural language queries.

    Why it's wrong here

    Question answering is designed for static FAQ knowledge bases, not for dynamic querying over a large corpus of feedback with sentiment and trend analysis.

Quick reference

Azure Blob Storage Tier Comparison

TierStorage CostRetrieval CostLatencyUse Case
HotHighestLowestImmediateActive data, frequent reads
CoolLowerHigherImmediateData accessed < once / month
ColdLower stillHigherImmediateData accessed < once / quarter
ArchiveLowestHighest + rehydration delayHoursLong-term compliance retention

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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.